26265933 2015 08 12 2015 08 13 2015 08 14

1748-7188 10 2015 Algorithms Mol Biol Erratum to: Inferring interaction type in gene regulatory networks using co-expression data. 25 10.1186/s13015-015-0055-3 [This corrects the article DOI: 10.1186/s13015-015-0054-4.]. Khosravi Pegah P School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran ; The Donnelly Centre, University of Toronto, Toronto, Canada. Gazestani Vahid H VH Institute of Parasitology, McGill University, Montreal, QC Canada. Pirhaji Leila L Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. Law Brian B The Donnelly Centre, University of Toronto, Toronto, Canada ; Department of Computer Science, University of Toronto, Toronto, Canada ; Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. Sadeghi Mehdi M School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran ; National Institute of Genetic Engineering and Biotechnology, Tehran, Iran. Bader Gary D GD The Donnelly Centre, University of Toronto, Toronto, Canada ; Department of Computer Science, University of Toronto, Toronto, Canada. Goliaei Bahram B Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran. eng Published Erratum 2015 08 11

England Algorithms Mol Biol 101265088 1748-7188 Algorithms Mol Biol. 2015;10:23 26157474 PMC4532253 2015 2015 7 20 2015 7 20 2015 8 11 2015 8 13 6 0 2015 8 13 6 0 2015 8 13 6 1 epublish 10.1186/s13015-015-0055-3 55 26265933 PMC4532253 26258683 2015 08 28

1546-1726 18 9 2015 Sep Nat. Neurosci. EAG2 potassium channel with evolutionarily conserved function as a brain tumor target. 1236-46 10.1038/nn.4088 Over 20% of the drugs for treating human diseases target ion channels, but no cancer drug approved by the US Food and Drug Administration (FDA) is intended to target an ion channel. We found that the EAG2 (Ether-a-go-go 2) potassium channel has an evolutionarily conserved function for promoting brain tumor growth and metastasis, delineate downstream pathways, and uncover a mechanism for different potassium channels to functionally cooperate and regulate mitotic cell volume and tumor progression. EAG2 potassium channel was enriched at the trailing edge of migrating medulloblastoma (MB) cells to regulate local cell volume dynamics, thereby facilitating cell motility. We identified the FDA-approved antipsychotic drug thioridazine as an EAG2 channel blocker that reduces xenografted MB growth and metastasis, and present a case report of repurposing thioridazine for treating a human patient. Our findings illustrate the potential of targeting ion channels in cancer treatment. Huang Xi X Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA. Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA. He Ye Y Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA. Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA. Dubuc Adrian M AM Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada. Hashizume Rintaro R Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA. Zhang Wei W Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA. Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA. Reimand Jüri J The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. Yang Huanghe H Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA. Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA. Wang Tongfei A TA Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA. Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA. Stehbens Samantha J SJ Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, California, USA. Younger Susan S Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA. Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA. Barshow Suzanne S Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA. Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA. Zhu Sijun S Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA. Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA. Cooper Michael K MK Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA. Peacock John J Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada. Ramaswamy Vijay V http://orcid.org/0000-0002-6557-895X Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada. Garzia Livia L Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada. Wu Xiaochong X Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada. Remke Marc M http://orcid.org/0000-0002-9404-9993 Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada. Forester Craig M CM Department of Pediatrics, University of California, San Francisco, San Francisco, California, USA. Kim Charles C CC Division of Experimental Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California, USA. Weiss William A WA Department of Pediatrics, University of California, San Francisco, San Francisco, California, USA. Department of Neurological Surgery and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California USA. Department of Neurology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA. James C David CD Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA. Shuman Marc A MA Department of Medicine and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA. Bader Gary D GD http://orcid.org/0000-0003-0185-8861 The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. Mueller Sabine S http://orcid.org/0000-0002-8216-9357 Department of Neurology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA. Taylor Michael D MD Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada. Jan Yuh Nung YN Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA. Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA. Jan Lily Yeh LY Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA. Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA. eng Journal Article 2015 08 10

United States Nat Neurosci 9809671 1097-6256 IM 2015 6 25 2015 7 15 2015 8 10 2015 8 11 6 0 2015 8 11 6 0 2015 8 11 6 0 ppublish nn.4088 10.1038/nn.4088 26258683 26165520 2015 07 13 2015 07 19

2045-2322 5 2015 Sci Rep GreedyPlus: An Algorithm for the Alignment of Interface Interaction Networks. 12074 10.1038/srep12074 The increasing ease and accuracy of protein-protein interaction detection has resulted in the ability to map the interactomes of multiple species. We now have an opportunity to compare species to better understand how interactomes evolve. As DNA and protein sequence alignment algorithms were required for comparative genomics, network alignment algorithms are required for comparative interactomics. A number of network alignment methods have been developed for protein-protein interaction networks, where proteins are represented as vertices linked by edges if they interact. Recently, protein interactions have been mapped at the level of amino acid positions, which can be represented as an interface-interaction network (IIN), where vertices represent binding sites, such as protein domains and short sequence motifs. However, current algorithms are not designed to align these networks and generally fail to do so in practice. We present a greedy algorithm, GreedyPlus, for IIN alignment, combining data from diverse sources, including network, protein and binding site properties, to identify putative orthologous relationships between interfaces in available worm and yeast data. GreedyPlus is fast and simple, allowing for easy customization of behaviour, yet still capable of generating biologically meaningful network alignments. Law Brian B 1] Department of Computer Science, University of Toronto, Toronto, ON, Canada [2] The Donnelly Centre, University of Toronto, Toronto, ON, Canada. Bader Gary D GD 1] Department of Computer Science, University of Toronto, Toronto, ON, Canada [2] The Donnelly Centre, University of Toronto, Toronto, ON, Canada [3] Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada. eng Journal Article 2015 07 13

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1748-7188 10 2015 Algorithms Mol Biol Inferring interaction type in gene regulatory networks using co-expression data. 23 10.1186/s13015-015-0054-4 Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction. This paper describes a novel algorithm, "Signing of Regulatory Networks" (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN. SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type. Khosravi Pegah P School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran ; The Donnelly Centre, University of Toronto, Toronto, Canada. Gazestani Vahid H VH Institute of Parasitology, McGill University, Montreal, QC Canada. Pirhaji Leila L Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. Law Brian B The Donnelly Centre, University of Toronto, Toronto, Canada ; Department of Computer Science, University of Toronto, Toronto, Canada. Sadeghi Mehdi M National Institute of Genetic Engineering and Biotechnology, Tehran, Iran ; School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. Goliaei Bahram B Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran. Bader Gary D GD The Donnelly Centre, University of Toronto, Toronto, Canada. eng Journal Article 2015 07 08

England Algorithms Mol Biol 101265088 1748-7188 Algorithms Mol Biol. 2015;10:25 26265933 PMC4495944 Gene expression data Information-based approach Interaction type Regulatory interaction 2015 2014 4 11 2015 6 16 2015 7 8 2015 7 10 6 0 2015 7 15 6 0 2015 7 15 6 1 epublish 10.1186/s13015-015-0054-4 54 26157474 PMC4495944 26125594 2015 07 01 2015 07 18

1548-7105 12 7 2015 Jun 30 Nat. Methods Pathway and network analysis of cancer genomes. 615-21 10.1038/nmeth.3440 Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations. Mutation Consequences and Pathway Analysis working group of the International Cancer Genome Consortium eng R01 HG007069 HG NHGRI NIH HHS United States Journal Article

United States Nat Methods 101215604 1548-7091 IM Creixell Pau P Cellular Signal Integration Group (C-SIG), Technical University of Denmark, Lyngby, Denmark. Reimand Jüri J The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. Haider Syed S Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. Wu Guanming G 1] Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. [2] Department of Medical Informatics and Clinical Epidemiology, Oregon Health &Science University, Portland, Oregon, USA. Shibata Tatsuhiro T Division of Cancer Genomics, National Cancer Center, Tokyo, Japan. Vazquez Miguel M Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid, Spain. Mustonen Ville V Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. Gonzalez-Perez Abel A Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain. Pearson John J Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, University of Queensland, St. Lucia, Brisbane, Queensland, Australia. Sander Chris C Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. Raphael Benjamin J BJ Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA. Marks Debora S DS Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA. Ouellette B F Francis BF 1] Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. [2] Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada. Valencia Alfonso A Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid, Spain. Bader Gary D GD The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. Boutros Paul C PC 1] Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. [2] Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. [3] Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada. Stuart Joshua M JM 1] Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, California, USA. [2] Center for Biomolecular Science and Engineering, University of California, Santa Cruz, Santa Cruz, California, USA. Linding Rune R 1] Cellular Signal Integration Group (C-SIG), Technical University of Denmark, Lyngby, Denmark. [2] Biotech Research &Innovation Centre (BRIC), University of Copenhagen (UCPH), Copenhagen, Denmark. Lopez-Bigas Nuria N 1] Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain. [2] Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain. Stein Lincoln D LD 1] Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. [2] Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. 2015 1 20 2015 4 27 2015 7 1 6 0 2015 7 1 6 0 2015 7 1 6 0 ppublish nmeth.3440 10.1038/nmeth.3440 26125594 26098549 2015 06 23 2015 06 27

1932-6203 10 6 2015 PLoS ONE Cardioprotective Signature of Short-Term Caloric Restriction. e0130658 10.1371/journal.pone.0130658 To understand the molecular pathways underlying the cardiac preconditioning effect of short-term caloric restriction (CR). Lifelong CR has been suggested to reduce the incidence of cardiovascular disease through a variety of mechanisms. However, prolonged adherence to a CR life-style is difficult. Here we reveal the pathways that are modulated by short-term CR, which are associated with protection of the mouse heart from ischemia. Male 10-12 wk old C57bl/6 mice were randomly assigned to an ad libitum (AL) diet with free access to regular chow, or CR, receiving 30% less food for 7 days (d), prior to myocardial infarction (MI) via permanent coronary ligation. At d8, the left ventricles (LV) of AL and CR mice were collected for Western blot, mRNA and microRNA (miR) analyses to identify cardioprotective gene expression signatures. In separate groups, infarct size, cardiac hemodynamics and protein abundance of caspase 3 was measured at d2 post-MI. This short-term model of CR was associated with cardio-protection, as evidenced by decreased infarct size (18.5±2.4% vs. 26.6±1.7%, N=10/group; P=0.01). mRNA and miR profiles pre-MI (N=5/group) identified genes modulated by short-term CR to be associated with circadian clock, oxidative stress, immune function, apoptosis, metabolism, angiogenesis, cytoskeleton and extracellular matrix (ECM). Western blots pre-MI revealed CR-associated increases in phosphorylated Akt and GSK3ß, reduced levels of phosphorylated AMPK and mitochondrial related proteins PGC-1α, cytochrome C and cyclooxygenase (COX) IV, with no differences in the levels of phosphorylated eNOS or MAPK (ERK1/2; p38). CR regimen was also associated with reduced protein abundance of cleaved caspase 3 in the infarcted heart and improved cardiac function. Noyan Hossein H Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada. El-Mounayri Omar O Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada. Isserlin Ruth R Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. Arab Sara S Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada. Momen Abdul A Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada. Cheng Henry S HS Division of Advanced Diagnostics, Toronto General Research Institute, Toronto, Ontario Canada. Wu Jun J Division of Advanced Diagnostics, Toronto General Research Institute, Toronto, Ontario Canada. Afroze Talat T Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada. Li Ren-Ke RK Division of Advanced Diagnostics, Toronto General Research Institute, Toronto, Ontario Canada; Heart and Stroke Richard Lewar Centre of Excellence in Cardiovascular Research, University of Toronto, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada. Fish Jason E JE Division of Advanced Diagnostics, Toronto General Research Institute, Toronto, Ontario Canada; Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, Canada; Heart and Stroke Richard Lewar Centre of Excellence in Cardiovascular Research, University of Toronto, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada. Bader Gary D GD Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada; The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. Husain Mansoor M Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada; Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, Canada; Heart and Stroke Richard Lewar Centre of Excellence in Cardiovascular Research, University of Toronto, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada; Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Ted Rogers Centre for Heart Research, University Health Network, Toronto, Ontario, Canada. eng GEO GSE68646 Journal Article Research Support, Non-U.S. Gov't 2015 06 22

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1878-3686 27 6 2015 Jun 8 Cancer Cell Inhibition of the Mitochondrial Protease ClpP as a Therapeutic Strategy for Human Acute Myeloid Leukemia. 864-76 10.1016/j.ccell.2015.05.004 S1535-6108(15)00180-4 From an shRNA screen, we identified ClpP as a member of the mitochondrial proteome whose knockdown reduced the viability of K562 leukemic cells. Expression of this mitochondrial protease that has structural similarity to the cytoplasmic proteosome is increased in leukemic cells from approximately half of all patients with AML. Genetic or chemical inhibition of ClpP killed cells from both human AML cell lines and primary samples in which the cells showed elevated ClpP expression but did not affect their normal counterparts. Importantly, Clpp knockout mice were viable with normal hematopoiesis. Mechanistically, we found that ClpP interacts with mitochondrial respiratory chain proteins and metabolic enzymes, and knockdown of ClpP in leukemic cells inhibited oxidative phosphorylation and mitochondrial metabolism. Copyright © 2015 Elsevier Inc. All rights reserved. Cole Alicia A Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Wang Zezhou Z Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Coyaud Etienne E Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Voisin Veronique V Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada. Gronda Marcela M Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Jitkova Yulia Y Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Mattson Rachel R Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Hurren Rose R Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Babovic Sonja S Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada. Maclean Neil N Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Restall Ian I Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Wang Xiaoming X Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Jeyaraju Danny V DV Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Sukhai Mahadeo A MA Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Prabha Swayam S Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Bashir Shaheena S Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada. Ramakrishnan Ashwin A Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Leung Elisa E Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada. Qia Yi Hua YH Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Zhang Nianxian N Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Combes Kevin R KR Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Ketela Troy T Department of Molecular Genetics, Donnelly Centre for Cellular and Biomolecular Research, Toronto, ON M5S 3E1, Canada. Lin Fengshu F Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Houry Walid A WA Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada. Aman Ahmed A Drug Discovery Program, Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. Al-Awar Rima R Drug Discovery Program, Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON M5S 1A8, Canada. Zheng Wei W National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892, USA. Wienholds Erno E Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; Department of Medicine, University of Toronto, Toronto, ON M5G 2C4, Canada. Xu Chang Jiang CJ Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada. Dick John J Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; Department of Medicine, University of Toronto, Toronto, ON M5G 2C4, Canada. Wang Jean C Y JC Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; Department of Medicine, University of Toronto, Toronto, ON M5G 2C4, Canada. Moffat Jason J Department of Molecular Genetics, Donnelly Centre for Cellular and Biomolecular Research, Toronto, ON M5S 3E1, Canada. Minden Mark D MD Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; Department of Medicine, University of Toronto, Toronto, ON M5G 2C4, Canada. Eaves Connie J CJ Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada. Bader Gary D GD Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada. Hao Zhenyue Z Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Kornblau Steven M SM Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Raught Brian B Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada. Schimmer Aaron D AD Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; Department of Medicine, University of Toronto, Toronto, ON M5G 2C4, Canada. Electronic address: aaron.schimmer@utoronto.ca. eng NCI 1R01CA157456 CA NCI NIH HHS United States R01 CA157456 CA NCI NIH HHS United States Canadian Institutes of Health Research Canada Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

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1548-7105 12 6 2015 Jun Nat. Methods MIMP: predicting the impact of mutations on kinase-substrate phosphorylation. 531-3 10.1038/nmeth.3396 Protein phosphorylation is important in cellular pathways and altered in disease. We developed MIMP (http://mimp.baderlab.org/), a machine learning method to predict the impact of missense single-nucleotide variants (SNVs) on kinase-substrate interactions. MIMP analyzes kinase sequence specificities and predicts whether SNVs disrupt existing phosphorylation sites or create new sites. This helps discover mutations that modify protein function by altering kinase networks and provides insight into disease biology and therapy development. Wagih Omar O 0000000221620431 The Donnelly Centre, University of Toronto, Toronto, Canada. Reimand Jüri J The Donnelly Centre, University of Toronto, Toronto, Canada. Bader Gary D GD 0000000301858861 The Donnelly Centre, University of Toronto, Toronto, Canada. eng MOP-84324 Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't 2015 05 04

United States Nat Methods 101215604 1548-7091 EC 2.7.- Phosphotransferases IM Amino Acid Sequence Artificial Intelligence Mutation, Missense Phosphorylation Phosphotransferases genetics metabolism Polymorphism, Single Nucleotide genetics Signal Transduction Software Substrate Specificity 2014 12 09 2015 3 30 2015 5 04 2015 5 5 6 0 2015 5 6 6 0 2015 8 12 6 0 ppublish nmeth.3396 10.1038/nmeth.3396 25938373 25915851 2015 08 26

1470-8728 469 1 2015 Jul 1 Biochem. J. Metabolomic profiling in liver of adiponectin-knockout mice uncovers lysophospholipid metabolism as an important target of adiponectin action. 71-82 10.1042/BJ20141455 Adiponectin mediates anti-diabetic effects via increasing hepatic insulin sensitivity and direct metabolic effects. In the present study, we conducted a comprehensive and unbiased metabolomic profiling of liver tissue from AdKO (adiponectin-knockout) mice, with and without adiponectin supplementation, fed on an HFD (high-fat diet) to derive insight into the mechanisms and consequences of insulin resistance. Hepatic lipid accumulation and insulin resistance induced by the HFD were reduced by adiponectin. The HFD significantly altered levels of 147 metabolites, and bioinformatic analysis indicated that one of the most striking changes was the profile of increased lysophospholipids. These changes were largely corrected by adiponectin, at least in part via direct regulation of PLA2 (phospholipase A2) as palmitate-induced PLA2 activation was attenuated by adiponectin in primary hepatocytes. Notable decreases in several glycerolipids after the HFD were reversed by adiponectin, which also corrected elevations in several diacyglycerol and ceramide species. Our data also indicate that stimulation of ω-oxidation of fatty acids by the HFD is enhanced by adiponectin. In conclusion, this metabolomic profiling approach in AdKO mice identified important targets of adiponectin action, including PLA2, to regulate lysophospholipid metabolism and ω-oxidation of fatty acids. © 2015 Authors; published by Portland Press Limited. Liu Ying Y Department of Biology, York University, Toronto, Ontario, Canada, M3J 1P3. Sen Sanjana S Department of Biology, York University, Toronto, Ontario, Canada, M3J 1P3. Wannaiampikul Sivaporn S Department of Biology, York University, Toronto, Ontario, Canada, M3J 1P3 Department of Tropical Nutrition and Food Science, Mahidol University, Bangkok 10400, Thailand. Palanivel Rengasamy R Department of Biology, York University, Toronto, Ontario, Canada, M3J 1P3. Hoo Ruby L C RL State Key Laboratory of Pharmaceutical Biotechnology, and Department of Medicine, University of Hong Kong, Pokfulam, Hong Kong. Isserlin Ruth R The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario, Canada, M5S 3E1. Bader Gary D GD The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario, Canada, M5S 3E1. Tungtrongchitr Rungsunn R Department of Tropical Nutrition and Food Science, Mahidol University, Bangkok 10400, Thailand. Deshaies Yves Y Department of Medicine and Québec Heart & Lung Institute Research Centre, Université Laval, Québec City, Québec, Canada, G1V 4G5. Xu Aimin A State Key Laboratory of Pharmaceutical Biotechnology, and Department of Medicine, University of Hong Kong, Pokfulam, Hong Kong. Sweeney Gary G Department of Biology, York University, Toronto, Ontario, Canada, M3J 1P3 gsweeney@yorku.ca. eng Journal Article 2015 04 27

England Biochem J 2984726R 0264-6021 IM adiponectin high-fat diet insulin lipid liver phospholipase 2014 12 1 2015 4 27 2015 4 27 2015 4 28 6 0 2015 4 29 6 0 2015 4 29 6 0 ppublish 25915851 BJ20141455 10.1042/BJ20141455 25904639 2015 06 24 2015 07 29

1468-6244 52 7 2015 Jul J. Med. Genet. Canadian Open Genetics Repository (COGR): a unified clinical genomics database as a community resource for standardising and sharing genetic interpretations. 438-45 10.1136/jmedgenet-2014-102933 The Canadian Open Genetics Repository is a collaborative effort for the collection, storage, sharing and robust analysis of variants reported by medical diagnostics laboratories across Canada. As clinical laboratories adopt modern genomics technologies, the need for this type of collaborative framework is increasingly important. A survey to assess existing protocols for variant classification and reporting was delivered to clinical genetics laboratories across Canada. Based on feedback from this survey, a variant assessment tool was made available to all laboratories. Each participating laboratory was provided with an instance of GeneInsight, a software featuring versioning and approval processes for variant assessments and interpretations and allowing for variant data to be shared between instances. Guidelines were established for sharing data among clinical laboratories and in the final outreach phase, data will be made readily available to patient advocacy groups for general use. The survey demonstrated the need for improved standardisation and data sharing across the country. A variant assessment template was made available to the community to aid with standardisation. Instances of the GeneInsight tool were provided to clinical diagnostic laboratories across Canada for the purpose of uploading, transferring, accessing and sharing variant data. As an ongoing endeavour and a permanent resource, the Canadian Open Genetics Repository aims to serve as a focal point for the collaboration of Canadian laboratories with other countries in the development of tools that take full advantage of laboratory data in diagnosing, managing and treating genetic diseases. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions. Lerner-Ellis Jordan J Laboratory Medicine and Pathobiology, University of Toronto & Mount Sinai Hospital, Toronto, Ontario, Canada Ontario Institute for Cancer Research, Toronto, Ontario, Canada. Wang Marina M http://orcid.org/0000-0002-6051-4308 Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada. White Shana S Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts, USA Massachusetts General Hospital, Boston, Massachusetts, USA. Lebo Matthew S MS Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts, USA Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. Canadian Open Genetics Repository Group eng Journal Article Research Support, Non-U.S. Gov't 2015 04 22

England J Med Genet 2985087R 0022-2593 IM Nature. 2003 Apr 24;422(6934):835-47 12695777 Pharmacogenomics. 2006 Oct;7(7):969-72 17054407 Nucleic Acids Res. 2012 Jan;40(Database issue):D940-6 22080554 Nature. 2010 Sep 2;467(7311):52-8 20811451 Nature. 2010 Oct 28;467(7319):1061-73 20981092 Nucleic Acids Res. 2014 Jan;42(Database issue):D980-5 24234437 Genome Biol. 2010;11(11):309 21122162 Hum Mutat. 2011 May;32(5):532-6 21432942 Genet Med. 2008 Apr;10(4):294-300 18414213 J Biomed Inform. 2012 Oct;45(5):950-7 22521718 Clin Genet. 2013 Nov;84(5):453-63 24033266 Nature. 2011 Feb 10;470(7333):204-13 21307933 PMC4501169 Clinical genetics Diagnostics tests Evidence Based Practice Genetic screening/counselling Genetics Agatep Ron R Ainsworth Peter P Akbari Mohammad R MR Aronson Melyssa M Bader Gary D GD Basran Raveen R Blavier Andre A Blumenthal Andrea A Buckley Kathleen K Campbell Jodi J Campeau Philippe M PM Care Melanie M Carson Nancy N Carter Ronald R Charames George G Chitayat David D Chong George G Chouinard Edmond E Chun Kathy K Craddock Kenneth J KJ Docking Rod R Eisen Andrea A Faghfoury Hanna H Farrell Sandra S Feilotter Harriet H Fernandez Bridget B Forster-Gibson Cynthia C Foulkes William W Hegele Robert R Holter Spring S Horsburgh Sheri S Hughes Lauren L Hume Stacey S Jewett Franny F Karsan Aly A Khalouei Sam S Knoll Joan J Kolomeitz Elena E Maire Georges G Marshall Christian C McCready Elizabeth E Moorhouse Michael J MJ Morel Chantal C Nelson Tanya T O'Connor Brian B Ouellette Francis F Parboosingh Jillian J Ray Peter P Rehm Heidi H Riddell Christie C Rosenblatt David S DS Ruchon Andrea A Sadikovic Bekim B Semotiuk Kara K Scherer Stephen W SW Shuman Cheryl C Silver Josh J Siminovitch Katherine K Solomon-Izsak Lesley L Speevak Marsha M Stavropoulos James J Stein Lincoln L Tannenbaum Rhonda R Terespolsky Deborah D Wintle Richard F RF Wong Beatrix B Wong Nora N Waye John S JS Woods Michael O MO Wyatt Philip P Young Sean S 2014 12 4 2015 3 15 2015 4 22 2015 4 24 6 0 2015 4 24 6 0 2015 4 24 6 0 ppublish 25904639 jmedgenet-2014-102933 10.1136/jmedgenet-2014-102933 PMC4501169 25901276 2015 04 22 2015 04 22 2015 04 24

2046-1402 4 2015 F1000Res Long read nanopore sequencing for detection of HLA and CYP2D6 variants and haplotypes. 17 10.12688/f1000research.6037.1 Haplotypes are often critical for the interpretation of genetic laboratory observations into medically actionable findings. Current massively parallel DNA sequencing technologies produce short sequence reads that are often unable to resolve haplotype information. Phasing short read data typically requires supplemental statistical phasing based on known haplotype structure in the population or parental genotypic data. Here we demonstrate that the MinION nanopore sequencer is capable of producing very long reads to resolve both variants and haplotypes of HLA-A, HLA-B and CYP2D6 genes important in determining patient drug response in sample NA12878 of CEPH/UTAH pedigree 1463, without the need for statistical phasing. Long read data from a single 24-hour nanopore sequencing run was used to reconstruct haplotypes, which were confirmed by HapMap data and statistically phased Complete Genomics and Sequenom genotypes. Our results demonstrate that nanopore sequencing is an emerging standalone technology with potential utility in a clinical environment to aid in medical decision-making. Ammar Ron R The Donnelly Centre, University of Toronto, Toronto, ON, M5S3E1, Canada. Paton Tara A TA The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G0A4, Canada. Torti Dax D The Donnelly Centre, University of Toronto, Toronto, ON, M5S3E1, Canada. Shlien Adam A Department of Laboratory Medicine and Pathobiology, University of Toronto; Program in Genetics and Genome Biology & Department of Paediatric Laboratory Medicine The Hospital for Sick Children, Toronto, ON, M5G1X8, Canada. Bader Gary D GD The Donnelly Centre, University of Toronto, Toronto, ON, M5S3E1, Canada ; Department of Computer Science, University of Toronto, Toronto, ON, M5S3G4, Canada ; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S1A8, Canada. eng Journal Article 2015 01 21

England F1000Res 101594320 2046-1402 PMC4392832 DNA nanopore sequencing, haplotype, pharmacogenomics 2015 2015 1 20 2015 1 21 2015 4 23 6 0 2015 4 23 6 0 2015 4 23 6 1 epublish 10.12688/f1000research.6037.1 25901276 PMC4392832 25882982 2015 05 06 2015 07 14 2015 05 06

1474-5488 16 5 2015 May Lancet Oncol. Molecular subgroups of atypical teratoid rhabdoid tumours in children: an integrated genomic and clinicopathological analysis. 569-82 10.1016/S1470-2045(15)70114-2 S1470-2045(15)70114-2 Rhabdoid brain tumours, also called atypical teratoid rhabdoid tumours, are lethal childhood cancers with characteristic genetic alterations of SMARCB1/hSNF5. Lack of biological understanding of the substantial clinical heterogeneity of these tumours restricts therapeutic advances. We integrated genomic and clinicopathological analyses of a cohort of patients with atypical teratoid rhabdoid tumours to find out the molecular basis for clinical heterogeneity in these tumours. We obtained 259 rhabdoid tumours from 37 international institutions and assessed transcriptional profiles in 43 primary tumours and copy number profiles in 38 primary tumours to discover molecular subgroups of atypical teratoid rhabdoid tumours. We used gene and pathway enrichment analyses to discover group-specific molecular markers and did immunohistochemical analyses on 125 primary tumours to evaluate clinicopathological significance of molecular subgroup and ASCL1-NOTCH signalling. Transcriptional analyses identified two atypical teratoid rhabdoid tumour subgroups with differential enrichment of genetic pathways, and distinct clinicopathological and survival features. Expression of ASCL1, a regulator of NOTCH signalling, correlated with supratentorial location (p=0·004) and superior 5-year overall survival (35%, 95% CI 13-57, and 20%, 6-34, for ASCL1-positive and ASCL1-negative tumours, respectively; p=0·033) in 70 patients who received multimodal treatment. ASCL1 expression also correlated with superior 5-year overall survival (34%, 7-61, and 9%, 0-21, for ASCL1-positive and ASCL1-negative tumours, respectively; p=0·001) in 39 patients who received only chemotherapy without radiation. Cox hazard ratios for overall survival in patients with differential ASCL1 enrichment treated with chemotherapy with or without radiation were 2·02 (95% CI 1·04-3·85; p=0·038) and 3·98 (1·71-9·26; p=0·001). Integrated analyses of molecular subgroupings with clinical prognostic factors showed three distinct clinical risk groups of tumours with different therapeutic outcomes. An integration of clinical risk factors and tumour molecular groups can be used to identify patients who are likely to have improved long-term radiation-free survival and might help therapeutic stratification of patients with atypical teratoid rhabdoid tumours. C17 Research Network, Genome Canada, b.r.a.i.n.child, Mitchell Duckman, Tal Doron and Suri Boon foundations. Copyright © 2015 Elsevier Ltd. All rights reserved. Torchia Jonathon J Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada. Picard Daniel D Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada. Lafay-Cousin Lucie L Alberta Children's Hospital, and Departments of Oncology and Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. Hawkins Cynthia E CE Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pathology, Hospital for Sick Children, Toronto, ON, Canada. Kim Seung-Ki SK Department of Neurosurgery, Seoul National University Children's Hospital, Seoul, South Korea. Letourneau Louis L Genome Quebec Innovation Centre, McGill University, Montreal, QC, Canada. Ra Young-Shin YS Department of Neurosurgery, Asan Medical Center, Songpa-gu, Seoul, South Korea. Ho King Ching KC Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada. Chan Tiffany Sin Yu TS Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada. Sin-Chan Patrick P Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada. Dunham Christopher P CP Division of Anatomic Pathology, Children's and Women's Health Centre of British Columbia, Vancouver, BC, Canada. Yip Stephen S Department of Neuropathology, Vancouver General Hospital, Vancouver, BC, Canada. Ng Ho-Keung HK Department of Anatomical and Cellular Pathology, Chinese University of Hong Kong, Hong Kong, China. Lu Jian-Qiang JQ Department of Laboratory Medicine and Pathology, University of Alberta Hospital, Edmonton, AB, Canada. Albrecht Steffen S Department of Pathology, Montreal Children's Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada. Pimentel José J Department of Neurology, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal. Chan Jennifer A JA Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, AB, Canada. Somers Gino R GR Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Paediatric Laboratory Medicine, Hospital for Sick Children, Toronto, ON, Canada. Zielenska Maria M Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Paediatric Laboratory Medicine, Hospital for Sick Children, Toronto, ON, Canada. Faria Claudia C CC Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal. Roque Lucia L Cytogenetic Laboratory, Centro de Investigação em Patobiologia Molecular, Portuguese Cancer Institute, Lisbon, Portugal. Baskin Berivan B Department of Immunology, Genetics and Pathology, Uppsala University Hospital, Uppsala, Sweden. Birks Diane D Department of Pediatrics, University of Colorado Denver, Aurora, CO, USA. Foreman Nick N Department of Pediatrics, University of Colorado Denver, Aurora, CO, USA. Strother Douglas D Alberta Children's Hospital, and Departments of Oncology and Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. Klekner Almos A Department of Neurosurgery, University of Debrecen, Debrecen, Hungary. Garami Miklos M Second Department of Pediatrics, Semmelweis University, Budapest, Hungary. Hauser Peter P Second Department of Pediatrics, Semmelweis University, Budapest, Hungary. Hortobágyi Tibor T Department of Histopathology, Faculty of Medicine, University of Szeged, Hungary. Bognár Laszló L Department of Neurosurgery, University of Debrecen, Debrecen, Hungary. Wilson Beverly B Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Alberta, Edmonton, AB, Canada. Hukin Juliette J Division of Neurology and Oncology, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada. Carret Anne-Sophie AS Division of Hematology-Oncology, Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montreal, QC, Canada. Van Meter Timothy E TE Pediatric Hematology-Oncology, Department of Pediatrics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA. Nakamura Hideo H Department of Neurosurgery, Kumamoto University, Kumamoto, Japan. Toledano Helen H Oncology Department, Schneider Hospital, Petach Tikva, Israel. Fried Iris I Pediatric Hematology Oncology Department, Hadassah Hebrew University Hospital, Jerusalem, Israel. Fults Daniel D Department of Neurosurgery, University of Utah, School of Medicine, Salt Lake City, UT, USA. Wataya Takafumi T Department of Neurosurgery, Shizuoka Children's Hospital, Aoi-ku, Shizuoka, Japan. Fryer Chris C Division of Hematology and Oncology, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada. Eisenstat David D DD Departments of Pediatrics and Medical Genetics, University of Alberta, Edmonton, AB, Canada. Scheineman Katrin K Department of Pediatrics, McMaster University, Hamilton, ON, Canada. Johnston Donna D Department of Pediatrics, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada. Michaud Jean J Department of Pathology and Laboratory Medicine, Ottawa Hospital and Children's Hospital of Eastern Ontario, Ottawa, ON, Canada. Zelcer Shayna S Division of Children's Health and Therapeutics, Children's Health Research Institute, London, ON, Canada. Hammond Robert R Department of Pathology, University of Western Ontario, London, ON, Canada. Ramsay David A DA Department of Pathology, London Health Sciences Centre, London, ON, Canada. Fleming Adam J AJ Division of Pediatric Hematology/Oncology, McMaster University, Hamilton, ON, Canada. Lulla Rishi R RR Division of Pediatrics-Hematology, Oncology and Stem Cell Transplantation, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA. Fangusaro Jason R JR Division of Pediatrics-Hematology, Oncology and Stem Cell Transplantation, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA. Sirachainan Nongnuch N Division of Hematology and Oncology, Department of Pediatrics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand. Larbcharoensub Noppadol N Department of Pathology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand. Hongeng Suradej S Division of Hematology and Oncology, Department of Pediatrics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand. Barakzai Muhammad Abrar MA Department of Pathology and Microbiology, Aga Khan University Hospital, Karachi, Pakistan. Montpetit Alexandre A Genome Quebec Innovation Centre, McGill University, Montreal, QC, Canada. Stephens Derek D Department of Clinical Research Services, Hospital for Sick Children, Toronto, ON, Canada. Grundy Richard G RG Children's Brain Tumour Research Centre, School of Clinical Sciences, Queen's Medical Centre, University of Nottingham, Nottingham, UK. Schüller Ulrich U Center for Neuropathology, Ludwig-Maximilians-University, Munich, Germany. Nicolaides Theodore T Department of Pediatrics Hematology/Oncology, University of California, San Francisco, CA, USA. Tihan Tarik T Department of Pathology and Laboratory Medicine, University of California, San Francisco, CA, USA. Phillips Joanna J Department of Neurological Surgery, University of California, San Francisco, CA, USA. Taylor Michael D MD Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Hospital for Sick Children, Toronto, ON, Canada. Rutka James T JT Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Hospital for Sick Children, Toronto, ON, Canada. Dirks Peter P Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Hospital for Sick Children, Toronto, ON, Canada. Bader Gary D GD Department of Computer Science, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, ON, Canada. Warmuth-Metz Monika M Department of Neuroradiology, University of Wuerzburg, Wuerzburg, Germany. Rutkowski Stefan S Department of Paediatric Haematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. Pietsch Torsten T Department of Neuropathology, University of Bonn Medical Center, Bonn, Germany. Judkins Alexander R AR Department of Pathology and Laboratory Medicine at Children's Hospital Los Angeles, Los Angeles, CA, USA. Jabado Nada N Department of Pediatrics, McGill University, Montreal, QC, Canada; Department of Human Genetics, McGill University, Montreal, QC, Canada. Bouffet Eric E Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada. Huang Annie A Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada. Electronic address: annie.huang@sickkids.ca. eng P50 CA097257 CA NCI NIH HHS United States Journal Article Research Support, Non-U.S. Gov't 2015 04 14

England Lancet Oncol 100957246 1470-2045 0 ASCL1 protein, human 0 Basic Helix-Loop-Helix Transcription Factors 0 Receptors, Notch Teratoid Tumor, Atypical IM Lancet Oncol. 2015 May;16(5):486-8 25882984 Basic Helix-Loop-Helix Transcription Factors biosynthesis genetics Child Child, Preschool Female Gene Expression Regulation, Neoplastic Genomics Humans Immunohistochemistry Infant Male Prognosis Receptors, Notch biosynthesis genetics Rhabdoid Tumor genetics pathology Risk Factors Signal Transduction genetics Teratoma genetics pathology 2015 4 14 2015 4 18 6 0 2015 4 18 6 0 2015 7 15 6 0 ppublish S1470-2045(15)70114-2 10.1016/S1470-2045(15)70114-2 25882982 25759811 2015 03 11 2015 03 11 2015 06 03

2296-4185 3 2015 Front Bioeng Biotechnol Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative. 19 10.3389/fbioe.2015.00019 The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models. Hucka Michael M Computing and Mathematical Sciences, California Institute of Technology , Pasadena, CA , USA. Nickerson David P DP Auckland Bioengineering Institute, University of Auckland , Auckland , New Zealand. Bader Gary D GD The Donnelly Centre, University of Toronto , Toronto, ON , Canada. Bergmann Frank T FT Computing and Mathematical Sciences, California Institute of Technology , Pasadena, CA , USA ; BioQuant/Centre for Organismal Studies (COS), University of Heidelberg , Heidelberg , Germany. Cooper Jonathan J Department of Computer Science, University of Oxford , Oxford , UK. Demir Emek E Computational Biology, Memorial Sloan-Kettering Cancer Center , New York, NY , USA. Garny Alan A Auckland Bioengineering Institute, University of Auckland , Auckland , New Zealand. Golebiewski Martin M Scientific Databases and Visualization, Heidelberg Institute for Theoretical Studies (HITS) , Heidelberg , Germany. Myers Chris J CJ Department of Electrical and Computer Engineering, University of Utah , Salt Lake City, UT , USA. Schreiber Falk F Faculty of Information Technology, Monash University , Melbourne, VIC , Australia ; Institute of Computer Science, University Halle-Wittenberg , Halle , Germany. Waltemath Dagmar D Department of Systems Biology and Bioinformatics, University of Rostock , Rostock , Germany. Le Novère Nicolas N Babraham Institute , Cambridge , UK ; European Molecular Biology Laboratory-European Bioinformatics Institute , Cambridge , UK. eng P50 GM094503 GM NIGMS NIH HHS United States Journal Article Review 2015 02 24

Switzerland Front Bioeng Biotechnol 101632513 2296-4185 PMC4338824 biology computational modeling data sharing file formats reproducible science standardization 2015 2014 11 10 2015 2 08 2015 2 24 2015 3 12 6 0 2015 3 12 6 0 2015 3 12 6 1 epublish 10.3389/fbioe.2015.00019 25759811 PMC4338824 25729925 2015 03 20 2015 05 19 2015 03 21

1529-2916 16 4 2015 Apr Nat. Immunol. IL-7 coordinates proliferation, differentiation and Tcra recombination during thymocyte β-selection. 397-405 10.1038/ni.3122 Signaling via the pre-T cell antigen receptor (pre-TCR) and the receptor Notch1 induces transient self-renewal (β-selection) of TCRβ(+) CD4(-)CD8(-) double-negative stage 3 (DN3) and DN4 progenitor cells that differentiate into CD4(+)CD8(+) double-positive (DP) thymocytes, which then rearrange the locus encoding the TCR α-chain (Tcra). Interleukin 7 (IL-7) promotes the survival of TCRβ(-) DN thymocytes by inducing expression of the pro-survival molecule Bcl-2, but the functions of IL-7 during β-selection have remained unclear. Here we found that IL-7 signaled TCRβ(+) DN3 and DN4 thymocytes to upregulate genes encoding molecules involved in cell growth and repressed the gene encoding the transcriptional repressor Bcl-6. Accordingly, IL-7-deficient DN4 cells lacked trophic receptors and did not proliferate but rearranged Tcra prematurely and differentiated rapidly. Deletion of Bcl6 partially restored the self-renewal of DN4 cells in the absence of IL-7, but overexpression of BCL2 did not. Thus, IL-7 critically acts cooperatively with signaling via the pre-TCR and Notch1 to coordinate proliferation, differentiation and Tcra recombination during β-selection. Boudil Amine A 1] Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada. [2] Department of Immunology, University of Toronto, Toronto, Canada. Matei Irina R IR Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada. Shih Han-Yu HY Department of Immunology, Duke University Medical Center, Durham, North Carolina, USA. Bogdanoski Goce G Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada. Yuan Julie S JS Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada. Chang Stephen G SG Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada. Montpellier Bertrand B 1] Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada. [2] Department of Immunology, University of Toronto, Toronto, Canada. Kowalski Paul E PE Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada. Voisin Veronique V The Donnelly Centre, University of Toronto, Toronto, Canada. Bashir Shaheena S The Donnelly Centre, University of Toronto, Toronto, Canada. Bader Gary D GD 0000000301858861 1] The Donnelly Centre, University of Toronto, Toronto, Canada. [2] Department of Molecular Genetics, University of Toronto, Toronto, Canada. Krangel Michael S MS Department of Immunology, Duke University Medical Center, Durham, North Carolina, USA. Guidos Cynthia J CJ 0000000276742612 1] Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada. [2] Department of Immunology, University of Toronto, Toronto, Canada. eng GEO GSE63932 FRN 11530 Canadian Institutes of Health Research Canada P41 GM103504 GM NIGMS NIH HHS United States P41 GM103504 GM NIGMS NIH HHS United States R37 GM041052 GM NIGMS NIH HHS United States R37 GM41052 GM NIGMS NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2015 03 02

United States Nat Immunol 100941354 1529-2908 0 Antigens, CD4 0 Antigens, CD8 0 Interleukin-7 0 Notch1 protein, mouse 0 Proto-Oncogene Proteins c-bcl-2 0 Proto-Oncogene Proteins c-bcl-6 0 Receptor, Notch1 0 Receptors, Antigen, T-Cell, alpha-beta 0 interleukin-7, mouse 114100-40-2 Bcl2 protein, mouse IM Immunol Res. 2011 Apr;49(1-3):192-201 21128009 Immunity. 2011 Feb 25;34(2):163-74 21349429 Blood. 2011 Oct 13;118(15):4174-8 21856866 Nat Immunol. 2011 Dec;12(12):1212-20 22037603 Nature. 2011 Aug 25;476(7361):467-71 21832993 J Exp Med. 2001 Aug 20;194(4):471-80 11514603 Immunity. 2002 Nov;17(5):561-73 12433363 Eur J Immunol. 2003 Jul;33(7):1968-77 12884863 J Immunol. 2004 Apr 1;172(7):4235-44 15034036 J Exp Med. 2004 Sep 20;200(6):797-803 15365098 J Exp Med. 2004 Oct 4;200(7):883-94 15452180 J Exp Med. 1990 Dec 1;172(6):1583-8 2147945 J Exp Med. 1994 Nov 1;180(5):1955-60 7964471 J Exp Med. 1995 Apr 1;181(4):1519-26 7699333 Science. 1997 Apr 25;276(5312):589-92 9110977 Nat Genet. 1997 Jun;16(2):161-70 9171827 Cell. 1997 Jun 27;89(7):1011-9 9215624 Cell. 1997 Jun 27;89(7):1033-41 9215626 Mol Cell Biol. 1998 Jun;18(6):3223-33 9584163 J Exp Med. 2004 Nov 1;200(9):1189-95 15504823 Eur J Immunol. 2005 Apr;35(4):1292-300 15770699 Immunity. 2006 Jan;24(1):53-64 16413923 Nat Immunol. 2006 Oct;7(10):1109-15 16936730 J Exp Med. 2006 Oct 2;203(10):2239-45 16966428 J Exp Med. 2006 Oct 2;203(10):2233-7 17000868 Oncogene. 2007 Jan 11;26(2):224-33 16819511 Nat Rev Immunol. 2007 Feb;7(2):144-54 17259970 Blood. 2007 Mar 1;109(5):1887-96 17077325 Proc Natl Acad Sci U S A. 2007 Apr 10;104(15):6323-8 17405860 EMBO J. 2007 Jul 25;26(14):3441-50 17599070 Immunity. 2008 Mar;28(3):335-45 18280186 Nat Immunol. 2008 Jun;9(6):613-22 18469817 J Exp Med. 2009 Apr 13;206(4):779-91 19349467 Curr Opin Immunol. 2009 Apr;21(2):133-9 19362456 Eur J Immunol. 2009 May;39(5):1231-40 19350552 Nat Rev Mol Cell Biol. 2010 Jan;11(1):9-22 20027184 J Exp Med. 2010 Jan 18;207(1):247-61 20038597 Nat Immunol. 2010 Feb;11(2):171-9 19946273 Annu Rev Immunol. 2010;28:343-65 20192807 J Immunol. 2012 Apr 1;188(7):3278-93 22357628 Proc Natl Acad Sci U S A. 2012 Dec 11;109(50):E3493-502 23169622 Nat Immunol. 2013 May;14(5):500-8 23525088 Lancet. 2013 Jun 1;381(9881):1943-55 23523389 J Exp Med. 2010 Jun 7;207(6):1209-21 20498019 Adv Immunol. 2010;105:193-210 20510734 J Immunol. 2013 Nov 1;191(9):4676-87 24068669 Blood. 2011 Jan 27;117(4):1184-95 21097675 Bioinformatics. 2011 Feb 1;27(3):431-2 21149340 Nat Immunol. 2015 Apr;16(4):337-8 25789675 Animals Antigens, CD4 genetics immunology Antigens, CD8 genetics immunology Cell Differentiation Cell Proliferation Cell Survival Gene Expression Regulation Interleukin-7 deficiency genetics immunology Mice Mice, Inbred C57BL Mice, Knockout Proto-Oncogene Proteins c-bcl-2 genetics immunology Proto-Oncogene Proteins c-bcl-6 deficiency genetics immunology Receptor, Notch1 genetics immunology Receptors, Antigen, T-Cell, alpha-beta genetics immunology Recombination, Genetic Signal Transduction Thymocytes cytology immunology metabolism Thymus Gland cytology immunology metabolism NIHMS663283 [Available on 10/01/15] PMC4368453 [Available on 10/01/15] 2014 12 01 2015 2 10 2015 3 02 2015 3 3 6 0 2015 3 3 6 0 2015 5 20 6 0 2015 10 1 0 0 ppublish ni.3122 10.1038/ni.3122 25729925 PMC4368453 NIHMS663283 25702641 2015 03 15 2015 04 04

2213-6711 4 3 2015 Mar 10 Stem Cell Reports A progesterone-CXCR4 axis controls mammary progenitor cell fate in the adult gland. 313-22 10.1016/j.stemcr.2015.01.011 S2213-6711(15)00032-6 Progesterone drives mammary stem and progenitor cell dynamics through paracrine mechanisms that are currently not well understood. Here, we demonstrate that CXCR4, the receptor for stromal-derived factor 1 (SDF-1; CXC12), is a crucial instructor of hormone-induced mammary stem and progenitor cell function. Progesterone elicits specific changes in the transcriptome of basal and luminal mammary epithelial populations, where CXCL12 and CXCR4 represent a putative ligand-receptor pair. In situ, CXCL12 localizes to progesterone-receptor-positive luminal cells, whereas CXCR4 is induced in both basal and luminal compartments in a progesterone-dependent manner. Pharmacological inhibition of CXCR4 signaling abrogates progesterone-directed expansion of basal (CD24(+)CD49f(hi)) and luminal (CD24(+)CD49f(lo)) subsets. This is accompanied by a marked reduction in CD49b(+)SCA-1(-) luminal progenitors, their functional capacity, and lobuloalveologenesis. These findings uncover CXCL12 and CXCR4 as novel paracrine effectors of hormone signaling in the adult mammary gland, and present a new avenue for potentially targeting progenitor cell growth and malignant transformation in breast cancer. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved. Shiah Yu-Jia YJ Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada. Tharmapalan Pirashaanthy P Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada. Casey Alison E AE Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada. Joshi Purna A PA Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada. McKee Trevor D TD Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; STTARR Innovation Centre, Radiation Medicine Program, Princess Margaret Hospital, Toronto, ON M5G 1L7, Canada. Jackson Hartland W HW Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada. Beristain Alexander G AG Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada. Chan-Seng-Yue Michelle A MA Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. Bader Gary D GD Department of Molecular Genetics, Medical Science Building, University of Toronto, Toronto, ON M5S 1A8, Canada. Lydon John P JP Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA. Waterhouse Paul D PD Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada. Boutros Paul C PC Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada; Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. Khokha Rama R Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada. Electronic address: rkhokha@uhnresearch.ca. eng GEO GSE59558 Journal Article Research Support, Non-U.S. Gov't 2015 02 19

United States Stem Cell Reports 101611300 2213-6711 IM Methods. 2003 Dec;31(4):265-73 14597310 Genome Res. 2003 Nov;13(11):2498-504 14597658 Endocrinology. 1997 Jun;138(6):2466-73 9165037 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 Nature. 2006 Jan 5;439(7072):84-8 16397499 Nature. 2006 Feb 23;439(7079):993-7 16395311 Stem Cells. 2006 Apr;24(4):1030-41 16253981 J Natl Cancer Inst. 2006 Jul 19;98(14):1011-4 16849684 Nat Cell Biol. 2008 Jun;10(6):716-22 18469806 Oncol Rep. 2009 Dec;22(6):1333-9 19885584 Proc Natl Acad Sci U S A. 2010 Feb 16;107(7):2989-94 20133621 Nature. 2010 Jun 10;465(7299):803-7 20445538 Nature. 2011 Jan 20;469(7330):314-22 21248838 Trends Endocrinol Metab. 2012 Jun;23(6):299-309 22613704 Oncotarget. 2014 Feb 15;5(3):599-612 24583601 Breast Cancer Res. 2012;14(5):R134 23088371 Biol Reprod. 2001 Sep;65(3):680-8 11514328 Nat Genet. 2001 Nov;29(3):295-300 11685206 Breast Cancer Res. 2001;3(6):365-72 11737887 Leukemia. 2002 Oct;16(10):1992-2003 12357350 Mol Endocrinol. 2002 Nov;16(11):2475-89 12403837 Genome Biol. 2003;4(4):R28 12702209 Proc Natl Acad Sci U S A. 2003 Aug 5;100(16):9440-5 12883005 Genome Biol. 2004;5(10):R80 15461798 PMC4376056 2014 7 21 2015 1 12 2015 1 13 2015 2 19 2015 2 24 6 0 2015 2 24 6 0 2015 2 24 6 0 ppublish S2213-6711(15)00032-6 10.1016/j.stemcr.2015.01.011 25702641 PMC4376056 25611800 2015 01 23 2015 06 30 2015 01 31

1553-7404 11 1 2015 Jan PLoS Genet. Evolutionary constraint and disease associations of post-translational modification sites in human genomes. e1004919 10.1371/journal.pgen.1004919 Interpreting the impact of human genome variation on phenotype is challenging. The functional effect of protein-coding variants is often predicted using sequence conservation and population frequency data, however other factors are likely relevant. We hypothesized that variants in protein post-translational modification (PTM) sites contribute to phenotype variation and disease. We analyzed fraction of rare variants and non-synonymous to synonymous variant ratio (Ka/Ks) in 7,500 human genomes and found a significant negative selection signal in PTM regions independent of six factors, including conservation, codon usage, and GC-content, that is widely distributed across tissue-specific genes and function classes. PTM regions are also enriched in known disease mutations, suggesting that PTM variation is more likely deleterious. PTM constraint also affects flanking sequence around modified residues and increases around clustered sites, indicating presence of functionally important short linear motifs. Using target site motifs of 124 kinases, we predict that at least ∼180,000 motif-breaker amino acid residues that disrupt PTM sites when substituted, and highlight kinase motifs that show specific negative selection and enrichment of disease mutations. We provide this dataset with corresponding hypothesized mechanisms as a community resource. As an example of our integrative approach, we propose that PTPN11 variants in Noonan syndrome aberrantly activate the protein by disrupting an uncharacterized cluster of phosphorylation sites. Further, as PTMs are molecular switches that are modulated by drugs, we study mutated binding sites of PTM enzymes in disease genes and define a drug-disease network containing 413 novel predicted disease-gene links. Reimand Jüri J The Donnelly Centre, University of Toronto, Canada. Wagih Omar O The Donnelly Centre, University of Toronto, Canada. Bader Gary D GD The Donnelly Centre, University of Toronto, Canada. eng MOP-84324 Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't 2015 01 22

United States PLoS Genet 101239074 1553-7390 0 Codon 0 Proteins EC 3.1.3.48 PTPN11 protein, human EC 3.1.3.48 Protein Tyrosine Phosphatase, Non-Receptor Type 11 IM Genome Res. 2009 Sep;19(9):1553-61 19602639 Pac Symp Biocomput. 2010;:337-47 19908386 Nucleic Acids Res. 2010 Jan;38(Database issue):D497-501 19884131 Nat Methods. 2010 Apr;7(4):248-9 20354512 Nat Biotechnol. 2010 Apr;28(4):322-4 20379172 Nat Methods. 2010 Aug;7(8):575-6 20676075 Cell. 2010 Sep 3;142(5):661-7 20813250 Nucleic Acids Res. 2010 Sep;38(16):e164 20601685 PLoS One. 2010;5(11):e13984 21085593 Nat Biotechnol. 2010 Dec;28(12):1248-50 21139605 Nucleic Acids Res. 2011 Jan;39(Database issue):D261-7 21062810 Nucleic Acids Res. 2011 Jul;39(Web Server issue):W307-15 21646343 Hum Mutat. 2011 Aug;32(8):894-9 21520341 Nature. 2011 Oct 20;478(7369):343-8 22012392 Nat Protoc. 2007;2(10):2366-82 17947979 Mol Cancer Res. 2007 Oct;5(10):981-9 17951399 Proc Natl Acad Sci U S A. 2007 Nov 6;104(45):17713-8 17978194 Nature. 2007 Nov 29;450(7170):663-9 18046402 Genome Res. 2008 May;18(5):695-705 18340042 Mutat Res. 2000 Apr;462(2-3):247-53 10767636 Nat Genet. 2000 May;25(1):25-9 10802651 Proc Natl Acad Sci U S A. 2001 Jun 5;98(12):6923-8 11381127 Science. 2001 Aug 10;293(5532):1074-80 11498575 Nucleic Acids Res. 2012 Jan;40(Database issue):D109-14 22080510 Nucleic Acids Res. 2012 Jan;40(Database issue):D261-70 22135298 Nat Biotechnol. 2012 Feb;30(2):159-64 22252508 Mol Cell. 2012 Jun 29;46(6):871-83 22749400 Science. 2012 Jul 6;337(6090):64-9 22604720 Nature. 2012 Aug 23;488(7412):504-7 22820252 Nature. 2012 Nov 1;491(7422):56-65 23128226 Nat Genet. 2001 Dec;29(4):465-8 11704759 Nat Rev Cancer. 2002 May;2(5):331-41 12044009 J Mol Biol. 2002 Oct 25;323(3):573-84 12381310 Science. 2002 Dec 6;298(5600):1912-34 12471243 Nat Biotechnol. 2003 Mar;21(3):255-61 12610572 Nat Rev Cancer. 2004 Mar;4(3):177-83 14993899 Genome Res. 2004 Jun;14(6):1188-90 15173120 Nat Med. 2004 Aug;10(8):789-99 15286780 Bioinformatics. 2004 Sep 1;20(13):2138-9 15044227 Nature. 1995 Feb 16;373(6515):573-80 7531822 J Biol Chem. 1996 Oct 4;271(40):24880-4 8798764 Nat Biotechnol. 2005 Jan;23(1):94-101 15592455 Trends Biochem Sci. 2005 Jun;30(6):286-90 15950870 Nat Rev Mol Cell Biol. 2005 Aug;6(8):599-609 16064136 J Biol Chem. 2005 Sep 2;280(35):30984-93 15987685 Nucleic Acids Res. 2007 Jan;35(Database issue):D721-6 17088289 Nat Biotechnol. 2008 Aug;26(8):864-6 18688235 Bioinformatics. 2008 Aug 15;24(16):i241-7 18689832 Sci Signal. 2008;1(35):ra2 18765831 Nucleic Acids Res. 2009 Jan;37(Database issue):D767-72 18988627 Nature. 2011 Dec 1;480(7375):128-31 21993622 Nat Rev Cancer. 2009 Jan;9(1):28-39 19104514 Mol Syst Biol. 2013;9:637 23340843 Nat Methods. 2013 Aug;10(8):723-9 23900255 Sci Rep. 2013;3:2651 24089029 Science. 2013 Oct 4;342(6154):1235587 24092746 Science. 2013 Dec 13;342(6164):1367-72 24337295 Nucleic Acids Res. 2014 Jan;42(Database issue):D1091-7 24203711 Nucleic Acids Res. 2014 Jan;42(Database issue):D472-7 24243840 Nucleic Acids Res. 2014 Jan;42(Database issue):D764-70 24270787 Nucleic Acids Res. 2014 Jan;42(Database issue):D749-55 24316576 Hum Genet. 2014 Jan;133(1):1-9 24077912 Nat Genet. 2014 Mar;46(3):310-5 24487276 Nature. 2014 Apr 24;508(7497):469-76 24759409 PLoS Genet. 2009 Jun;5(6):e1000529 19543373 Nat Protoc. 2009;4(7):1073-81 19561590 Sci Signal. 2009;2(81):ra39 19638616 Bioinformatics. 2009 Aug 15;25(16):2078-9 19505943 Base Composition Binding Sites Codon genetics Conserved Sequence genetics Genome, Human Humans Noonan Syndrome etiology genetics Protein Processing, Post-Translational genetics Protein Tyrosine Phosphatase, Non-Receptor Type 11 genetics Proteins genetics metabolism Selection, Genetic genetics PMC4303425 2015 1 2014 7 4 2014 11 24 2015 1 22 2015 1 23 6 0 2015 1 23 6 0 2015 7 1 6 0 epublish 10.1371/journal.pgen.1004919 PGENETICS-D-14-01805 25611800 PMC4303425 25580224 2015 01 12 2015 01 12 2015 01 14

2046-1402 3 2014 F1000Res The Cytoscape app article collection. 138 10.12688/f1000research.4642.1 As a network visualization and analysis platform, Cytoscape relies on apps to provide domain-specific features and functions. There are many resources available to support Cytoscape app development and distribution, including the Cytoscape App Store and an online "cookbook" for app developers. This article collection is another resource to help researchers find out more about relevant Cytoscape apps and to provide app developers with useful implementation tips. The collection will grow over time as new Cytoscape apps are developed and published. Pico Alexander R AR Gladstone Institutes, San Francisco, CA 95158, USA. Bader Gary D GD The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada. Demchak Barry B University of California, San Diego, La Jolla, CA 92093, USA. Guitart Pla Oriol O Institut Pasteur, Paris, 75015, France. Hull Timothy T University of California, San Diego, La Jolla, CA 92093, USA. Longabaugh William W Institute for Systems Biology, Seattle, WA 98109, USA. Lopes Christian C The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada. Lotia Samad S Gladstone Institutes, San Francisco, CA 95158, USA. Molenaar Piet P Amsterdam Medical Centre, Amsterdam, 1105 AZ, Netherlands. Montojo Jason J The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada. Morris John H JH University of California San Francisco, San Francisco, CA 94143, USA. Ono Keiichiro K University of California, San Diego, La Jolla, CA 92093, USA. Schwikowski Benno B Institut Pasteur, Paris, 75015, France. Welker David D University of California, San Diego, La Jolla, CA 92093, USA. Ideker Trey T University of California, San Diego, La Jolla, CA 92093, USA. eng Journal Article 2014 07 01

England F1000Res 101594320 2046-1402 PMC4288400 2014 2014 6 26 2014 7 1 2015 1 13 6 0 2015 1 13 6 0 2015 1 13 6 1 epublish 10.12688/f1000research.4642.1 25580224 PMC4288400 25561528 2015 01 21 2015 04 27 2015 03 17

1091-6490 112 3 2015 Jan 20 Proc. Natl. Acad. Sci. U.S.A. Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. 851-6 10.1073/pnas.1320611111 Glioblastoma (GBM) is a cancer comprised of morphologically, genetically, and phenotypically diverse cells. However, an understanding of the functional significance of intratumoral heterogeneity is lacking. We devised a method to isolate and functionally profile tumorigenic clones from patient glioblastoma samples. Individual clones demonstrated unique proliferation and differentiation abilities. Importantly, naïve patient tumors included clones that were temozolomide resistant, indicating that resistance to conventional GBM therapy can preexist in untreated tumors at a clonal level. Further, candidate therapies for resistant clones were detected with clone-specific drug screening. Genomic analyses revealed genes and pathways that associate with specific functional behavior of single clones. Our results suggest that functional clonal profiling used to identify tumorigenic and drug-resistant tumor clones will lead to the discovery of new GBM clone-specific treatment strategies. Meyer Mona M Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8; Reimand Jüri J Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8; The Donnelly Centre, University of Toronto, Toronto, ON, Canada M5S 3E1 Canada; Lan Xiaoyang X Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8; Head Renee R Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8; Zhu Xueming X Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8; Kushida Michelle M Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8; Bayani Jane J Department of Transformative Pathology at the Ontario Institute for Cancer Research, Ontario Institute for Cancer Research, Toronto, ON, Canada M5G 0A3; Pressey Jessica C JC Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada M5S 3G5; Lionel Anath C AC Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8; The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada M5G 1L7; Clarke Ian D ID Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8; Ontario College of Art and Design, Toronto, ON, Canada M5T 1W1; Cusimano Michael M Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada M5B 1W8; Squire Jeremy A JA Department of Pathology, Queen's University, Kingston, ON, Canada K7L 3N6; Scherer Stephen W SW Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8; The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada M5G 1L7; Bernstein Mark M Division of Neurosurgery, Toronto Western Hospital, Toronto, ON, Canada M5T 2S8; and. Woodin Melanie A MA Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada M5S 3G5; Bader Gary D GD Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8; The Donnelly Centre, University of Toronto, Toronto, ON, Canada M5S 3E1 Canada; gary.bader@utoronto.ca peter.dirks@sickkids.ca. Dirks Peter B PB Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada M5S 1A8 gary.bader@utoronto.ca peter.dirks@sickkids.ca. eng P41GM103504 GM NIGMS NIH HHS United States R01 CA121941 CA NCI NIH HHS United States Canadian Institutes of Health Research Canada Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2015 01 05

United States Proc Natl Acad Sci U S A 7505876 0027-8424 0 Antineoplastic Agents 7GR28W0FJI Dacarbazine 85622-93-1 temozolomide IM Nat Methods. 2014 Apr;11(4):396-8 24633410 PLoS One. 2010;5(11):e13984 21085593 Nat Rev Cancer. 2014 Feb;14(2):92-107 24457416 N Engl J Med. 2000 Nov 9;343(19):1350-4 11070098 Bioinformatics. 2003 Aug 12;19(12):1572-4 12912839 Nat Genet. 2004 Sep;36(9):949-51 15286789 Biostatistics. 2004 Oct;5(4):557-72 15475419 Mol Biol Cell. 1993 Jan;4(1):121-33 7680247 Proc Natl Acad Sci U S A. 1994 Aug 2;91(16):7727-31 8052651 Nature. 2004 Nov 18;432(7015):396-401 15549107 J Neurooncol. 2004 Nov;70(2):217-28 15674479 Nature. 2011 Apr 7;472(7341):90-4 21399628 Proc Natl Acad Sci U S A. 2011 Jun 14;108(24):9951-6 21628563 Nucleic Acids Res. 2011 Jul;39(Web Server issue):W307-15 21646343 Cancer Cell. 2011 Dec 13;20(6):810-7 22137795 Proc Natl Acad Sci U S A. 2012 Feb 21;109(8):3041-6 22323597 Cancer Res. 2012 Apr 1;72(7):1614-20 22311673 Nature. 2012 Jun 21;486(7403):395-9 22495314 Clin Cancer Res. 2012 Aug 15;18(16):4240-6 22648273 PLoS Genet. 2012;8(8):e1002843 22912587 Cancer Cell. 2012 Oct 16;22(4):425-37 23079654 Proc Natl Acad Sci U S A. 2013 Mar 5;110(10):4009-14 23412337 Cell. 2013 Oct 10;155(2):462-77 24120142 Science. 2014 Jan 10;343(6167):189-93 24336570 Cell Biochem Biophys. 2007;47(2):178-86 17652770 Science. 2008 Sep 26;321(5897):1807-12 18772396 Nature. 2008 Oct 23;455(7216):1061-8 18772890 Science. 2008 Nov 21;322(5905):1247-50 19023080 N Engl J Med. 2005 Mar 10;352(10):987-96 15758009 Nat Chem Biol. 2007 May;3(5):268-73 17417631 Bioinformatics. 2009 Sep 1;25(17):2149-56 19535535 Cell Stem Cell. 2009 Nov 6;5(5):504-14 19896441 Cancer Cell. 2010 Jan 19;17(1):98-110 20129251 Cancer Cell. 2010 May 18;17(5):510-22 20399149 Neuro Oncol. 2010 Aug;12(8):882-9 20472883 Science. 2014 Jun 20;344(6190):1396-401 24925914 Antineoplastic Agents therapeutic use Brain Neoplasms drug therapy genetics pathology Cell Line, Tumor Dacarbazine analogs & derivatives therapeutic use Drug Resistance, Neoplasm Glioblastoma drug therapy genetics pathology Humans Single-Cell Analysis PMC4311802 cancer clonal heterogeneity functional analysis genomic analysis glioblastoma 2015 1 5 2015 1 7 6 0 2015 1 7 6 0 2015 4 29 6 0 ppublish 25561528 1320611111 10.1073/pnas.1320611111 PMC4311802 25361207 2014 12 03 2015 02 15

1742-2051 11 1 2015 Jan Mol Biosyst Systems analysis reveals down-regulation of a network of pro-survival miRNAs drives the apoptotic response in dilated cardiomyopathy. 239-51 10.1039/c4mb00265b Apoptosis is a hallmark of multiple etiologies of heart failure, including dilated cardiomyopathy. Since microRNAs are master regulators of cardiac development and key effectors of intracellular signaling, they represent novel candidates for understanding the mechanisms driving the increased dysfunction and loss of cardiomyocytes during cardiovascular disease progression. To determine the role of cardiac miRNAs in the apoptotic response, we used microarray technology to monitor miRNA levels in a validated murine phospholambam mutant model of dilated cardiomyopathy. 24 miRNAs were found to be differentially expressed, most of which have not been previously linked to dilated cardiomyopathy. We showed that individual silencing of 7 out of 8 significantly down-regulated miRNAs (mir-1, -29c, -30c, -30d, -149, -486, -499) led to a strong apoptotic phenotype in cell culture, suggesting they repress pro-apoptotic factors. To identify putative miRNA targets most likely relevant to cell death, we computationally integrated transcriptomic, proteomic and functional annotation data. We showed the dependency of prioritized target abundance on miRNA expression using RNA interference and quantitative mass spectrometry. We concluded that down regulation of key pro-survival miRNAs causes up-regulation of apoptotic signaling effectors that contribute to cardiac cell loss, potentially leading to system decompensation and heart failure. Isserlin Ruth R The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario, Canada M5S 3E1. andrew.emili@utoronto.ca. Merico Daniele D Wang Dingyan D Vuckovic Dajana D Bousette Nicolas N Gramolini Anthony O AO Bader Gary D GD Emili Andrew A eng P41 GM103504 GM NIGMS NIH HHS United States P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2014 10 31

England Mol Biosyst 101251620 1742-2051 IM 2014 10 31 2014 12 2 2014 11 1 6 0 2014 11 2 6 0 2014 11 2 6 0 ppublish 10.1039/c4mb00265b 25361207 25330770 2014 12 04 2015 07 21 2015 03 17

1757-4684 6 12 2014 Dec EMBO Mol Med Combined deletion of Pten and p53 in mammary epithelium accelerates triple-negative breast cancer with dependency on eEF2K. 1542-60 10.15252/emmm.201404402 The tumor suppressors Pten and p53 are frequently lost in breast cancer, yet the consequences of their combined inactivation are poorly understood. Here, we show that mammary-specific deletion of Pten via WAP-Cre, which targets alveolar progenitors, induced tumors with shortened latency compared to those induced by MMTV-Cre, which targets basal/luminal progenitors. Combined Pten-p53 mutations accelerated formation of claudin-low, triple-negative-like breast cancer (TNBC) that exhibited hyper-activated AKT signaling and more mesenchymal features relative to Pten or p53 single-mutant tumors. Twenty-four genes that were significantly and differentially expressed between WAP-Cre:Pten/p53 and MMTV-Cre:Pten/p53 tumors predicted poor survival for claudin-low patients. Kinome screens identified eukaryotic elongation factor-2 kinase (eEF2K) inhibitors as more potent than PI3K/AKT/mTOR inhibitors on both mouse and human Pten/p53-deficient TNBC cells. Sensitivity to eEF2K inhibition correlated with AKT pathway activity. eEF2K monotherapy suppressed growth of Pten/p53-deficient TNBC xenografts in vivo and cooperated with doxorubicin to efficiently kill tumor cells in vitro. Our results identify a prognostic signature for claudin-low patients and provide a rationale for using eEF2K inhibitors for treatment of TNBC with elevated AKT signaling. © 2014 The Authors. Published under the terms of the CC BY 4.0 license. Liu Jeff C JC Division of Advanced Diagnostics, Toronto General Research Institute - University Health Network, Toronto, ON, Canada. Voisin Veronique V The Donnelly Centre, University of Toronto, Toronto, ON, Canada. Wang Sharon S Division of Advanced Diagnostics, Toronto General Research Institute - University Health Network, Toronto, ON, Canada Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada. Wang Dong-Yu DY Princess Margaret Cancer Center, Toronto, ON, Canada Campbell Family Institute for Breast Cancer Research, Princess Margaret Hospital, Toronto, ON, Canada. Jones Robert A RA Division of Advanced Diagnostics, Toronto General Research Institute - University Health Network, Toronto, ON, Canada. Datti Alessandro A SMART Laboratory for High-Throughput Screening Programs, Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital, Toronto, ON, Canada Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, Italy. Uehling David D Drug Discovery Program, Department of Pharmacology and Toxicology, Ontario Institute for Cancer Research, University of Toronto, Toronto, ON, Canada. Al-awar Rima R Drug Discovery Program, Department of Pharmacology and Toxicology, Ontario Institute for Cancer Research, University of Toronto, Toronto, ON, Canada. Egan Sean E SE Program in Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada. Bader Gary D GD The Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada. Tsao Ming M Princess Margaret Cancer Center, Toronto, ON, Canada Department of Medical Biophysics, University Health Network, Toronto, ON, Canada. Mak Tak W TW Campbell Family Institute for Breast Cancer Research, Princess Margaret Hospital, Toronto, ON, Canada SMART Laboratory for High-Throughput Screening Programs, Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital, Toronto, ON, Canada Department of Medical Biophysics, University Health Network, Toronto, ON, Canada. Zacksenhaus Eldad E Division of Advanced Diagnostics, Toronto General Research Institute - University Health Network, Toronto, ON, Canada Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada Department of Medical Biophysics, University Health Network, Toronto, ON, Canada eldad.zacksenhaus@utoronto.ca. eng GM103504 GM NIGMS NIH HHS United States R01 CA121941 CA NCI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. 2014 10 20

England EMBO Mol Med 101487380 1757-4676 0 Enzyme Inhibitors 0 Tumor Suppressor Protein p53 EC 2.7.1.17 Elongation Factor 2 Kinase EC 2.7.11.1 Oncogene Protein v-akt EC 3.1.3.67 PTEN Phosphohydrolase IM Cell. 2013 May 23;153(5):1064-79 23706743 Clin Cancer Res. 2013 Jul 15;19(14):3738-44 23748695 Oncotarget. 2013 Aug;4(8):1317-28 23945331 Cancer Cell. 2013 Oct 14;24(4):450-65 24094812 Cell Stem Cell. 2014 Mar 6;14(3):275-91 24607403 Cancer Res. 2014 Apr 1;74(7):2119-30 24487029 J Pathol. 2014 Jun;233(2):124-37 24615332 Cancer Cell. 2014 Aug 11;26(2):163-76 25043604 Oncogene. 2015 Feb 5;34(6):671-80 24531711 Cancer Res. 2000 Jul 1;60(13):3605-11 10910075 Mol Cell. 2001 Aug;8(2):317-25 11545734 Development. 2002 Mar;129(6):1377-86 11880347 Development. 2002 Sep;129(17):4159-70 12163417 Cancer Res. 2003 Oct 15;63(20):6894-9 14583488 Bioinformatics. 2004 Jan 1;20(1):105-14 14693816 Autophagy. 2012 Apr;8(4):445-544 22966490 Int J Cancer. 2012 Nov 15;131(10):2456-64 22422301 Cancer Cell. 2012 Dec 11;22(6):709-24 23201163 Cancer Cell. 2012 Dec 11;22(6):725-36 23201165 Nat Rev Cancer. 2006 Mar;6(3):184-92 16453012 Cancer Cell. 2006 Dec;10(6):515-27 17157791 Curr Biol. 2007 Jan 23;17(2):115-25 17240336 Mol Cancer Res. 2007 Feb;5(2):195-201 17314276 Clin Cancer Res. 2007 Apr 15;13(8):2329-34 17438091 Proc Natl Acad Sci U S A. 2007 May 1;104(18):7564-9 17452630 Cell. 2012 Mar 2;148(5):1015-28 22385965 J Clin Oncol. 2012 Mar 10;30(8):777-82 22271473 Nature. 2012 Jun 21;486(7403):395-9 22495314 Proc Natl Acad Sci U S A. 2012 Jul 24;109(30):12046-51 22753496 Breast Cancer Res. 2010;12(5):R68 20813035 Sci Rep. 2012;2:227 22355741 Genome Biol. 2007;8(5):R76 17493263 Cancer Res. 2007 Sep 15;67(18):8671-81 17875707 Cell. 2008 May 2;133(3):403-14 18455982 Nat Cell Biol. 2008 May;10(5):593-601 18376396 Cell. 2008 May 16;133(4):704-15 18485877 Clin Exp Metastasis. 2008;25(6):629-42 18461285 Oncogene. 2008 Sep 18;27(41):5497-510 18794884 Biochem Biophys Res Commun. 2008 Dec 26;377(4):1205-10 18996088 Br J Cancer. 2009 Jan 27;100(2):405-11 19165203 Cell. 2009 Feb 6;136(3):535-50 19203586 J Clin Oncol. 2009 Mar 10;27(8):1160-7 19204204 Cancer Res. 2009 Mar 15;69(6):2453-60 19244119 Autophagy. 2009 Apr;5(3):393-6 19221463 BMC Med. 2009;7:9 19291283 PLoS Biol. 2009 Jun 2;7(6):e1000121 19492080 Nat Rev Clin Oncol. 2013 Mar;10(3):143-53 23400000 Proc Natl Acad Sci U S A. 2003 Dec 23;100(26):15853-8 14668450 Mol Pharmacol. 2004 Sep;66(3):460-7 15322237 Science. 1997 Mar 28;275(5308):1943-7 9072974 Cancer Cell. 2007 Jun;11(6):555-69 17560336 Nat Med. 2009 Aug;15(8):907-13 19648928 Nat Med. 2009 Aug;15(8):842-4 19661985 Proc Natl Acad Sci U S A. 2009 Aug 18;106(33):13820-5 19666588 Nat Protoc. 2009;4(12):1798-806 20010931 PLoS One. 2010;5(3):e9715 20300520 Nat Rev Cancer. 2010 Apr;10(4):254-66 20332778 Proc Natl Acad Sci U S A. 2010 Apr 13;107(15):6994-9 20335537 J Mammary Gland Biol Neoplasia. 2010 Jun;15(2):235-52 20521089 J Clin Invest. 2010 Sep;120(9):3296-309 20679727 Proc Natl Acad Sci U S A. 2010 Aug 31;107(35):15449-54 20713713 Nature. 2010 Oct 21;467(7318):986-90 20962848 PLoS One. 2010;5(11):e13984 21085593 BMC Cancer. 2010;10:654 21118481 Mol Oncol. 2011 Feb;5(1):5-23 21147047 Nat Cell Biol. 2011 Mar;13(3):317-23 21336307 J Exp Med. 2011 May 9;208(5):875-83 21518799 Cell Cycle. 2011 May 15;10(10):1563-70 21502814 Oncotarget. 2011 Jun;2(6):435-47 21646685 Nat Genet. 1997 Apr;15(4):356-62 9090379 Cell. 1998 Oct 2;95(1):29-39 9778245 Curr Biol. 1998 Oct 22;8(21):1169-78 9799734 J Pharmacol Exp Ther. 2005 Apr;313(1):325-32 15626722 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 J Clin Invest. 2011 Jul;121(7):2750-67 21633166 Expert Rev Anticancer Ther. 2011 Aug;11(8):1243-51 21916578 Proc Natl Acad Sci U S A. 2012 Feb 21;109(8):2778-83 21633010 Nat Cell Biol. 2013 Mar;15(3):274-83 23434823 Proc Natl Acad Sci U S A. 2013 Apr 2;110(14):E1301-10 23509284 EMBO Mol Med. 2014 Dec;6(12):1512-4 25471453 Animals Elongation Factor 2 Kinase antagonists & inhibitors genetics metabolism Enzyme Inhibitors pharmacology Epithelium enzymology metabolism Female Gene Deletion Humans Mammary Glands, Human enzymology metabolism Mice Mice, Inbred C57BL Oncogene Protein v-akt genetics metabolism PTEN Phosphohydrolase genetics metabolism Triple Negative Breast Neoplasms drug therapy enzymology genetics Tumor Suppressor Protein p53 genetics metabolism PMC4287974 Pten eEF2K p53 prognosis triple‐negative breast cancer 2014 10 22 6 0 2014 10 22 6 0 2015 7 22 6 0 epublish 25330770 emmm.201404402 10.15252/emmm.201404402 PMC4287974 25254104 2014 09 25 2014 09 25 2014 09 27

2046-1402 3 2014 F1000Res GeneMANIA: Fast gene network construction and function prediction for Cytoscape. 153 10.12688/f1000research.4572.1 The GeneMANIA Cytoscape app enables users to construct a composite gene-gene functional interaction network from a gene list. The resulting network includes the genes most related to the original list, and functional annotations from Gene Ontology. The edges are annotated with details about the publication or data source the interactions were derived from. The app leverages GeneMANIA's database of 1800+ networks, containing over 500 million interactions spanning 8 organisms: A. thaliana, C. elegans, D. melanogaster, D. rerio, H. sapiens, M. musculus, R. norvegicus, and S. cerevisiae. Users may also import their own organisms, networks, and expression profiles. The app is compatible with Cytoscape versions 2 and 3. Montojo Jason J Departments of Molecular Genetics and Computer Science, The Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada. Zuberi Khalid K Departments of Molecular Genetics and Computer Science, The Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada. Rodriguez Harold H Departments of Molecular Genetics and Computer Science, The Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada. Bader Gary D GD Departments of Molecular Genetics and Computer Science, The Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada. Morris Quaid Q Departments of Molecular Genetics and Computer Science, The Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada. eng Journal Article 2014 07 01

England F1000Res 101594320 2046-1402 PMC4168749 2014 2014 6 20 2014 7 1 2014 9 26 6 0 2014 9 26 6 0 2014 9 26 6 1 epublish 10.12688/f1000research.4572.1 25254104 PMC4168749 25199793 2014 09 09

1934-340X 47 2014 Curr Protoc Bioinformatics Biological network exploration with cytoscape 3. 8.13.1-8.13.24 10.1002/0471250953.bi0813s47 Cytoscape is one of the most popular open-source software tools for the visual exploration of biomedical networks composed of protein, gene, and other types of interactions. It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data, thereby facilitating research tasks such as predicting gene function and constructing pathways. Cytoscape provides core functionality to load, visualize, search, filter, and save networks, and hundreds of Apps extend this functionality to address specific research needs. The latest generation of Cytoscape (version 3.0 and later) has substantial improvements in function, user interface, and performance relative to previous versions. This protocol aims to jump-start new users with specific protocols for basic Cytoscape functions, such as installing Cytoscape and Cytoscape Apps, loading data, visualizing and navigating the networks, visualizing network associated data (attributes), and identifying clusters. It also highlights new features that benefit experienced users. Curr. Protoc. Bioinform. 47:8.13.1-8.13.24. © 2014 by John Wiley & Sons, Inc. Copyright © 2014 John Wiley & Sons, Inc. Su Gang G Molecular Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, Michigan. Morris John H JH Demchak Barry B Bader Gary D GD eng P41 GM103504 GM NIGMS NIH HHS United States Journal Article 2014 09 08

United States Curr Protoc Bioinformatics 101157830 1934-3396 IM Nucleic Acids Res. 2013 Jan;41(Database issue):D816-23 23203989 Bioinformatics. 2002;18 Suppl 1:S233-40 12169552 Bioinformatics. 2013 May 15;29(10):1350-1 23595664 Nucleic Acids Res. 2013 Jul;41(Web Server issue):W115-22 23794635 Nucleic Acids Res. 2005 Jan 1;33(Database issue):D514-7 15608251 Nucleic Acids Res. 2009 Jan;37(Database issue):D642-6 18978014 Bioinformatics. 2007 Sep 1;23(17):2345-7 17623706 Proc Natl Acad Sci U S A. 2007 May 22;104(21):8685-90 17502601 PLoS Comput Biol. 2007 Apr 20;3(4):e59 17447836 Nat Protoc. 2006;1(2):662-71 17406294 Nucleic Acids Res. 2007 Jan;35(Database issue):D760-5 17099226 Bioinformatics. 2005 Aug 15;21(16):3448-9 15972284 Bioinformatics. 2005 Apr 1;21(7):1164-71 15513999 Bioinformatics. 2005 Feb 15;21(4):430-8 15608051 Nucleic Acids Res. 2014 Jan;42(Database issue):D1269-74 24271398 Nucleic Acids Res. 2005 Jan 1;33(Database issue):D428-32 15608231 Genome Res. 2003 Nov;13(11):2498-504 14597658 BMC Bioinformatics. 2003 Jan 13;4:2 12525261 Novartis Found Symp. 2002;247:91-101; discussion 101-3, 119-28, 244-52 12539951 Nucleic Acids Res. 2013 Jan;41(Database issue):D808-15 23203871 Nat Methods. 2012 Nov;9(11):1069-76 23132118 Nat Protoc. 2012 Apr;7(4):670-85 22422314 Bioinformatics. 2012 Feb 1;28(3):373-80 22135418 BMC Bioinformatics. 2011;12:436 22070249 Mol Biosyst. 2012 Feb;8(2):453-63 22159132 Nucleic Acids Res. 2012 Jan;40(Database issue):D841-6 22121220 Nucleic Acids Res. 2012 Jan;40(Database issue):D1301-7 22096230 Trends Biochem Sci. 2011 Apr;36(4):179-82 21345680 Bioinformatics. 2010 Dec 15;26(24):3135-7 21123224 Bioinformatics. 2010 Nov 15;26(22):2927-8 20926419 Bioinformatics. 2010 Apr 1;26(7):971-3 20139469 Bioinformatics. 2009 Nov 1;25(21):2857-9 19729372 Nat Biotechnol. 2009 Oct;27(10):921-4 19816451 PLoS One. 2009;4(2):e4345 19190775 Mol Biol Cell. 2000 Dec;11(12):4241-57 11102521 Nucleic Acids Res. 2001 Jan 1;29(1):242-5 11125103 Nucleic Acids Res. 2002 Jan 1;30(1):207-10 11752295 Nat Biotechnol. 2013 Jan;31(1):38-45 23242164 NIHMS628460 [Available on 09/08/15] PMC4174321 [Available on 09/08/15] Cytoscape interactive network visualization network analysis 2014 9 10 6 0 2014 9 10 6 0 2014 9 10 6 0 2015 9 8 0 0 epublish 10.1002/0471250953.bi0813s47 25199793 PMC4174321 NIHMS628460 25075306 2014 07 30 2014 07 30 2015 03 17

2046-1402 3 2014 F1000Res Enrichment Map - a Cytoscape app to visualize and explore OMICs pathway enrichment results. 141 10.12688/f1000research.4536.1 High-throughput OMICs experiments generate signals for millions of entities (i.e. genes, proteins, metabolites or any measurable biological entity) in the cell. In an effort to summarize and explore these signals, expression results are examined in the context of known pathways and processes, through enrichment analysis to generate a set of pathways and processes that is significantly enriched. Due to the high redundancy in annotation resources this often results in hundreds of sets. To facilitate the analysis of these results, we have developed the Enrichment Map app to visualize enrichments as a network. We have updated Enrichment Map to support Cytoscape 3, and have added additional features including new data formats and command line access. Isserlin Ruth R The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada. Merico Daniele D The Centre for Applied Genomics, Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada. Voisin Veronique V The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada. Bader Gary D GD The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada. eng R01 CA121941 CA NCI NIH HHS United States Journal Article 2014 07 01

England F1000Res 101594320 2046-1402 PMC4103489 2014 2014 6 20 2014 7 1 2014 7 31 6 0 2014 7 31 6 0 2014 7 31 6 1 epublish 10.12688/f1000research.4536.1 25075306 PMC4103489 25043047 2014 07 24 2014 10 09 2015 07 29

1476-4687 511 7510 2014 Jul 24 Nature Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. 428-34 10.1038/nature13379 Medulloblastoma is a highly malignant paediatric brain tumour currently treated with a combination of surgery, radiation and chemotherapy, posing a considerable burden of toxicity to the developing child. Genomics has illuminated the extensive intertumoral heterogeneity of medulloblastoma, identifying four distinct molecular subgroups. Group 3 and group 4 subgroup medulloblastomas account for most paediatric cases; yet, oncogenic drivers for these subtypes remain largely unidentified. Here we describe a series of prevalent, highly disparate genomic structural variants, restricted to groups 3 and 4, resulting in specific and mutually exclusive activation of the growth factor independent 1 family proto-oncogenes, GFI1 and GFI1B. Somatic structural variants juxtapose GFI1 or GFI1B coding sequences proximal to active enhancer elements, including super-enhancers, instigating oncogenic activity. Our results, supported by evidence from mouse models, identify GFI1 and GFI1B as prominent medulloblastoma oncogenes and implicate 'enhancer hijacking' as an efficient mechanism driving oncogene activation in a childhood cancer. Northcott Paul A PA 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany [2]. Lee Catherine C 1] Biomedical Sciences Graduate Program, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093-0685, USA [2] Tumor Initiation and Maintenance Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, California 92037, USA [3]. Zichner Thomas T 1] European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, Heidelberg 69117, Germany [2]. Stütz Adrian M AM European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, Heidelberg 69117, Germany. Erkek Serap S 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany [2] European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, Heidelberg 69117, Germany. Kawauchi Daisuke D Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Shih David J H DJ The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada. Hovestadt Volker V Division of Molecular Genetics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Zapatka Marc M Division of Molecular Genetics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Sturm Dominik D Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Jones David T W DT Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Kool Marcel M Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Remke Marc M The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada. Cavalli Florence M G FM The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada. Zuyderduyn Scott S The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada. Bader Gary D GD The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada. VandenBerg Scott S Department of Pathology, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. Esparza Lourdes Adriana LA Tumor Initiation and Maintenance Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, California 92037, USA. Ryzhova Marina M Department of Neuropathology, NN Burdenko Neurosurgical Institute, 4th Tverskaya-Yamskaya 16, Moscow 125047, Russia. Wang Wei W Division of Molecular Genetics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Wittmann Andrea A Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Stark Sebastian S Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Sieber Laura L Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Seker-Cin Huriye H Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Linke Linda L Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Kratochwil Fabian F Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Jäger Natalie N Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Buchhalter Ivo I Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Imbusch Charles D CD Data Management Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Zipprich Gideon G Data Management Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Raeder Benjamin B European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, Heidelberg 69117, Germany. Schmidt Sabine S Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Diessl Nicolle N Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Wolf Stephan S Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Wiemann Stefan S Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Brors Benedikt B Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Lawerenz Chris C Data Management Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Eils Jürgen J Data Management Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Warnatz Hans-Jörg HJ Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, Berlin 14195, Germany. Risch Thomas T Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, Berlin 14195, Germany. Yaspo Marie-Laure ML Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, Berlin 14195, Germany. Weber Ursula D UD Division of Molecular Genetics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Bartholomae Cynthia C CC Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, Heidelberg 69120, Germany. von Kalle Christof C 1] Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, Heidelberg 69120, Germany [2] Heidelberg Center for Personalised Oncology (DKFZ-HIPO), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Turányi Eszter E 1st Department of Pathology and Experimental Cancer Research, Semmelweis University SE, II.sz. Gyermekklinika, Budapest 1094, Hungary. Hauser Peter P 2nd Department of Pediatrics, Semmelweis University, SE, II.sz. Gyermekklinika, Budapest 1094, Hungary. Sanden Emma E 1] Glioma Immunotherapy Group, Division of Neurosurgery, Lund University, Paradisgatan 2, Lund 221 00, Sweden [2] Department of Clinical Sciences, Lund University, Paradisgatan 2, Lund 221 00, Sweden. Darabi Anna A 1] Glioma Immunotherapy Group, Division of Neurosurgery, Lund University, Paradisgatan 2, Lund 221 00, Sweden [2] Department of Clinical Sciences, Lund University, Paradisgatan 2, Lund 221 00, Sweden. Siesjö Peter P 1] Glioma Immunotherapy Group, Division of Neurosurgery, Lund University, Paradisgatan 2, Lund 221 00, Sweden [2] Department of Clinical Sciences, Lund University, Paradisgatan 2, Lund 221 00, Sweden. Sterba Jaroslav J Department of Pediatric Oncology, Masaryk University and University Hospital, Brno, Cernopolni 9 Brno 613 00, Czech Republic. Zitterbart Karel K Department of Pediatric Oncology, Masaryk University and University Hospital, Brno, Cernopolni 9 Brno 613 00, Czech Republic. Sumerauer David D Department of Pediatric Hematology and Oncology, 2nd Faculty of Medicine, Charles University and University Hospital Motol, V Úvalu 84, Prague 150 06, Czech Republic. van Sluis Peter P Department of Oncogenomics, AMC, University of Amsterdam, Meibergdreef 9, Amsterdam 1105, AZ Netherlands. Versteeg Rogier R Department of Oncogenomics, AMC, University of Amsterdam, Meibergdreef 9, Amsterdam 1105, AZ Netherlands. Volckmann Richard R Department of Oncogenomics, AMC, University of Amsterdam, Meibergdreef 9, Amsterdam 1105, AZ Netherlands. Koster Jan J Department of Oncogenomics, AMC, University of Amsterdam, Meibergdreef 9, Amsterdam 1105, AZ Netherlands. Schuhmann Martin U MU Department of Neurosurgery, Tübingen University Hospital, Hoppe-Seyler Strasse 3, Tübingen 72076, Germany. Ebinger Martin M Department of Neurosurgery, Tübingen University Hospital, Hoppe-Seyler Strasse 3, Tübingen 72076, Germany. Grimes H Leighton HL Division of Immunobiology, Program in Cancer Pathology of the Divisions of Experimental Hematology and Pathology, Program in Hematologic Malignancies of the Cancer and Blood Disease Insitute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, Ohio 452229, USA. Robinson Giles W GW 1] Department of Developmental Neurobiology, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, USA [2] Department of Oncology, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, USA. Gajjar Amar A Department of Oncology, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, USA. Mynarek Martin M Department of Paediatric Haematology and Oncology, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, Hamburg 20246, Germany. von Hoff Katja K Department of Paediatric Haematology and Oncology, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, Hamburg 20246, Germany. Rutkowski Stefan S Department of Paediatric Haematology and Oncology, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, Hamburg 20246, Germany. Pietsch Torsten T Department of Neuropathology, University of Bonn, Sigmund-Freud-Str. 25, Bonn 53105, Germany. Scheurlen Wolfram W Cnopf'sche Kinderklinik, Nürnberg Children's Hospital, St-Johannis-Mühlgasse 19, Nürnberg 90419, Germany. Felsberg Jörg J Department of Neuropathology, Heinrich-Heine-University Düsseldorf, Moorenstrasse 5, Düsseldorf 40225, Germany. Reifenberger Guido G Department of Neuropathology, Heinrich-Heine-University Düsseldorf, Moorenstrasse 5, Düsseldorf 40225, Germany. Kulozik Andreas E AE Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Im Neuenheimer Feld 430, Heidelberg 69120, Germany. von Deimling Andreas A Department of Neuropathology, University of Heidelberg, Im Neuenheimer Feld 220, Heidelberg 69120, Germany. Witt Olaf O Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Im Neuenheimer Feld 430, Heidelberg 69120, Germany. Eils Roland R 1] Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany [2] Heidelberg Center for Personalised Oncology (DKFZ-HIPO), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Gilbertson Richard J RJ 1] Department of Developmental Neurobiology, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, USA [2] Department of Oncology, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, USA. Korshunov Andrey A Department of Neuropathology, University of Heidelberg, Im Neuenheimer Feld 220, Heidelberg 69120, Germany. Taylor Michael D MD 1] The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada [2] Division of Neurosurgery, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada. Lichter Peter P 1] Division of Molecular Genetics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany [2] Heidelberg Center for Personalised Oncology (DKFZ-HIPO), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Korbel Jan O JO 1] European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, Heidelberg 69117, Germany [2] EMBL, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Saffron Walden CB10 1SD, UK. Wechsler-Reya Robert J RJ Tumor Initiation and Maintenance Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, California 92037, USA. Pfister Stefan M SM 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany [2] Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Im Neuenheimer Feld 430, Heidelberg 69120, Germany. eng 5P30CA030199 CA NCI NIH HHS United States P01 CA096832 CA NCI NIH HHS United States P30 CA030199 CA NCI NIH HHS United States P41GM103504 GM NIGMS NIH HHS United States R01 CA159859 CA NCI NIH HHS United States R01 CA159859 CA NCI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2014 06 22

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1744-4292 10 2014 Mol. Syst. Biol. Intercellular network structure and regulatory motifs in the human hematopoietic system. 741 10.15252/msb.20145141 The hematopoietic system is a distributed tissue that consists of functionally distinct cell types continuously produced through hematopoietic stem cell (HSC) differentiation. Combining genomic and phenotypic data with high-content experiments, we have built a directional cell-cell communication network between 12 cell types isolated from human umbilical cord blood. Network structure analysis revealed that ligand production is cell type dependent, whereas ligand binding is promiscuous. Consequently, additional control strategies such as cell frequency modulation and compartmentalization were needed to achieve specificity in HSC fate regulation. Incorporating the in vitro effects (quiescence, self-renewal, proliferation, or differentiation) of 27 HSC binding ligands into the topology of the cell-cell communication network allowed coding of cell type-dependent feedback regulation of HSC fate. Pathway enrichment analysis identified intracellular regulatory motifs enriched in these cell type- and ligand-coupled responses. This study uncovers cellular mechanisms of hematopoietic cell feedback in HSC fate regulation, provides insight into the design principles of the human hematopoietic system, and serves as a foundation for the analysis of intercellular regulation in multicellular systems. © 2014 The Authors. Published under the terms of the CC BY 4.0 license. Qiao Wenlian W Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada. Wang Weijia W Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada. Laurenti Elisa E Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada. Turinsky Andrei L AL The Hospital for Sick Children, Toronto, ON, Canada. Wodak Shoshana J SJ The Hospital for Sick Children, Toronto, ON, Canada Department of Biochemistry, University of Toronto, Toronto, ON, Canada. Bader Gary D GD Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Department of Computer Science, University of Toronto, Toronto, ON, Canada The Donnelly Centre, University of Toronto, Toronto, ON, Canada. Dick John E JE Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada. Zandstra Peter W PW Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada The Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada McEwen Centre for Regenerative Medicine, University of Health Network, Toronto, ON, Canada Heart & Stroke/Richard Lewar Centre of Excellence, Toronto, ON, Canada peter.zandstra@utoronto.ca. eng P41 GM103504 GM NIGMS NIH HHS United States Canadian Institutes of Health Research Canada Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2014 07 15

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1664-8021 5 2014 Front Genet Network Assessor: an automated method for quantitative assessment of a network's potential for gene function prediction. 123 10.3389/fgene.2014.00123 Significant effort has been invested in network-based gene function prediction algorithms based on the guilt by association (GBA) principle. Existing approaches for assessing prediction performance typically compute evaluation metrics, either averaged across all functions being considered, or strictly from properties of the network. Since the success of GBA algorithms depends on the specific function being predicted, evaluation metrics should instead be computed for each function. We describe a novel method for computing the usefulness of a network by measuring its impact on gene function cross validation prediction performance across all gene functions. We have implemented this in software called Network Assessor, and describe its use in the GeneMANIA (GM) quality control system. Network Assessor is part of the GM command line tools. Montojo Jason J Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada. Zuberi Khalid K Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada. Shao Quentin Q Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada. Bader Gary D GD Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada. Morris Quaid Q Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada. eng Journal Article 2014 05 16

Switzerland Front Genet 101560621 1664-8021 PMC4032932 cross validation function prediction machine learning network biology network inference 2014 2014 1 06 2014 4 21 2014 5 16 2014 6 7 6 0 2014 6 7 6 0 2014 6 7 6 1 epublish 10.3389/fgene.2014.00123 24904632 PMC4032932 24878544 2014 06 02 2015 01 12

1932-6203 9 5 2014 PLoS ONE Characterizing the Late Pleistocene MSA Lithic Technology of Sibudu, KwaZulu-Natal, South Africa. e98359 10.1371/journal.pone.0098359 Studies of the African Middle Stone Age (MSA) have become central for defining the cultural adaptations that accompanied the evolution of modern humans. While much of recent research in South Africa has focused on the Still Bay and Howiesons Poort (HP), periods following these technocomplexes were often neglected. Here we examine lithic assemblages from Sibudu that post-date the HP to further the understanding of MSA cultural variability during the Late Pleistocene. Sibudu preserves an exceptionally thick, rich, and high-resolution archaeological sequence that dates to ∼ 58 ka, which has recently been proposed as type assemblage for the "Sibudan". This study presents a detailed analysis of the six uppermost lithic assemblages from these deposits (BM-BSP) that we excavated from 2011-2013. We define the key elements of the lithic technology and compare our findings to other assemblages post-dating the HP. The six lithic assemblages provide a distinct and robust cultural signal, closely resembling each other in various technological, techno-functional, techno-economic, and typological characteristics. These results refute assertions that modern humans living after the HP possessed an unstructured and unsophisticated MSA lithic technology. While we observed several parallels with other contemporaneous MSA sites, particularly in the eastern part of southern Africa, the lithic assemblages at Sibudu demonstrate a distinct and so far unique combination of techno-typological traits. Our findings support the use of the Sibudan to help structuring this part of the southern African MSA and emphasize the need for further research to identify the spatial and temporal extent of this proposed cultural unit. Will Manuel M Department of Early Prehistory and Quaternary Ecology, University of Tübingen, Schloss Hohentübingen, Tübingen, Germany. Bader Gregor D GD Department of Early Prehistory and Quaternary Ecology, University of Tübingen, Schloss Hohentübingen, Tübingen, Germany. Conard Nicholas J NJ Department of Early Prehistory and Quaternary Ecology, University of Tübingen, Schloss Hohentübingen, Tübingen, Germany; Senckenberg Center for Human Evolution and Paleoecology, University of Tübingen, Schloss Hohentübingen, Tübingen, Germany. eng Journal Article Research Support, Non-U.S. Gov't 2014 05 30

United States PLoS One 101285081 1932-6203 IM J Hum Evol. 2000 Nov;39(5):453-563 11102266 Proc Natl Acad Sci U S A. 2009 Jun 16;106(24):9590-4 19433786 Science. 2002 Feb 15;295(5558):1278-80 11786608 Nature. 2003 Jun 12;423(6941):742-7 12802332 Curr Anthropol. 2003 Dec;44(5):627-51 14971366 Science. 2004 Apr 16;304(5669):404 15087540 Proc Natl Acad Sci U S A. 2004 Apr 20;101(16):5708-15 15007171 Philos Trans R Soc Lond B Biol Sci. 2004 Feb 29;359(1442):255-64; discussion 264 15101581 J Hum Evol. 2005 Jan;48(1):3-24 15656934 Science. 2009 Aug 14;325(5942):859-62 19679810 Proc Natl Acad Sci U S A. 2010 Apr 6;107(14):6180-5 20194764 Proc Natl Acad Sci U S A. 2010 Apr 27;107(17):7621-2 20410459 J Hum Evol. 2010 Sep-Oct;59(3-4):425-43 20934095 Science. 2011 Oct 14;334(6053):219-22 21998386 Science. 2011 Dec 9;334(6061):1388-91 22158814 Nature. 2012 Nov 22;491(7425):590-3 23135405 J Hum Evol. 2013 Jun;64(6):518-37 23628196 Nat Commun. 2013;4:1905 23695699 PLoS One. 2013;8(11):e80031 24236171 J Hum Evol. 2014 Jul;72:26-51 24746546 Nature. 2005 Feb 17;433(7027):733-6 15716951 J Hum Evol. 2005 Mar;48(3):279-300 15737394 J Hum Evol. 2006 Mar;50(3):359-64 16469361 Proc Natl Acad Sci U S A. 2006 Jun 20;103(25):9381-6 16772383 J Hum Evol. 2007 Feb;52(2):142-63 16996574 J Hum Evol. 2007 Jun;52(6):681-9 17337038 Nature. 2007 Oct 18;449(7164):905-8 17943129 Science. 2008 Oct 31;322(5902):733-5 18974351 Science. 2009 Jun 5;324(5932):1298-301 19498164 J Hum Evol. 2001 Dec;41(6):631-78 11782112 Archaeology Biological Evolution Culture Fossils Humans South Africa Technology Tool Use Behavior PMC4039507 2014 2014 2 5 2014 5 1 2014 5 30 2014 6 1 6 0 2014 6 1 6 0 2015 1 13 6 0 epublish 10.1371/journal.pone.0098359 PONE-D-14-05289 24878544 PMC4039507 24870542 2014 05 29 2014 07 07 2015 07 27

1476-4687 509 7502 2014 May 29 Nature A draft map of the human proteome. 575-81 10.1038/nature13302 The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease. Kim Min-Sik MS 1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. Pinto Sneha M SM Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Getnet Derese D 1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Adrienne Helis Malvin Medical Research Foundation, New Orleans, Louisiana 70130, USA. Nirujogi Raja Sekhar RS Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Manda Srikanth S SS Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Chaerkady Raghothama R 1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. Madugundu Anil K AK Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Kelkar Dhanashree S DS Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Isserlin Ruth R The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada. Jain Shobhit S The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada. Thomas Joji K JK Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Muthusamy Babylakshmi B Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Leal-Rojas Pamela P 1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Pathology, Universidad de La Frontera, Center of Genetic and Immunological Studies-Scientific and Technological Bioresource Nucleus, Temuco 4811230, Chile. Kumar Praveen P Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Sahasrabuddhe Nandini A NA Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Balakrishnan Lavanya L Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Advani Jayshree J Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. George Bijesh B Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Renuse Santosh S Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Selvan Lakshmi Dhevi N LD Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Patil Arun H AH Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Nanjappa Vishalakshi V Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Radhakrishnan Aneesha A Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Prasad Samarjeet S McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. Subbannayya Tejaswini T Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Raju Rajesh R Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Kumar Manish M Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Sreenivasamurthy Sreelakshmi K SK Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Marimuthu Arivusudar A Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Sathe Gajanan J GJ Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Chavan Sandip S Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Datta Keshava K KK Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Subbannayya Yashwanth Y Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Sahu Apeksha A Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Yelamanchi Soujanya D SD Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Jayaram Savita S Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Rajagopalan Pavithra P Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Sharma Jyoti J Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Murthy Krishna R KR Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Syed Nazia N Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Goel Renu R Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Khan Aafaque A AA Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Ahmad Sartaj S Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Dey Gourav G Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Mudgal Keshav K School of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ, UK. Chatterjee Aditi A Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Huang Tai-Chung TC McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. Zhong Jun J McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. Wu Xinyan X 1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. Shaw Patrick G PG McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. Freed Donald D McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. Zahari Muhammad S MS Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. Mukherjee Kanchan K KK Department of Neurosurgery, Postgraduate Institute of Medical Education & Research, Chandigarh 160012, India. Shankar Subramanian S Department of Internal Medicine Armed Forces Medical College, Pune 411040, India. Mahadevan Anita A 1] Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India [2] Human Brain Tissue Repository, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore 560029, India. Lam Henry H Department of Chemical and Biomolecular Engineering and Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. Mitchell Christopher J CJ McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. Shankar Susarla Krishna SK 1] Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India [2] Human Brain Tissue Repository, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore 560029, India. Satishchandra Parthasarathy P Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India. Schroeder John T JT Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21224, USA. Sirdeshmukh Ravi R Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Maitra Anirban A 1] The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [2] Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA. Leach Steven D SD 1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA. Drake Charles G CG 1] Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [2] Departments of Immunology and Urology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA. Halushka Marc K MK The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA. Prasad T S Keshava TS Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Hruban Ralph H RH 1] The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [2] Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA. Kerr Candace L CL 1] Department of Obstetrics and Gynecology, Johns Hopkins University School of Medicine Baltimore, Maryland 21205, USA [2] Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA. Bader Gary D GD The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada. Iacobuzio-Donahue Christine A CA 1] The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [2] Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [3] Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA. Gowda Harsha H Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. Pandey Akhilesh A 1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [3] Institute of Bioinformatics, International Tech Park, Bangalore 560066, India [4] Adrienne Helis Malvin Medical Research Foundation, New Orleans, Louisiana 70130, USA [5] The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [6] Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [7] Diana Helis Henry Medical Research Foundation, New Orleans, Louisiana 70130, USA. eng HHSN268201000032C HL NHLBI NIH HHS United States HHSN268201000032C PHS HHS United States P41 GM103504 GM NIGMS NIH HHS United States P41GM103504 GM NIGMS NIH HHS United States T32 GM007814 GM NIGMS NIH HHS United States U24 CA160036 CA NCI NIH HHS United States U24CA160036 CA NCI NIH HHS United States U54 GM103520 GM NIGMS NIH HHS United States U54GM103520 GM NIGMS NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

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1095-9203 344 6180 2014 Apr 11 Science Mapping the cellular response to small molecules using chemogenomic fitness signatures. 208-11 10.1126/science.1250217 Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to understanding how cells survive stress. Identifying the proteins and pathways perturbed by small molecules affects biology and medicine by revealing the mechanisms of drug action. We used a yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We identified 317 compounds that specifically perturb the function of 121 genes and characterized the mechanism of specific compounds. Global analysis revealed that the cellular response to small molecules is limited and described by a network of 45 major chemogenomic signatures. Our results provide a resource for the discovery of functional interactions among genes, chemicals, and biological processes. Lee Anna Y AY The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada. St Onge Robert P RP Proctor Michael J MJ Wallace Iain M IM Nile Aaron H AH Spagnuolo Paul A PA Jitkova Yulia Y Gronda Marcela M Wu Yan Y Kim Moshe K MK Cheung-Ong Kahlin K Torres Nikko P NP Spear Eric D ED Han Mitchell K L MK Schlecht Ulrich U Suresh Sundari S Duby Geoffrey G Heisler Lawrence E LE Surendra Anuradha A Fung Eula E Urbanus Malene L ML Gebbia Marinella M Lissina Elena E Miranda Molly M Chiang Jennifer H JH Aparicio Ana Maria AM Zeghouf Mahel M Davis Ronald W RW Cherfils Jacqueline J Boutry Marc M Kaiser Chris A CA Cummins Carolyn L CL Trimble William S WS Brown Grant W GW Schimmer Aaron D AD Bankaitis Vytas A VA Nislow Corey C Bader Gary D GD Giaever Guri G eng GM103504 GM NIGMS NIH HHS United States GM44530 GM NIGMS NIH HHS United States MOP-700724 Canadian Institutes of Health Research Canada MOP-79368 Canadian Institutes of Health Research Canada MOP-81340 Canadian Institutes of Health Research Canada P01 HG000205 HG NHGRI NIH HHS United States P41 GM103504 GM NIGMS NIH HHS United States R01 003317-07 PHS HHS United States R01 CA157456 CA NCI NIH HHS United States R01 GM044530 GM NIGMS NIH HHS United States R01 HG003317 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

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1932-6203 9 4 2014 PLoS ONE Prediction and experimental characterization of nsSNPs altering human PDZ-binding motifs. e94507 10.1371/journal.pone.0094507 Single nucleotide polymorphisms (SNPs) are a major contributor to genetic and phenotypic variation within populations. Non-synonymous SNPs (nsSNPs) modify the sequence of proteins and can affect their folding or binding properties. Experimental analysis of all nsSNPs is currently unfeasible and therefore computational predictions of the molecular effect of nsSNPs are helpful to guide experimental investigations. While some nsSNPs can be accurately characterized, for instance if they fall into strongly conserved or well annotated regions, the molecular consequences of many others are more challenging to predict. In particular, nsSNPs affecting less structured, and often less conserved regions, are difficult to characterize. Binding sites that mediate protein-protein or other protein interactions are an important class of functional sites on proteins and can be used to help interpret nsSNPs. Binding sites targeted by the PDZ modular peptide recognition domain have recently been characterized. Here we use this data to show that it is possible to computationally identify nsSNPs in PDZ binding motifs that modify or prevent binding to the proteins containing the motifs. We confirm these predictions by experimentally validating a selected subset with ELISA. Our work also highlights the importance of better characterizing linear motifs in proteins as many of these can be affected by genetic variations. Gfeller David D The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada; Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, Lausanne, Switzerland. Ernst Andreas A The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. Jarvik Nick N The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. Sidhu Sachdev S SS The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. Bader Gary D GD The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. eng Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't 2014 04 10

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1367-4811 30 15 2014 Aug 1 Bioinformatics HyperModules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery. 2230-2 10.1093/bioinformatics/btu172 Correlating disease mutations with clinical and phenotypic information such as drug response or patient survival is an important goal of personalized cancer genomics and a first step in biomarker discovery. HyperModules is a network search algorithm that finds frequently mutated gene modules with significant clinical or phenotypic signatures from biomolecular interaction networks. HyperModules is available in Cytoscape App Store and as a command line tool at www.baderlab.org/Sofware/HyperModules. Juri.Reimand@utoronto.ca or Gary.Bader@utoronto.ca Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. Leung Alvin A The Donnelly Centre, University of Toronto, 160 College Street, M5S 3E1 Toronto, Ontario, Canada. Bader Gary D GD The Donnelly Centre, University of Toronto, 160 College Street, M5S 3E1 Toronto, Ontario, Canada. Reimand Jüri J The Donnelly Centre, University of Toronto, 160 College Street, M5S 3E1 Toronto, Ontario, Canada. eng P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, N.I.H., Extramural 2014 04 08

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1878-3686 25 3 2014 Mar 17 Cancer Cell Genome sequencing of SHH medulloblastoma predicts genotype-related response to smoothened inhibition. 393-405 10.1016/j.ccr.2014.02.004 S1535-6108(14)00073-7 Smoothened (SMO) inhibitors recently entered clinical trials for sonic-hedgehog-driven medulloblastoma (SHH-MB). Clinical response is highly variable. To understand the mechanism(s) of primary resistance and identify pathways cooperating with aberrant SHH signaling, we sequenced and profiled a large cohort of SHH-MBs (n = 133). SHH pathway mutations involved PTCH1 (across all age groups), SUFU (infants, including germline), and SMO (adults). Children >3 years old harbored an excess of downstream MYCN and GLI2 amplifications and frequent TP53 mutations, often in the germline, all of which were rare in infants and adults. Functional assays in different SHH-MB xenograft models demonstrated that SHH-MBs harboring a PTCH1 mutation were responsive to SMO inhibition, whereas tumors harboring an SUFU mutation or MYCN amplification were primarily resistant. Copyright © 2014 Elsevier Inc. All rights reserved. Kool Marcel M Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Electronic address: m.kool@dkfz.de. Jones David T W DT Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Jäger Natalie N Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Northcott Paul A PA Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Pugh Trevor J TJ Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA. Hovestadt Volker V Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Piro Rosario M RM Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Esparza L Adriana LA Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA. Markant Shirley L SL Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA. Remke Marc M The Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada. Milde Till T Department of Pediatric Oncology, Hematology and Immunology, University Hospital Heidelberg, 69120 Heidelberg, Germany. Bourdeaut Franck F Institut Curie, 75005 Paris, France; Institut Curie/INSERM U830, 75248 Paris, France. Ryzhova Marina M Department of Neuropathology, NN Burdenko Neurosurgical Institute, Moscow 125047, Russia. Sturm Dominik D Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Pfaff Elke E Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Stark Sebastian S Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Hutter Sonja S Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Seker-Cin Huriye H Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Johann Pascal P Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Bender Sebastian S Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Schmidt Christin C Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Rausch Tobias T European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany. Shih David D The Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada. Reimand Jüri J The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada. Sieber Laura L Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Wittmann Andrea A Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Linke Linda L Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Witt Hendrik H Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany; Department of Pediatric Oncology, Hematology and Immunology, University Hospital Heidelberg, 69120 Heidelberg, Germany. Weber Ursula D UD Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Zapatka Marc M Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. König Rainer R Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany; Integrated Research and Treatment Center, Center for Sepsis Control and Care, Jena University Hospital, 07747 Jena, Germany; Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute (HKI), 07745 Jena, Germany. Beroukhim Rameen R Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA; Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. Bergthold Guillaume G Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA; Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA; UMR 8203, CNRS Vectorology and Anticancer Therapeutics, Gustave Roussy Cancer Institute, University Paris XI, 94805 Villejuif Cedex, France. van Sluis Peter P Department of Oncogenomics, Academic Medical Center, Amsterdam 1105 AZ, the Netherlands. Volckmann Richard R Department of Oncogenomics, Academic Medical Center, Amsterdam 1105 AZ, the Netherlands. Koster Jan J Department of Oncogenomics, Academic Medical Center, Amsterdam 1105 AZ, the Netherlands. Versteeg Rogier R Department of Oncogenomics, Academic Medical Center, Amsterdam 1105 AZ, the Netherlands. Schmidt Sabine S Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Wolf Stephan S Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Lawerenz Chris C Data Management Facility, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Bartholomae Cynthia C CC Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69121 Heidelberg, Germany. von Kalle Christof C Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69121 Heidelberg, Germany. Unterberg Andreas A Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69121 Heidelberg, Germany. Herold-Mende Christel C Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69121 Heidelberg, Germany. Hofer Silvia S Department of Oncology, University Hospital Zürich, 8006 Zürich, Switzerland. Kulozik Andreas E AE Department of Pediatric Oncology, Hematology and Immunology, University Hospital Heidelberg, 69120 Heidelberg, Germany. von Deimling Andreas A Department of Neuropathology, University of Heidelberg, 69120 Heidelberg, Germany; Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Scheurlen Wolfram W Cnopf'sche Kinderklinik, Nürnberg Children's Hospital, 90419 Nürnberg, Germany. Felsberg Jörg J Department of Neuropathology, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany. Reifenberger Guido G Department of Neuropathology, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany. Hasselblatt Martin M Institute for Neuropathology, University Hospital Münster, 48149 Münster, Germany. Crawford John R JR Departments of Pediatrics and Neurosciences, University of California San Diego, La Jolla, CA 92093; Rady Children's Hospital, San Diego, CA 92123, USA. Grant Gerald A GA Division of Pediatric Hematology/Oncology, Department of Pediatrics, Duke University Medical Center, Durham, NC 27710, USA; Department of Surgery, Duke University Medical Center, Durham, NC 27710, USA. Jabado Nada N Departments of Pediatrics and Human Genetics, McGill University Health Centre Research Institute, Montreal, QC H3H 1P3, Canada. Perry Arie A Departments of Pathology and Laboratory Medicine, University of California, San Francisco, San Francisco, CA 94143, USA. Cowdrey Cynthia C Departments of Pathology and Neurological Surgery, Brain Tumor Research Center, University of California, San Francisco, San Francisco, CA 94143, USA. Croul Sydney S Department of Neuropathology, The Arthur and Sonia Labatt Brain Tumour Research Centre, Toronto, ON M5G 1L7, Canada. Zadeh Gelareh G Department of Neuropathology, The Arthur and Sonia Labatt Brain Tumour Research Centre, Toronto, ON M5G 1L7, Canada. Korbel Jan O JO European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany. Doz Francois F Institut Curie, 75005 Paris, France; Université Paris Descartes, 75006 Paris, France. Delattre Olivier O Institut Curie, 75005 Paris, France; Institut Curie/INSERM U830, 75248 Paris, France. Bader Gary D GD The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada. McCabe Martin G MG Manchester Academic Health Science Centre, Manchester M13 9NT, UK. Collins V Peter VP Department of Pathology, University of Cambridge, Cambridge CB2 1QP, UK. Kieran Mark W MW Pediatric Medical Neuro-Oncology, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA 02215, USA. Cho Yoon-Jae YJ Department of Neurology and Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA. Pomeroy Scott L SL Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA. Witt Olaf O CCU Pediatric Oncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Brors Benedikt B Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Taylor Michael D MD The Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada. Schüller Ulrich U Center for Neuropathology and Prion Research, Ludwig-Maximilians-Universität, 81377 München, Germany. Korshunov Andrey A Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany; Department of Neuropathology, University of Heidelberg, 69120 Heidelberg, Germany; Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Eils Roland R Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Wechsler-Reya Robert J RJ Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA. Lichter Peter P Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Pfister Stefan M SM Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany; Department of Pediatric Oncology, Hematology and Immunology, University Hospital Heidelberg, 69120 Heidelberg, Germany. ICGC PedBrain Tumor Project eng GENBANK GSE49243 GSE49377 GSE49576 K08 NS075144 NS NINDS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't

United States Cancer Cell 101130617 1535-6108 0 Biphenyl Compounds 0 GLI2 protein, human 0 Hedgehog Proteins 0 Kruppel-Like Transcription Factors 0 LDE225 0 MYCN protein, human 0 Nuclear Proteins 0 Oncogene Proteins 0 Pyridines 0 Receptors, Cell Surface 0 Receptors, G-Protein-Coupled 0 Repressor Proteins 0 SHH protein, human 0 SMO protein, human 0 SUFU protein, human 0 TP53 protein, human 0 Tumor Suppressor Protein p53 0 patched receptors EC 2.7.1.- Phosphatidylinositol 3-Kinases EC 2.7.11.1 Proto-Oncogene Proteins c-akt EC 2.7.7.49 TERT protein, human EC 2.7.7.49 Telomerase EC 3.6.1.- DDX3X protein, human EC 3.6.4.13 DEAD-box RNA Helicases IM EMBO J. 2014 Sep 17;33(18):1984-6 25061226 Adolescent Adult Animals Base Sequence Biphenyl Compounds therapeutic use Cerebellar Neoplasms drug therapy genetics Child Child, Preschool DEAD-box RNA Helicases genetics DNA Copy Number Variations genetics Drug Resistance, Neoplasm genetics Female Gene Expression Profiling Hedgehog Proteins genetics High-Throughput Nucleotide Sequencing Humans Infant Kruppel-Like Transcription Factors genetics Male Medulloblastoma drug therapy genetics Mice Mice, Inbred NOD Mice, SCID Molecular Sequence Data Neoplasm Transplantation Nuclear Proteins genetics Oncogene Proteins genetics Phosphatidylinositol 3-Kinases genetics metabolism Promoter Regions, Genetic genetics Proto-Oncogene Proteins c-akt genetics metabolism Pyridines therapeutic use Receptors, Cell Surface genetics Receptors, G-Protein-Coupled antagonists & inhibitors genetics Repressor Proteins genetics Signal Transduction genetics Telomerase genetics Tumor Suppressor Protein p53 genetics Young Adult NIHMS628780 PMC4493053 2013 8 5 2013 11 19 2014 2 13 2014 3 22 6 0 2014 3 22 6 0 2014 5 14 6 0 ppublish S1535-6108(14)00073-7 10.1016/j.ccr.2014.02.004 24651015 PMC4493053 NIHMS628780 24596128 2014 04 15 2014 12 09 2015 01 27

1615-9861 14 9 2014 May Proteomics Highlights of B/D-HPP and HPP Resource Pillar Workshops at 12th Annual HUPO World Congress of Proteomics: September 14-18, 2013, Yokohama, Japan. 975-88 10.1002/pmic.201400041 At the 12th Annual HUPO World Congress of Proteomics in Japan, the Human Proteome Project (HPP) presented 16 scientific workshop sessions. Here we summarize highlights of ten workshops from the Biology and Disease-driven HPP (B/D-HPP) teams and three from the HPP Resource Pillars. Highlights of the three Chromosome-centric HPP sessions appeared in the many articles of the 2014 C-HPP special issue of the Journal of Proteome Research . © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Aebersold Ruedi R Institute of Molecular Systems Biology, Seattle, WA, USA. Bader Gary D GD Edwards Aled M AM van Eyk Jennifer J Kussman Martin M Qin Jun J Omenn Gilbert S GS eng 092809 Wellcome Trust United Kingdom Journal Article

Germany Proteomics 101092707 1615-9853 IM Computational Biology Databases, Protein Humans Japan Proteomics Bioinformatics Biology and Disease-driven Human Proteome Project HUPO-2013 Yokohama Congress Human Proteome Project Organ and biofluid proteomes ProteomeAnalyzer 2014 1 31 2014 2 20 2014 3 6 6 0 2014 3 7 6 0 2014 12 15 6 0 ppublish 24596128 10.1002/pmic.201400041 24553142 2014 02 27 2014 03 13 2015 05 01

1476-4687 506 7489 2014 Feb 27 Nature Epigenomic alterations define lethal CIMP-positive ependymomas of infancy. 445-50 10.1038/nature13108 Ependymomas are common childhood brain tumours that occur throughout the nervous system, but are most common in the paediatric hindbrain. Current standard therapy comprises surgery and radiation, but not cytotoxic chemotherapy as it does not further increase survival. Whole-genome and whole-exome sequencing of 47 hindbrain ependymomas reveals an extremely low mutation rate, and zero significant recurrent somatic single nucleotide variants. Although devoid of recurrent single nucleotide variants and focal copy number aberrations, poor-prognosis hindbrain ependymomas exhibit a CpG island methylator phenotype. Transcriptional silencing driven by CpG methylation converges exclusively on targets of the Polycomb repressive complex 2 which represses expression of differentiation genes through trimethylation of H3K27. CpG island methylator phenotype-positive hindbrain ependymomas are responsive to clinical drugs that target either DNA or H3K27 methylation both in vitro and in vivo. We conclude that epigenetic modifiers are the first rational therapeutic candidates for this deadly malignancy, which is epigenetically deregulated but genetically bland. Mack S C SC 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada [3] Division of Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada [4]. Witt H H 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg 69120, Germany [3] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [4]. Piro R M RM 1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany. Gu L L 1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany. Zuyderduyn S S Department of Molecular Genetics, Banting and Best Department of Medical Research, The Donnelly Centre, University of Toronto, Toronto, Ontario M4N 1X8, Canada. Stütz A M AM 1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Genome Biology, European Molecular Biology, Laboratory Meyerhofstr. 1, Heidelberg 69117, Germany. Wang X X 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Gallo M M Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Garzia L L Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Zayne K K Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Zhang X X Department of Genetics, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, New Hampshire 03756, USA. Ramaswamy V V 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Jäger N N 1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany. Jones D T W DT 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. Sill M M 1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany. Pugh T J TJ Department of Neurology, Harvard Medical School, Children's Hospital Boston, MIT, Boston, Massachusetts 02115, USA. Ryzhova M M 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. Wani K M KM Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. Shih D J H DJ 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Head R R Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Remke M M 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Bailey S D SD 1] Ontario Cancer Institute, Princess Margaret Cancer Centre-University Health Network, Toronto, Ontario M5G 1L7, Canada [2] Ontario Institute for Cancer Research, Toronto, Ontario M5G 1L7, Canada. Zichner T T 1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Genome Biology, European Molecular Biology, Laboratory Meyerhofstr. 1, Heidelberg 69117, Germany. Faria C C CC Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Barszczyk M M 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Stark S S 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. Seker-Cin H H 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. Hutter S S 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. Johann P P 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. Bender S S 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. Hovestadt V V 1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany. Tzaridis T T 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. Dubuc A M AM 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Northcott P A PA 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. Peacock J J 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Bertrand K C KC 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Agnihotri S S Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Cavalli F M G FM Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Clarke I I Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Nethery-Brokx K K Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Creasy C L CL Cancer Epigenetics Discovery Performance Unit, GlaxoSmithKline Pharmaceuticals, Collegeville, Pennsylvania 19426, USA. Verma S K SK Cancer Epigenetics Discovery Performance Unit, GlaxoSmithKline Pharmaceuticals, Collegeville, Pennsylvania 19426, USA. Koster J J Department of Oncogenomics, Academic Medical Center, Amsterdam 1105, The Netherlands. Wu X X Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Yao Y Y 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Milde T T 1] Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg 69120, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [3] CCU Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany. Sin-Chan P P Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Zuccaro J J Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Lau L L Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Pereira S S Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Castelo-Branco P P Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Hirst M M 1] Centre for High-Throughput Biology, Department of Microbiology & Immunology, University of British Columbia, Vancouver, V6T 1Z4 British Columbia, Canada [2] Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada. Marra M A MA 1] Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada [2] Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia V6H 3N1, Canada. Roberts S S SS Department of Pediatrics and National Capital Consortium, Uniformed Services University, Bethesda, Maryland 20814, USA. Fults D D Department of Neurosurgery, University of Utah School of Medicine, Salt Lake City, Utah 84132, USA. Massimi L L Pediatric Neurosurgery, Catholic University Medical School, Gemelli Hospital, Rome 00168, Italy. Cho Y J YJ Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California 94305, USA. Van Meter T T Department of Pediatrics, Virginia Commonwealth, Richmond, Virginia 23298-0646, USA. Grajkowska W W Department of Pathology, University of Warsaw, Children's Memorial Health Institute University of Warsaw, Warsaw 04-730, Poland. Lach B B Division of Anatomical Pathology, Department of Pathology and Molecular Medicine, McMaster University, Hamilton General Hospital, Hamilton, Ontario L8S 4K1, Canada. Kulozik A E AE 1] Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg 69120, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. von Deimling A A 1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Department of Neuropathology Ruprecht-Karls-University Heidelberg, Institute of Pathology, Heidelberg 69120, Germany. Witt O O 1] Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg 69120, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [3] CCU Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany. Scherer S W SW Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Fan X X 1] University of Michigan Cell and Developmental Biology, Ann Arbor, Michigan 48109-2200, USA [2] Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA. Muraszko K M KM Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA. Kool M M 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. Pomeroy S L SL Department of Neurology, Harvard Medical School, Children's Hospital Boston, MIT, Boston, Massachusetts 02115, USA. Gupta N N Department of Neurosurgery, University of California San Francisco, San Francisco, California 94143-0112, USA. Phillips J J Departments of Neurology, Pediatrics, and Neurosurgery, University of California, San Francisco, The Helen Diller Family Cancer Research Building, San Francisco, California 94158, USA. Huang A A 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Department of Neuro-oncology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada. Tabori U U 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Department of Neuro-oncology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada. Hawkins C C 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Malkin D D Department of Haematology and Oncology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada. Kongkham P N PN 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada [3] Division of Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Weiss W A WA Departments of Neurology, Pediatrics, and Neurosurgery, University of California, San Francisco, The Helen Diller Family Cancer Research Building, San Francisco, California 94158, USA. Jabado N N Departments of Pediatrics and Human Genetics, McGill University and the McGill University Health Center Research Institute, Montreal, Quebec H3Z 2Z3, Canada. Rutka J T JT 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada [3] Division of Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Bouffet E E Department of Neuro-oncology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada. Korbel J O JO Genome Biology, European Molecular Biology, Laboratory Meyerhofstr. 1, Heidelberg 69117, Germany. Lupien M M 1] Ontario Cancer Institute, Princess Margaret Cancer Centre-University Health Network, Toronto, Ontario M5G 1L7, Canada [2] Ontario Institute for Cancer Research, Toronto, Ontario M5G 1L7, Canada [3] Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1X8, Canada. Aldape K D KD Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. Bader G D GD Department of Molecular Genetics, Banting and Best Department of Medical Research, The Donnelly Centre, University of Toronto, Toronto, Ontario M4N 1X8, Canada. Eils R R 1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany. Lichter P P 1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany. Dirks P B PB 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada [3] Division of Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada [4] Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada. Pfister S M SM 1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg 69120, Germany [3] German Cancer Consortium (DKTK), Heidelberg 69120, Germany. Korshunov A A 1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] University of Michigan Cell and Developmental Biology, Ann Arbor, Michigan 48109-2200, USA [3] CCU Neuropathology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany. Taylor M D MD 1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada [3] Division of Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada. eng GEO GSE43353 P30 CA016672 CA NCI NIH HHS United States P50 CA097257 CA NCI NIH HHS United States R01 CA121941 CA NCI NIH HHS United States R01 CA148621 CA NCI NIH HHS United States R01 CA163737 CA NCI NIH HHS United States R01CA148699 CA NCI NIH HHS United States R01CA159859 CA NCI NIH HHS United States Canadian Institutes of Health Research Canada Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2014 02 19

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1932-6203 8 12 2013 PLoS ONE Coordinate microRNA-mediated regulation of protein complexes in prostate cancer. e84261 10.1371/journal.pone.0084261 MicroRNAs are a class of small non-coding regulatory RNA molecules that regulate mRNAs post-transcriptionally. Recent evidence has shown that miRNAs target entire functionally related proteins such as protein complexes and biological pathways. However, characterizing the influence of miRNAs on genes whose encoded proteins are part of protein complexes has not been studied in the context of disease. We propose an entropy-based framework to identify miRNA-mediated dysregulation of functionally related proteins during prostate cancer progression. The proposed framework uses experimentally verified miRNA-target interactions, functionally related proteins and expression data to identify miRNA-influenced protein complexes in prostate cancer, and identify genes that are dysregulated as a result. The framework constructs correlation matrixes between functionally related proteins and miRNAs that have targets in the complex, and assesses the changes in the Shannon entropy of the modules across different stages of prostate cancer. Results reveal that SMAD4 and HDAC containing protein complexes are highly affected and disrupted by miRNAs, particularly miRNA-1 and miRNA-16. Using biological pathways to define functionally related proteins reveals that NF-kB-, RAS-, and Syndecan-mediated pathways are dysregulated due to miRNA-1- and miRNA-16-mediated regulation. These results suggest that miRNA-1 and miRNA-16 are important master regulators of miRNA-mediated regulation in prostate cancer. Moreover, results reveal that miRNAs with high-influence on the disrupted protein complexes are diagnostic and prognostic biomarker candidates for prostate cancer progression. The observation of miRNA-mediated protein complex regulation and miRNA-mediated pathway regulation, with partial experimental verification from previous studies, demonstrates that our framework is a promising approach for the identification of novel miRNAs and protein complexes related to disease progression. Alshalalfa Mohammed M Department of Computer Science, University of Calgary, Calgary, Alberta, Canada ; Biotechnology Research Centre, Palestine Polytechnic University, Hebron, Palestine. Bader Gary D GD The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada and the Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. Bismar Tarek A TA Departments of Pathology, Oncology and Molecular Biology and Biochemistry, Faculty of Medicine, University of Calgary, Alberta, Canada. Alhajj Reda R Department of Computer Science, University of Calgary, Calgary, Alberta, Canada. eng P41 GM103504 GM NIGMS NIH HHS United States P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2013 12 31

United States PLoS One 101285081 1932-6203 0 MIRN1 microRNA, human 0 MIRN16 microRNA, human 0 MicroRNAs 0 Multiprotein Complexes 0 SMAD4 protein, human 0 Smad4 Protein 0 Tumor Markers, Biological EC 3.5.1.98 HDAC1 protein, human EC 3.5.1.98 Histone Deacetylase 1 IM Mol Cancer Ther. 2007 May;6(5):1483-91 17483436 Cancer Res. 2007 Jul 1;67(13):6130-5 17616669 RNA. 2007 Sep;13(9):1402-8 17652130 Nat Methods. 2007 Dec;4(12):1045-9 18026111 Nucleic Acids Res. 2008 Jan;36(Database issue):D646-50 17965090 Nucleic Acids Res. 2008 Jan;36(Database issue):D154-8 17991681 Oncogene. 2008 Mar 13;27(12):1788-93 17891175 Proteomics. 2008 May;8(10):1975-9 18491312 Nucleic Acids Res. 2009 Jan;37(Database issue):D105-10 18996891 PLoS One. 2011;6(3):e17911 21445301 Biosystems. 2011 Sep;105(3):201-9 21524683 BMC Syst Biol. 2011;5:136 21867514 Mol Cancer Ther. 2011 Oct;10(10):1857-66 21768329 Cell. 2011 Oct 14;147(2):370-81 22000015 BMC Syst Biol. 2011;5:183 22050994 Int J Cancer. 2012 Feb 1;130(3):611-21 21400514 Bioinformatics. 2012 Feb 15;28(4):453-6 22180412 Oncogene. 2012 Feb 23;31(8):978-91 21765474 Nucleic Acids Res. 2012 Apr;40(8):3689-703 22210864 PLoS One. 2012;7(6):e40130 22768240 Clin Cancer Res. 2012 Aug 15;18(16):4291-302 22723371 Clin Cancer Res. 2012 Sep 1;18(17):4691-701 22811583 Proc Natl Acad Sci U S A. 2000 Aug 29;97(18):10101-6 10963673 APMIS. 2004 Feb;112(2):89-97 15056224 J Clin Oncol. 2004 Jul 15;22(14):2790-9 15254046 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 Cancer Cell. 2005 Nov;8(5):393-406 16286247 Breast Cancer Res. 2005;7(6):R953-64 16280042 Nat Rev Cancer. 2006 Apr;6(4):259-69 16557279 Bioinformatics. 2006 Jul 15;22(14):e507-13 16873514 Dev Biol. 2007 Feb 1;302(1):1-12 16989803 BMC Bioinformatics. 2007;8:111 17397530 Nat Protoc. 2009;4(1):44-57 19131956 Epigenetics. 2008 Nov;3(6):300-9 19029799 Am J Pathol. 2009 Aug;175(2):489-99 19628766 Oncogene. 2008 Dec;27 Suppl 2:S52-7 19956180 Int J Cancer. 2010 Mar 1;126(5):1166-76 19676045 Int J Biol Sci. 2010;6(1):1-8 20087440 BMC Med Genomics. 2010;3:8 20233430 Acta Biochim Biophys Sin (Shanghai). 2010 Jun 15;42(6):363-9 20539944 BMC Bioinformatics. 2010;11:338 20565989 PLoS One. 2010;5(11):e13984 21085593 Int J Cancer. 2011 Feb 1;128(3):608-16 20473869 Nucleic Acids Res. 2011 Jan;39(Database issue):D163-9 21071411 Nature. 2011 Feb 10;470(7333):269-73 21289624 Entropy Gene Expression Profiling Gene Expression Regulation, Neoplastic genetics physiology Histone Deacetylase 1 metabolism Humans Male MicroRNAs metabolism Multiprotein Complexes metabolism Prostatic Neoplasms metabolism Signal Transduction genetics physiology Smad4 Protein metabolism Tumor Markers, Biological metabolism PMC3877262 2013 2013 5 6 2013 11 21 2013 12 31 2014 1 7 6 0 2014 1 7 6 0 2014 9 3 6 0 epublish 10.1371/journal.pone.0084261 PONE-D-13-18403 24391925 PMC3877262 24089029 2013 10 03

2045-2322 3 2013 Sci Rep The mutational landscape of phosphorylation signaling in cancer. 2651 10.1038/srep02651 Somatic mutations in cancer genomes include drivers that provide selective advantages to tumor cells and passengers present due to genome instability. Discovery of pan-cancer drivers will help characterize biological systems important in multiple cancers and lead to development of better therapies. Driver genes are most often identified by their recurrent mutations across tumor samples. However, some mutations are more important for protein function than others. Thus considering the location of mutations with respect to functional protein sites can predict their mechanisms of action and improve the sensitivity of driver gene detection. Protein phosphorylation is a post-translational modification central to cancer biology and treatment, and frequently altered by driver mutations. Here we used our ActiveDriver method to analyze known phosphorylation sites mutated by single nucleotide variants (SNVs) in The Cancer Genome Atlas Research Network (TCGA) pan-cancer dataset of 3,185 genomes and 12 cancer types. Phosphorylation-related SNVs (pSNVs) occur in ~90% of tumors, show increased conservation and functional mutation impact compared to other protein-coding mutations, and are enriched in cancer genes and pathways. Gene-centric analysis found 150 known and candidate cancer genes with significant pSNV recurrence. Using a novel computational method, we predict that 29% of these mutations directly abolish phosphorylation or modify kinase target sites to rewire signaling pathways. This analysis shows that incorporation of information about protein signaling sites will improve computational pipelines for variant function prediction. Reimand Jüri J The Donnelly Centre, University of Toronto, Canada. Wagih Omar O Bader Gary D GD eng GM103504 GM NIGMS NIH HHS United States MOP-84324 Canadian Institutes of Health Research Canada P41 GM103504 GM NIGMS NIH HHS United States P41 RR031228 RR NCRR NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2013 10 02

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2045-2322 3 2013 Sci Rep Comprehensive identification of mutational cancer driver genes across 12 tumor types. 2650 10.1038/srep02650 With the ability to fully sequence tumor genomes/exomes, the quest for cancer driver genes can now be undertaken in an unbiased manner. However, obtaining a complete catalog of cancer genes is difficult due to the heterogeneous molecular nature of the disease and the limitations of available computational methods. Here we show that the combination of complementary methods allows identifying a comprehensive and reliable list of cancer driver genes. We provide a list of 291 high-confidence cancer driver genes acting on 3,205 tumors from 12 different cancer types. Among those genes, some have not been previously identified as cancer drivers and 16 have clear preference to sustain mutations in one specific tumor type. The novel driver candidates complement our current picture of the emergence of these diseases. In summary, the catalog of driver genes and the methodology presented here open new avenues to better understand the mechanisms of tumorigenesis. Tamborero David D 1] Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, Barcelona, Spain [2]. Gonzalez-Perez Abel A Perez-Llamas Christian C Deu-Pons Jordi J Kandoth Cyriac C Reimand Jüri J Lawrence Michael S MS Getz Gad G Bader Gary D GD Ding Li L Lopez-Bigas Nuria N eng P41 GM103504 GM NIGMS NIH HHS United States P41 GM103504 GM NIGMS NIH HHS United States U01 HG006517 HG NHGRI NIH HHS United States U54 HG003079 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2013 10 02

England Sci Rep 101563288 2045-2322 0 Carcinogens IM Nature. 2013 Jul 11;499(7457):214-8 23770567 Cell. 2013 Mar 28;153(1):17-37 23540688 Bioinformatics. 2013 Sep 15;29(18):2238-44 23884480 Nat Genet. 2013 Oct;45(10):1113-20 24071849 Sci Rep. 2013;3:2651 24089029 Nat Methods. 2013 Nov;10(11):1081-2 24037244 Nucleic Acids Res. 2010 Jan;38(Database issue):D355-60 19880382 Nat Genet. 2000 May;25(1):25-9 10802651 Nature. 2000 Aug 24;406(6798):897-902 10972292 Clin Cancer Res. 2002 Dec;8(12):3824-31 12473596 Mol Cell Biol. 2003 Jan;23(2):437-49 12509444 Nucleic Acids Res. 2003 Jul 1;31(13):3812-4 12824425 Nat Rev Cancer. 2004 Mar;4(3):177-83 14993899 J Biol Chem. 2004 Apr 16;279(16):16433-40 14871897 Nature. 1982 Nov 11;300(5888):143-9 6290897 Nature. 1982 Nov 11;300(5888):149-52 7133135 J Cell Sci. 2005 Oct 15;118(Pt 20):4901-12 16219695 Oncogene. 2006 Mar 2;25(9):1391-9 16331276 Prostate. 2006 Jul 1;66(10):1013-28 16001449 Nature. 2007 Mar 8;446(7132):153-8 17344846 Front Biosci. 2008;13:71-84 17981529 Mol Cell Biol. 2008 Apr;28(8):2803-14 18285463 Proc Natl Acad Sci U S A. 2008 Sep 2;105(35):13057-62 18755892 Nature. 2008 Oct 23;455(7216):1061-8 18772890 Nature. 2009 Apr 9;458(7239):719-24 19360079 Exp Mol Med. 2009 Nov 30;41(11):765-71 19887899 Nat Methods. 2010 Apr;7(4):248-9 20354512 Nature. 2010 Apr 15;464(7291):993-8 20393554 Prostate. 2010 May 15;70(7):755-64 20058239 Genome Biol. 2010;11(5):R53 20482850 Nucleic Acids Res. 2011 Jan;39(Database issue):D685-90 21071392 Mol Cell Biol. 2011 May;31(10):2122-33 21383062 PLoS One. 2011;6(5):e19541 21602921 Nucleic Acids Res. 2011 Sep 1;39(17):e118 21727090 Genome Biol. 2011;12(4):R41 21527027 Biosci Rep. 2012 Apr 1;32(2):113-30 22115363 Mol Cell. 2012 Jan 27;45(2):171-84 22196886 Genes Chromosomes Cancer. 2012 Jun;51(6):618-29 22383183 Cell. 2012 Jul 20;150(2):251-63 22817889 Genome Res. 2012 Aug;22(8):1589-98 22759861 PLoS One. 2012;7(8):e42456 22879989 Nucleic Acids Res. 2012 Nov;40(21):e169 22904074 Mol Syst Biol. 2013;9:637 23340843 PLoS One. 2013;8(2):e55489 23408991 Proc Natl Acad Sci U S A. 2013 Mar 5;110(10):3931-6 23417300 Science. 2013 Mar 29;339(6127):1546-58 23539594 Nat Methods. 2013 Aug;10(8):723-9 23900255 Carcinogens Cell Transformation, Neoplastic genetics metabolism DNA Mutational Analysis methods Gene Regulatory Networks Genetic Association Studies Genomics methods Humans Mutation Neoplasms genetics metabolism Reproducibility of Results Signal Transduction PMC3788361 2013 6 27 2013 8 23 2013 10 3 6 0 2013 10 3 6 0 2014 7 7 6 0 epublish srep02650 10.1038/srep02650 24084849 PMC3788361 24068901 2013 09 26 2014 04 21 2014 11 12

1553-7358 9 9 2013 PLoS Comput. Biol. Using biological pathway data with paxtools. e1003194 10.1371/journal.pcbi.1003194 A rapidly growing corpus of formal, computable pathway information can be used to answer important biological questions including finding non-trivial connections between cellular processes, identifying significantly altered portions of the cellular network in a disease state and building predictive models that can be used for precision medicine. Due to its complexity and fragmented nature, however, working with pathway data is still difficult. We present Paxtools, a Java library that contains algorithms, software components and converters for biological pathways represented in the standard BioPAX language. Paxtools allows scientists to focus on their scientific problem by removing technical barriers to access and analyse pathway information. Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in Software S1. A detailed manual for obtaining and using Paxtools can be found in Protocol S1. The latest sources and release bundles can be obtained from biopax.org/paxtools. Demir Emek E Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America. Babur Ozgün O Rodchenkov Igor I Aksoy Bülent Arman BA Fukuda Ken I KI Gross Benjamin B Sümer Onur Selçuk OS Bader Gary D GD Sander Chris C eng 5U41 HG006623-02 HG NHGRI NIH HHS United States U41 HG006623 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural 2013 09 19

United States PLoS Comput Biol 101238922 1553-734X IM Nucleic Acids Res. 2009 Jul;37(Web Server issue):W305-11 19465376 J Biol Chem. 2009 Feb 27;284(9):5451-5 18940806 Nucleic Acids Res. 2010 Jan;38(Database issue):D473-9 19850718 Nucleic Acids Res. 2010 Jan;38(Database issue):D204-10 20015972 Bioinformatics. 2010 Feb 1;26(3):429-31 20007251 Bioinformatics. 2010 Jun 15;26(12):i237-45 20529912 BMC Syst Biol. 2010;4:92 20587024 Bioinformatics. 2010 Sep 15;26(18):2340-1 20628075 PLoS Biol. 2010;8(8). pii: e1000472. doi: 10.1371/journal.pbio.1000472 20824171 Nat Biotechnol. 2010 Sep;28(9):935-42 20829833 Nucleic Acids Res. 2010 Sep;38(17):5648-56 20466809 Nucleic Acids Res. 2011 Jan;39(Database issue):D691-7 21067998 Nucleic Acids Res. 2011 Jan;39(Database issue):D685-90 21071392 Bioinformatics. 2011 Dec 15;27(24):3437-8 22021903 Nucleic Acids Res. 2012 Jan;40(Database issue):D797-802 22123737 Nucleic Acids Res. 2012 Jan;40(Database issue):D261-70 22135298 Nucleic Acids Res. 2012 Jan;40(Database issue):D803-8 22140115 Genome Res. 2012 Feb;22(2):398-406 21908773 Cancer Discov. 2012 May;2(5):401-4 22588877 Bioinformatics. 2012 Aug 1;28(15):2016-21 22581176 BMC Syst Biol. 2012;6:29 22548703 Curr Opin Cell Biol. 2005 Feb;17(1):9-11 15661513 Nat Rev Drug Discov. 2005 Jan;4(1):45-58 15688072 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6 16381921 Nat Biotechnol. 2006 May;24(5):537-44 16680138 BMC Bioinformatics. 2006;7:497 17101041 Nat Rev Cancer. 2007 Jan;7(1):23-34 17167517 Mol Syst Biol. 2007;3:140 17940530 Nat Protoc. 2007;2(10):2366-82 17947979 BMC Biol. 2007;5:44 17925023 Science. 2008 Sep 26;321(5897):1801-6 18772397 Nature. 2008 Oct 23;455(7216):1061-8 18772890 Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9 18832364 BMC Bioinformatics. 2009;10:376 19917102 Algorithms Computational Biology methods Programming Languages PMC3777916 2013 9 2013 1 21 2013 6 25 2013 9 19 2013 9 27 6 0 2013 9 27 6 0 2014 4 22 6 0 ppublish 10.1371/journal.pcbi.1003194 PCOMPBIOL-D-13-00124 24068901 PMC3777916 23918249 2013 10 04 2014 04 07 2014 11 13

1367-4811 29 20 2013 Oct 15 Bioinformatics The BioPAX Validator. 2659-60 10.1093/bioinformatics/btt452 BioPAX is a community-developed standard language for biological pathway data. A key functionality required for efficient BioPAX data exchange is validation-detecting errors and inconsistencies in BioPAX documents. The BioPAX Validator is a command-line tool, Java library and online web service for BioPAX that performs >100 classes of consistency checks. The validator recognizes common syntactic errors and semantic inconsistencies and reports them in a customizable human readable format. It can also automatically fix some errors and normalize BioPAX data. Since its release, the validator has become a critical tool for the pathway informatics community, detecting thousands of errors and helping substantially increase the conformity and uniformity of BioPAX-formatted data. The BioPAX Validator is open source and released under LGPL v3 license. All sources, binaries and documentation can be found at sf.net/p/biopax, and the latest stable version of the web application is available at biopax.org/validator. Rodchenkov Igor I The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, USA. Demir Emek E Sander Chris C Bader Gary D GD eng 1U41HG006623 HG NHGRI NIH HHS United States U41 HG006623 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural 2013 08 05

England Bioinformatics 9808944 1367-4803 IM Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6 16381921 Bioinformatics. 2008 Mar 15;24(6):880-1 18252737 PLoS Comput Biol. 2013;9(9):e1003194 24068901 Bioinformatics. 2010 Sep 15;26(18):2340-1 20628075 Nat Biotechnol. 2010 Sep;28(9):935-42 20829833 Proteomics. 2009 Nov;9(22):5112-9 19834897 Documentation Internet Software PMC3789551 2013 8 5 2013 8 29 2013 8 7 6 0 2013 8 7 6 0 2014 4 8 6 0 ppublish btt452 10.1093/bioinformatics/btt452 23918249 PMC3789551 23900255 2013 07 31 2013 12 11 2014 11 13

1548-7105 10 8 2013 Aug Nat. Methods Computational approaches to identify functional genetic variants in cancer genomes. 723-9 10.1038/nmeth.2562 The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype. Gonzalez-Perez Abel A Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain. Mustonen Ville V Reva Boris B Ritchie Graham R S GR Creixell Pau P Karchin Rachel R Vazquez Miguel M Fink J Lynn JL Kassahn Karin S KS Pearson John V JV Bader Gary D GD Boutros Paul C PC 0000000305537520 Muthuswamy Lakshmi L Ouellette B F Francis BF Reimand Jüri J Linding Rune R 0000000304204839 Shibata Tatsuhiro T Valencia Alfonso A Butler Adam A Dronov Serge S Flicek Paul P Shannon Nick B NB Carter Hannah H Ding Li L Sander Chris C Stuart Josh M JM Stein Lincoln D LD Lopez-Bigas Nuria N International Cancer Genome Consortium Mutation Pathways and Consequences Subgroup of the Bioinformatics Analyses Working Group eng 095908 Wellcome Trust United Kingdom R01 CA180778 CA NCI NIH HHS United States U01 HG006517 HG NHGRI NIH HHS United States U54 HG003079 HG NHGRI NIH HHS United States Journal Article

United States Nat Methods 101215604 1548-7091 IM Mol Syst Biol. 2013;9:637 23340843 Nucleic Acids Res. 2012 Nov;40(21):e169 22904074 Science. 2013 Feb 22;339(6122):957-9 23348506 Bioinformatics. 2013 Mar 1;29(5):647-8 23325621 BMC Genomics. 2013;14 Suppl 3:S3 23819870 Nature. 2013 Jul 11;499(7457):214-8 23770567 Hum Genet. 2013 Nov;132(11):1235-43 23793516 Cell. 2000 Jan 7;100(1):57-70 10647931 Nucleic Acids Res. 2001 Jan 1;29(1):308-11 11125122 Nature. 2002 Jun 27;417(6892):949-54 12068308 Nat Rev Cancer. 2003 Jun;3(6):459-65 12778136 Nucleic Acids Res. 2003 Jul 1;31(13):3812-4 12824425 Nat Rev Cancer. 2004 Mar;4(3):177-83 14993899 Bioinformatics. 2004 May 1;20(7):1006-14 14751981 Genome Res. 2005 Jul;15(7):978-86 15965030 Science. 2006 Oct 13;314(5797):268-74 16959974 Science. 2007 Jan 12;315(5809):233-7 17218526 Nature. 2007 Mar 8;446(7132):153-8 17344846 Nucleic Acids Res. 2007 Jul;35(Web Server issue):W595-8 17537827 Nucleic Acids Res. 2008 Jan;36(Database issue):D102-6 18006571 Genome Biol. 2007;8(11):R232 17976239 Nat Genet. 2009 Apr;41(4):393-5 19287383 Nature. 2009 Apr 9;458(7239):719-24 19360079 Bioinformatics. 2009 Jun 1;25(11):1431-2 19369493 Nucleic Acids Res. 2009 May;37(9):e67 19339519 Science. 2009 Jun 26;324(5935):1720-3 19443739 Nat Protoc. 2009;4(7):1073-81 19561590 Nucleic Acids Res. 2009 Jul;37(Web Server issue):W202-8 19458158 Cancer Res. 2009 Aug 15;69(16):6660-7 19654296 BMC Bioinformatics. 2009;10:377 19919720 PLoS One. 2009;4(12):e8311 20011534 Nature. 2010 Jan 14;463(7278):191-6 20016485 Nature. 2010 Jan 14;463(7278):184-90 20016488 Nat Methods. 2010 Apr;7(4):248-9 20354512 Nature. 2010 Apr 15;464(7291):993-8 20393554 Genome Res. 2010 May;20(5):685-92 20194951 Bioinformatics. 2010 Aug 15;26(16):2069-70 20562413 Nucleic Acids Res. 2010 Sep;38(16):e164 20601685 Nucleic Acids Res. 2011 Jan;39(Database issue):D945-50 20952405 Cell. 2011 Mar 4;144(5):646-74 21376230 Hum Mutat. 2011 Apr;32(4):358-68 21412949 Am J Hum Genet. 2011 Apr 8;88(4):440-9 21457909 Cancer Res. 2011 Jul 1;71(13):4550-61 21555372 Bioinformatics. 2011 Aug 1;27(15):2147-8 21685053 Hum Mutat. 2011 Aug;32(8):894-9 21520341 Nucleic Acids Res. 2011 Sep 1;39(17):e118 21727090 Genomics. 2011 Oct;98(4):310-7 21763417 Nat Genet. 2012 Jan;44(1):47-52 22158541 Bioinformatics. 2012 Mar 1;28(5):724-5 22257670 Proc Natl Acad Sci U S A. 2012 Mar 6;109(10):3879-84 22343534 Fly (Austin). 2012 Apr-Jun;6(2):80-92 22728672 Nucleic Acids Res. 2012 Jul;40(Web Server issue):W54-8 22693211 Cell. 2012 Jul 20;150(2):251-63 22817889 Genome Res. 2012 Aug;22(8):1589-98 22759861 Bioinformatics. 2012 Sep 1;28(17):2267-9 22743228 Genome Res. 2012 Sep;22(9):1760-74 22955987 Genome Res. 2012 Sep;22(9):1790-7 22955989 Nature. 2012 Sep 6;489(7414):57-74 22955616 Bioinformatics. 2012 Sep 15;28(18):i640-i646 22962493 Nat Biotechnol. 2012 Sep;30(9):842-8 22965061 Science. 2012 Oct 12;338(6104):221 22923433 Nat Genet. 2012 Nov;44(11):1191-8 23001124 Nature. 2012 Nov 1;491(7422):56-65 23128226 Science. 2013 Feb 22;339(6122):959-61 23348503 Computational Biology methods Genetic Variation Genome, Human Humans Mutation Neoplasms genetics NIHMS543406 PMC3919555 2013 1 25 2013 6 07 2013 8 1 6 0 2013 8 1 6 0 2013 12 16 6 0 ppublish nmeth.2562 10.1038/nmeth.2562 23900255 PMC3919555 NIHMS543406 23794635 2013 06 24 2013 09 06 2014 11 13

1362-4962 41 Web Server issue 2013 Jul Nucleic Acids Res. GeneMANIA prediction server 2013 update. W115-22 10.1093/nar/gkt533 GeneMANIA (http://www.genemania.org) is a flexible user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query gene list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. GeneMANIA can also be used in a function prediction setting: given a query gene, GeneMANIA finds a small set of genes that are most likely to share function with that gene based on their interactions with it. Enriched Gene Ontology categories among this set can sometimes point to the function of the gene. Seven organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens, Rattus norvegicus and Saccharomyces cerevisiae), and hundreds of data sets have been collected from GEO, BioGRID, IRefIndex and I2D, as well as organism-specific functional genomics data sets. Users can customize their search by selecting specific data sets to query and by uploading their own data sets to analyze. Zuberi Khalid K The Donnelly Centre, University of Toronto, Ontario, Canada. Franz Max M Rodriguez Harold H Montojo Jason J Lopes Christian Tannus CT Bader Gary D GD Morris Quaid Q eng P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't

England Nucleic Acids Res 0411011 0305-1048 IM Nucleic Acids Res. 2012 Jan;40(Database issue):D306-12 22096229 Nucleic Acids Res. 2012 Jan;40(Database issue):D857-61 22096227 Nucleic Acids Res. 2012 Jan;40(Database issue):D700-5 22110037 Proteomics. 2012 May;12(10):1687-96 22589215 Nucleic Acids Res. 2012 Jul;40(Web Server issue):W484-90 22684505 Plant J. 2012 Sep;71(6):1038-50 22607031 Nucleic Acids Res. 2013 Jan;41(Database issue):D808-15 23203871 Nucleic Acids Res. 2013 Jan;41(Database issue):D816-23 23203989 PLoS One. 2012;7(12):e51947 23284828 Genome Biol. 2012;13(12):R125 23268829 Nucleic Acids Res. 2002 Jan 1;30(1):207-10 11752295 Genome Biol. 2005;6(13):R114 16420673 Genome Biol. 2008;9 Suppl 1:S2 18613946 Genome Biol. 2008;9 Suppl 1:S4 18613948 Curr Protoc Bioinformatics. 2008 Sep;Chapter 9:Unit 9.11 18819079 BMC Bioinformatics. 2008;9:405 18823568 Nat Protoc. 2009;4(1):44-57 19131956 Bioinformatics. 2010 Jan 1;26(1):111-9 19850753 Genome Biol. 2009;10(11):R130 19919682 Nucleic Acids Res. 2010 Jul;38(Web Server issue):W214-20 20576703 Bioinformatics. 2010 Jul 15;26(14):1806-7 20495000 Bioinformatics. 2010 Jul 15;26(14):1759-65 20507895 Genome Biol. 2010;11(5):R53 20482850 Bioinformatics. 2010 Sep 15;26(18):2347-8 20656902 Bioinformatics. 2010 Nov 15;26(22):2927-8 20926419 PLoS One. 2010;5(11):e13984 21085593 PLoS One. 2011;6(2):e17258 21364756 Bioinformatics. 2011 Jun 15;27(12):1739-40 21546393 Bioinformatics. 2011 Jul 1;27(13):1882-3 21561920 Nat Methods. 2011 Jul;8(7):528-9 21716279 Genome Res. 2011 Jul;21(7):1109-21 21536720 Methods Mol Biol. 2011;781:399-414 21877293 Nucleic Acids Res. 2011 Nov 1;39(20):8677-88 21785136 Nucleic Acids Res. 2012 Jan;40(Database issue):D735-41 22067452 Nucleic Acids Res. 2012 Jan;40(Database issue):D821-8 22110034 Algorithms Animals Gene Regulatory Networks Genes Humans Internet Mice Rats Software PMC3692113 2013 6 25 6 0 2013 6 26 6 0 2013 9 7 6 0 ppublish gkt533 10.1093/nar/gkt533 23794635 PMC3692113 23595664 2013 05 16 2013 11 04 2014 11 16

1367-4811 29 10 2013 May 15 Bioinformatics Cytoscape app store. 1350-1 10.1093/bioinformatics/btt138 Cytoscape is an open source software tool for biological network visualization and analysis, which can be extended with independently developed apps. We launched the Cytoscape App Store to highlight the important features that apps add to Cytoscape, enable researchers to find and install apps they need and help developers promote their apps. The App Store is available at http://apps.cytoscape.org. apico@gladstone.ucsf.edu. Lotia Samad S Gladstone Institutes, San Francisco, CA 94158, USA. Montojo Jason J Dong Yue Y Bader Gary D GD Pico Alexander R AR eng P41 GM103504 GM NIGMS NIH HHS United States P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2013 04 16

England Bioinformatics 9808944 1367-4803 IM Genome Res. 2003 Nov;13(11):2498-504 14597658 Bioinformatics. 2009 Apr 15;25(8):1091-3 19237447 Nat Protoc. 2007;2(10):2366-82 17947979 Computational Biology instrumentation Internet Metabolic Networks and Pathways Software PMC3654709 2013 4 16 2013 4 19 6 0 2013 4 19 6 0 2013 11 5 6 0 ppublish btt138 10.1093/bioinformatics/btt138 23595664 PMC3654709 23549480 2013 04 03 2013 09 13 2014 11 16

1744-4292 9 2013 Mol. Syst. Biol. SH3 interactome conserves general function over specific form. 652 10.1038/msb.2013.9 Src homology 3 (SH3) domains bind peptides to mediate protein-protein interactions that assemble and regulate dynamic biological processes. We surveyed the repertoire of SH3 binding specificity using peptide phage display in a metazoan, the worm Caenorhabditis elegans, and discovered that it structurally mirrors that of the budding yeast Saccharomyces cerevisiae. We then mapped the worm SH3 interactome using stringent yeast two-hybrid and compared it with the equivalent map for yeast. We found that the worm SH3 interactome resembles the analogous yeast network because it is significantly enriched for proteins with roles in endocytosis. Nevertheless, orthologous SH3 domain-mediated interactions are highly rewired. Our results suggest a model of network evolution where general function of the SH3 domain network is conserved over its specific form. Xin Xiaofeng X The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. Gfeller David D Cheng Jackie J Tonikian Raffi R Sun Lin L Guo Ailan A Lopez Lianet L Pavlenco Alevtina A Akintobi Adenrele A Zhang Yingnan Y Rual Jean-François JF Currell Bridget B Seshagiri Somasekar S Hao Tong T Yang Xinping X Shen Yun A YA Salehi-Ashtiani Kourosh K Li Jingjing J Cheng Aaron T AT Bouamalay Dryden D Lugari Adrien A Hill David E DE Grimes Mark L ML Drubin David G DG Grant Barth D BD Vidal Marc M Boone Charles C Sidhu Sachdev S SS Bader Gary D GD eng MOP-84324 Canadian Institutes of Health Research Canada MOP-93725 Canadian Institutes of Health Research Canada P41 GM103504 GM NIGMS NIH HHS United States R01 GM065462 GM NIGMS NIH HHS United States R01 GM067237 GM NIGMS NIH HHS United States R01 GM067237 GM NIGMS NIH HHS United States R01 GM65462 GM NIGMS NIH HHS United States R01 HG001715 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

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1932-6203 8 3 2013 PLoS ONE Metabolic adaptation to chronic inhibition of mitochondrial protein synthesis in acute myeloid leukemia cells. e58367 10.1371/journal.pone.0058367 Recently, we demonstrated that the anti-bacterial agent tigecycline preferentially induces death in leukemia cells through the inhibition of mitochondrial protein synthesis. Here, we sought to understand mechanisms of resistance to tigecycline by establishing a leukemia cell line resistant to the drug. TEX leukemia cells were treated with increasing concentrations of tigecycline over 4 months and a population of cells resistant to tigecycline (RTEX+TIG) was selected. Compared to wild type cells, RTEX+TIG cells had undetectable levels of mitochondrially translated proteins Cox-1 and Cox-2, reduced oxygen consumption and increased rates of glycolysis. Moreover, RTEX+TIG cells were more sensitive to inhibitors of glycolysis and more resistant to hypoxia. By electron microscopy, RTEX+TIG cells had abnormally swollen mitochondria with irregular cristae structures. RNA sequencing demonstrated a significant over-representation of genes with binding sites for the HIF1α:HIF1β transcription factor complex in their promoters. Upregulation of HIF1α mRNA and protein in RTEX+TIG cells was confirmed by Q-RTPCR and immunoblotting. Strikingly, upon removal of tigecycline from RTEX+TIG cells, the cells re-established aerobic metabolism. Levels of Cox-1 and Cox-2, oxygen consumption, glycolysis, mitochondrial mass and mitochondrial membrane potential returned to wild type levels, but HIF1α remained elevated. However, upon re-treatment with tigecycline for 72 hours, the glycolytic phenotype was re-established. Thus, we have generated cells with a reversible metabolic phenotype by chronic treatment with an inhibitor of mitochondrial protein synthesis. These cells will provide insight into cellular adaptations used to cope with metabolic stress. Jhas Bozhena B The Princess Margaret Hospital and The Ontario Cancer Institute, University Health Network, Toronto, Canada. Sriskanthadevan Shrivani S Skrtic Marko M Sukhai Mahadeo A MA Voisin Veronique V Jitkova Yulia Y Gronda Marcela M Hurren Rose R Laister Rob C RC Bader Gary D GD Minden Mark D MD Schimmer Aaron D AD eng 1R01CA157456 CA NCI NIH HHS United States GM103504 GM NIGMS NIH HHS United States P41 GM103504 GM NIGMS NIH HHS United States R01 CA121941 CA NCI NIH HHS United States R01 CA157456 CA NCI NIH HHS United States Canadian Institutes of Health Research Canada Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2013 03 08

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1744-4292 9 2013 Mol. Syst. Biol. Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. 637 10.1038/msb.2012.68 Large-scale cancer genome sequencing has uncovered thousands of gene mutations, but distinguishing tumor driver genes from functionally neutral passenger mutations is a major challenge. We analyzed 800 cancer genomes of eight types to find single-nucleotide variants (SNVs) that precisely target phosphorylation machinery, important in cancer development and drug targeting. Assuming that cancer-related biological systems involve unexpectedly frequent mutations, we used novel algorithms to identify genes with significant phosphorylation-associated SNVs (pSNVs), phospho-mutated pathways, kinase networks, drug targets, and clinically correlated signaling modules. We highlight increased survival of patients with TP53 pSNVs, hierarchically organized cancer kinase modules, a novel pSNV in EGFR, and an immune-related network of pSNVs that correlates with prolonged survival in ovarian cancer. Our findings include multiple actionable cancer gene candidates (FLNB, GRM1, POU2F1), protein complexes (HCF1, ASF1), and kinases (PRKCZ). This study demonstrates new ways of interpreting cancer genomes and presents new leads for cancer research. Reimand Jüri J The Donnelly Centre, University of Toronto, Toronto, Canada. Juri.Reimand@utoronto.ca Bader Gary D GD eng GM103504 GM NIGMS NIH HHS United States MOP-84324 Canadian Institutes of Health Research Canada P41 GM103504 GM NIGMS NIH HHS United States P41 RR031228 RR NCRR NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

England Mol Syst Biol 101235389 1744-4292 0 Contractile Proteins 0 FLNB protein, human 0 Filamins 0 Microfilament Proteins 0 Octamer Transcription Factor-1 0 POU2F1 protein, human 0 Proteins EC 2.7.- Protein Kinases EC 2.7.10.1 EGFR protein, human EC 2.7.10.1 Receptor, Epidermal Growth Factor EC 2.7.11.1 protein kinase C zeta EC 2.7.11.13 Protein Kinase C IM Bioinformatics. 2004 Sep 1;20(13):2138-9 15044227 Science. 1991 Dec 20;254(5039):1814-6 1684878 Science. 1997 Mar 21;275(5307):1787-90 9065402 Curr Opin Microbiol. 2004 Dec;7(6):638-46 15556037 Nature. 2010 Oct 28;467(7319):1061-73 20981092 Nucleic Acids Res. 2011 Jan;39(Database issue):D1035-41 21059682 Nucleic Acids Res. 2011 Jan;39(Database issue):D261-7 21062810 J Biol Chem. 2011 Jan 21;286(3):2236-44 20959462 Cell. 2011 Mar 4;144(5):646-74 21376230 Nat Genet. 2011 May;43(5):464-9 21499249 N Engl J Med. 2011 Jun 16;364(24):2305-15 21663470 N Engl J Med. 2011 Jun 30;364(26):2507-16 21639808 Nucleic Acids Res. 2011 Jul;39(Web Server issue):W307-15 21646343 Mol Cell. 2005 Jan 21;17(2):301-11 15664198 Nat Rev Cancer. 2005 May;5(5):341-54 15864276 Blood. 2005 Dec 1;106(12):3747-54 16109776 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D108-10 16381825 EMBO J. 2006 Jun 7;25(11):2615-22 16757976 Science. 2006 Oct 13;314(5797):268-74 16959974 Nucleic Acids Res. 2007 Jan;35(Database issue):D721-6 17088289 Eur J Hum Genet. 2007 Nov;15(11):1176-82 17609672 Science. 2007 Nov 16;318(5853):1108-13 17932254 Nucleic Acids Res. 2008 Jan;36(Database issue):D154-8 17991681 J Proteome Res. 2008 May;7(5):2140-50 18452278 Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W452-9 18460544 Proc Natl Acad Sci U S A. 2008 Aug 5;105(31):10762-7 18669648 Science. 2008 Sep 26;321(5897):1807-12 18772396 Science. 2008 Sep 26;321(5897):1801-6 18772397 Nature. 2008 Oct 23;455(7216):1061-8 18772890 Nature. 2008 Oct 23;455(7216):1069-75 18948947 Nucleic Acids Res. 2009 Jan;37(Database issue):D619-22 18981052 Nucleic Acids Res. 2009 Jan;37(Database issue):D767-72 18988627 Eur J Cancer. 2009 Feb;45(3):321-7 19114024 Nat Rev Genet. 2009 Apr;10(4):252-63 19274049 Proteomics. 2009 May;9(10):2861-74 19415658 Genome Res. 2009 Jul;19(7):1316-23 19498102 J Mol Biol. 2009 Jul 31;390(5):1030-47 19505475 Sci Signal. 2009;2(81):ra39 19638616 Cell Stem Cell. 2009 Aug 7;5(2):214-26 19664995 PLoS One. 2009;4(11):e7830 19915675 Nucleic Acids Res. 2010 Jan;38(Database issue):D497-501 19884131 Nucleic Acids Res. 2010 Jan;38(Database issue):D652-7 19906727 Sci Signal. 2010;3(104):ra3 20068231 J Biol Chem. 2010 Apr 2;285(14):10748-60 20110358 Nat Rev Cancer. 2007 Mar;7(3):169-81 17318210 Sci Am. 2007 Mar;296(3):50-7 17348159 Oncogene. 2007 Jun 7;26(27):3980-8 17213819 Hum Mol Genet. 2007 Jul 15;16(14):1661-75 17510210 Mol Syst Biol. 2007;3:140 17940530 Nature. 2010 Apr 15;464(7291):993-8 20393554 Clin Genet. 2010 Jun;77(6):525-34 20412080 J Biol Chem. 2010 Jul 9;285(28):21446-57 20452982 Curr Opin Struct Biol. 2010 Jun;20(3):342-50 20399638 Cell. 2010 Sep 3;142(5):661-7 20813250 Nature. 2011 Jun 30;474(7353):609-15 21720365 Nature. 2011 Jul 7;475(7354):101-5 21642962 Nucleic Acids Res. 2012 Jan;40(Database issue):D242-51 22110040 Nucleic Acids Res. 2012 Jan;40(Database issue):D261-70 22135298 Mol Endocrinol. 2012 Jan;26(1):203-17 22074951 Nat Biotechnol. 2012 Feb;30(2):159-64 22252508 Science. 2012 Jun 1;336(6085):1150-3 22582013 Nature. 2012 Jun 21;486(7403):405-9 22722202 FEBS Lett. 2012 Aug 14;586(17):2751-63 22561014 Proc Natl Acad Sci U S A. 1999 Nov 23;96(24):13777-82 10570149 J Biol Chem. 2000 Jan 28;275(4):2721-6 10644735 Mutat Res. 2000 Apr;462(2-3):247-53 10767636 Nat Genet. 2000 May;25(1):25-9 10802651 Proc Natl Acad Sci U S A. 2000 May 23;97(11):6185-90 10823959 EMBO J. 2001 Apr 2;20(7):1630-9 11285227 J Biol Chem. 2002 May 17;277(20):17901-5 11834740 Nat Rev Cancer. 2002 May;2(5):331-41 12044009 Genes Dev. 2003 Apr 1;17(7):896-911 12670868 Science. 2003 Apr 18;300(5618):445-52 12702867 Nat Genet. 2003 May;34(1):108-12 12704387 Nat Rev Cancer. 2004 Mar;4(3):177-83 14993899 Trends Genet. 2004 Apr;20(4):214-20 15041176 Nat Med. 2004 Aug;10(8):789-99 15286780 Algorithms Contractile Proteins genetics metabolism Female Filamins Genes, p53 Glioblastoma genetics Humans Microfilament Proteins genetics metabolism Models, Statistical Mutation Neoplasms genetics mortality Octamer Transcription Factor-1 genetics metabolism Ovarian Neoplasms genetics mortality Phosphorylation Polymorphism, Single Nucleotide Predictive Value of Tests Protein Kinase C genetics metabolism Protein Kinases genetics metabolism Proteins genetics metabolism Receptor, Epidermal Growth Factor genetics metabolism Signal Transduction genetics PMC3564258 2012 5 04 2012 12 06 2013 1 24 6 0 2013 1 24 6 0 2013 9 17 6 0 ppublish msb201268 10.1038/msb.2012.68 23340843 PMC3564258 23336252 2013 03 20 2013 09 18 2014 11 04

1471-2105 14 2013 BMC Bioinformatics Predicting PDZ domain mediated protein interactions from structure. 27 10.1186/1471-2105-14-27 PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training-testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training-testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW. Hui Shirley S The Donnelly Centre, University of Toronto, Toronto, ON, Canada. Xing Xiang X Bader Gary D GD eng MOP-84324 Canadian Institutes of Health Research Canada P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't 2013 01 21

England BMC Bioinformatics 100965194 1471-2105 0 Ligands 0 Peptides 0 Proteome IM J Mol Biol. 2009 May 15;388(4):902-16 19324052 Adv Enzyme Regul. 2009;49(1):98-106 19534027 Mol Cells. 2009 Jun 30;27(6):629-34 19533032 Bioinformatics. 2009 Nov 1;25(21):2843-4 19759195 Nucleic Acids Res. 2010 Jan;38(Database issue):D525-31 19850723 Nucleic Acids Res. 2010 Jan;38(Database issue):D497-501 19884131 Nucleic Acids Res. 2010 Jan;38(Database issue):D532-9 19897547 BMC Bioinformatics. 2010;11:144 20298601 Adv Exp Med Biol. 2010;674:107-23 20549944 J Mol Biol. 2010 Sep 17;402(2):460-74 20654621 Database (Oxford). 2010;2010:baq023 20940177 BMC Bioinformatics. 2010;11:507 20939902 PLoS One. 2010;5(11):e13984 21085593 Mol Vis. 2010;16:2259-72 21139678 Nucleic Acids Res. 2011 Jan;39(Database issue):D561-8 21045058 Nucleic Acids Res. 2011 Jan;39(Database issue):D698-704 21071413 J Mol Model. 2011 Feb;17(2):315-24 20461427 Bioinformatics. 2011 Feb 1;27(3):383-90 21127034 Toxicol Sci. 2011 Mar;120 Suppl 1:S49-75 21059794 Proc Natl Acad Sci U S A. 2011 Aug 30;108(35):14560-5 21841138 PLoS Comput Biol. 2011 Oct;7(10):e1002195 22039361 PLoS One. 2011;6(11):e25376 22069443 J Invest Dermatol. 2012 Jan;132(1):226-36 21881583 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D411-4 16381900 J Clin Invest. 1999 Nov;104(10):1353-61 10562297 Nucleic Acids Res. 2000 Jan 1;28(1):235-42 10592235 Nat Genet. 2000 May;25(1):25-9 10802651 J Leukoc Biol. 2001 Apr;69(4):513-21 11310836 Nucleic Acids Res. 2003 Jan 1;31(1):248-50 12519993 J Biol Chem. 2003 Feb 28;278(9):7645-54 12446668 Science. 2003 Apr 18;300(5618):445-52 12702867 Genome Res. 2003 Nov;13(11):2498-504 14597658 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D449-51 14681454 J Pathol. 2004 Jul;203(3):831-8 15221943 Proc Natl Acad Sci U S A. 1987 Nov;84(21):7696-700 3499612 Pharmacol Ther. 1992;53(2):261-73 1641409 Science. 1997 Jan 3;275(5296):73-7 8974395 Bioinformatics. 1998;14(7):617-23 9730927 Mech Dev. 1999 Jan;80(1):101-6 10096067 J Cell Biochem. 2004 Dec 15;93(6):1115-33 15449317 Nat Rev Drug Discov. 2004 Dec;3(12):1047-56 15573103 Bioinformatics. 2005 May 15;21(10):2347-55 15728116 Bioinformatics. 2005 Aug 15;21(16):3448-9 15972284 Bioinformatics. 2006 Jan 15;22(2):195-201 16301204 Clin Sci (Lond). 2006 May;110(5):525-41 16597322 Curr Opin Struct Biol. 2006 Apr;16(2):178-82 16546376 J Thromb Haemost. 2006 May;4(5):932-9 16689737 Pathophysiol Haemost Thromb. 2006;35(1-2):162-5 16855365 J Biol Chem. 2006 Aug 4;281(31):22299-311 16737968 J Biol Chem. 2006 Aug 4;281(31):22312-20 16737969 Bioinformatics. 2006 Nov 1;22(21):2590-6 16945945 Protein Sci. 2007 Jun;16(6):1053-62 17473018 Science. 2007 Jul 20;317(5836):364-9 17641200 Biochemistry. 2007 Sep 25;46(38):10864-74 17713926 Bioinformatics. 2007 Nov 15;23(22):3110-2 17720983 Proteins. 2008 Apr;71(1):261-77 17932912 PLoS Comput Biol. 2008 Apr;4(4):e1000052 18389064 Nat Biotechnol. 2008 Sep;26(9):1041-5 18711339 PLoS Biol. 2008 Sep 30;6(9):e239 18828675 Oncogene. 2008 Oct 9;27(46):6044-55 18591935 BMC Bioinformatics. 2008;9:405 18823568 Nucleic Acids Res. 2009 Jan;37(Database issue):D690-7 19033362 Wound Repair Regen. 2008 Sep-Oct;16(5):585-601 19128254 J Struct Funct Genomics. 2009 Mar;10(1):1-8 19037750 BMC Bioinformatics. 2009;10:32 19166624 Nat Chem Biol. 2009 Apr;5(4):217-9 19252499 Curr Opin Struct Biol. 2009 Apr;19(2):145-55 19327982 Humans Ligands Models, Molecular PDZ Domains Peptides chemistry metabolism Protein Interaction Mapping methods Protein Interaction Maps Proteome chemistry metabolism Sequence Analysis, Protein Support Vector Machines PMC3602153 2012 2 15 2012 12 19 2013 1 21 2013 1 23 6 0 2013 1 23 6 0 2013 9 21 6 0 epublish 1471-2105-14-27 10.1186/1471-2105-14-27 23336252 PMC3602153 23324130 2013 01 28 2013 07 08 2014 11 04

1471-2261 13 2013 BMC Cardiovasc Disord Integrative pathway dissection of molecular mechanisms of moxLDL-induced vascular smooth muscle phenotype transformation. 4 10.1186/1471-2261-13-4 Atherosclerosis (AT) is a chronic inflammatory disease characterized by the accumulation of inflammatory cells, lipoproteins and fibrous tissue in the walls of arteries. AT is the primary cause of heart attacks and stroke and is the leading cause of death in Western countries. To date, the pathogenesis of AT is not well-defined. Studies have shown that the dedifferentiation of contractile and quiescent vascular smooth muscle cells (SMC) to the proliferative, migratory and synthetic phenotype in the intima is pivotal for the onset and progression of AT. To further delineate the mechanisms underlying the pathogenesis of AT, we analyzed the early molecular pathways and networks involved in the SMC phenotype transformation. Quiescent human coronary artery SMCs were treated with minimally-oxidized LDL (moxLDL), for 3 hours and 21 hours, respectively. Transcriptomic data was generated for both time-points using microarrays and was subjected to pathway analysis using Gene Set Enrichment Analysis, GeneMANIA and Ingenuity software tools. Gene expression heat maps and pathways enriched in differentially expressed genes were compared to identify functional biological themes to elucidate early and late molecular mechanisms of moxLDL-induced SMC dedifferentiation. Differentially expressed genes were found to be enriched in cholesterol biosynthesis, inflammatory cytokines, chemokines, growth factors, cell cycle control and myogenic contraction themes. These pathways are consistent with inflammatory responses, cell proliferation, migration and ECM production, which are characteristic of SMC dedifferentiation. Furthermore, up-regulation of cholesterol synthesis and dysregulation of cholesterol metabolism was observed in moxLDL-induced SMC. These observations are consistent with the accumulation of cholesterol and oxidized cholesterol esters, which induce proinflammatory reactions during atherogenesis. Our data implicate for the first time IL12, IFN-α, HGF, CSF3, and VEGF signaling in SMC phenotype transformation. GPCR signaling, HBP1 (repressor of cyclin D1 and CDKN1B), and ID2 and ZEB1 transcriptional regulators were also found to have important roles in SMC dedifferentiation. Several microRNAs were observed to regulate the SMC phenotype transformation via an interaction with IFN-γ pathway. Also, several "nexus" genes in complex networks, including components of the multi-subunit enzyme complex involved in the terminal stages of cholesterol synthesis, microRNAs (miR-203, miR-511, miR-590-3p, miR-346*/miR- 1207-5p/miR-4763-3p), GPCR proteins (GPR1, GPR64, GPRC5A, GPR171, GPR176, GPR32, GPR25, GPR124) and signal transduction pathways, were found to be regulated. The systems biology analysis of the in vitro model of moxLDL-induced VSMC phenotype transformation was associated with the regulation of several genes not previously implicated in SMC phenotype transformation. The identification of these potential candidate genes enable hypothesis generation and in vivo functional experimentation (such as gain and loss-of-function studies) to establish causality with the process of SMC phenotype transformation and atherogenesis. Karagiannis George S GS Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, and Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, ON, M5S 1A8, Canada. Weile Jochen J Bader Gary D GD Minta Joe J eng P41 GM103504 GM NIGMS NIH HHS United States P41 RR031228 RR NCRR NIH HHS United States P41HG04118 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2013 01 16

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LDL metabolism Muscle, Smooth, Vascular metabolism pathology Myocytes, Smooth Muscle metabolism Oligonucleotide Array Sequence Analysis Phenotype Systems Biology Time Factors PMC3556327 2012 2 23 2012 12 29 2013 1 16 2013 1 18 6 0 2013 1 18 6 0 2013 7 9 6 0 epublish 1471-2261-13-4 10.1186/1471-2261-13-4 23324130 PMC3556327 23292740 2013 02 13 2013 08 12 2014 11 04

1367-4811 29 4 2013 Feb 15 Bioinformatics GESTODIFFERENT: a Cytoscape plugin for the generation and the identification of gene regulatory networks describing a stochastic cell differentiation process. 513-4 10.1093/bioinformatics/bts726 The characterization of the complex phenomenon of cell differentiation is a key goal of both systems and computational biology. GeStoDifferent is a Cytoscape plugin aimed at the generation and the identification of gene regulatory networks (GRNs) describing an arbitrary stochastic cell differentiation process. The (dynamical) model adopted to describe general GRNs is that of noisy random Boolean networks (NRBNs), with a specific focus on their emergent dynamical behavior. GeStoDifferent explores the space of GRNs by filtering the NRBN instances inconsistent with a stochastic lineage differentiation tree representing the cell lineages that can be obtained by following the fate of a stem cell descendant. Matched networks can then be analyzed by Cytoscape network analysis algorithms or, for instance, used to define (multiscale) models of cellular dynamics. Freely available at http://bimib.disco.unimib.it/index.php/Retronet#GESTODifferent or at the Cytoscape App Store http://apps.cytoscape.org/. Antoniotti Marco M Department of Informatics, Systems and Communication, University of Milan Bicocca, Viale Sarca 336, 20126, Milano, Italy. marco.antoniotti@unimib.it Bader Gary D GD Caravagna Giulio G Crippa Silvia S Graudenzi Alex A Mauri Giancarlo G eng P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't 2013 01 03

England Bioinformatics 9808944 1367-4803 IM J Theor Biol. 1969 Mar;22(3):437-67 5803332 PLoS One. 2008;3(8):e2922 18698344 PLoS One. 2011;6(3):e17703 21464974 Bioinformatics. 2011 Feb 1;27(3):431-2 21149340 Nature. 2009 Jul 2;460(7251):49-52 19571877 Cell Differentiation genetics Cell Lineage Gene Regulatory Networks Models, Genetic Software Stochastic Processes PMC3888149 2013 1 3 2013 1 25 2013 1 8 6 0 2013 1 8 6 0 2013 8 13 6 0 ppublish bts726 10.1093/bioinformatics/bts726 23292740 PMC3888149 23259511 2013 01 04 2013 06 07

1535-3907 12 1 2013 Jan 4 J. Proteome Res. The biology/disease-driven human proteome project (B/D-HPP): enabling protein research for the life sciences community. 23-7 10.1021/pr301151m The biology and disease oriented branch of the Human Proteome Project (B/D-HPP) was established by the Human Proteome Organization (HUPO) with the main goal of supporting the broad application of state-of the-art measurements of proteins and proteomes by life scientists studying the molecular mechanisms of biological processes and human disease. This will be accomplished through the generation of research and informational resources that will support the routine and definitive measurement of the process or disease relevant proteins. The B/D-HPP is highly complementary to the C-HPP and will provide datasets and biological characterization useful to the C-HPP teams. In this manuscript we describe the goals, the plans, and the current status of the of the B/D-HPP. Aebersold Ruedi R Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. aebersold@imsb.biol.ethz.ch Bader Gary D GD Edwards Aled M AM van Eyk Jennifer E JE Kussmann Martin M Qin Jun J Omenn Gilbert S GS eng Journal Article Research Support, Non-U.S. Gov't 2012 12 21

United States J Proteome Res 101128775 1535-3893 0 Proteome IM Biological Science Disciplines Disease classification genetics Gene Expression Genome, Human Human Genome Project Humans Mass Spectrometry Proteome genetics metabolism 2012 12 21 2012 12 25 6 0 2012 12 25 6 0 2013 6 8 6 0 ppublish 10.1021/pr301151m 23259511 23240084 2012 12 14 2015 05 14

2050-084X 1 2012 Elife Chromatin is an ancient innovation conserved between Archaea and Eukarya. e00078 10.7554/eLife.00078 The eukaryotic nucleosome is the fundamental unit of chromatin, comprising a protein octamer that wraps ∼147 bp of DNA and has essential roles in DNA compaction, replication and gene expression. Nucleosomes and chromatin have historically been considered to be unique to eukaryotes, yet studies of select archaea have identified homologs of histone proteins that assemble into tetrameric nucleosomes. Here we report the first archaeal genome-wide nucleosome occupancy map, as observed in the halophile Haloferax volcanii. Nucleosome occupancy was compared with gene expression by compiling a comprehensive transcriptome of Hfx. volcanii. We found that archaeal transcripts possess hallmarks of eukaryotic chromatin structure: nucleosome-depleted regions at transcriptional start sites and conserved -1 and +1 promoter nucleosomes. Our observations demonstrate that histones and chromatin architecture evolved before the divergence of Archaea and Eukarya, suggesting that the fundamental role of chromatin in the regulation of gene expression is ancient.DOI:http://dx.doi.org/10.7554/eLife.00078.001. Ammar Ron R Department of Molecular Genetics , University of Toronto , Toronto , Canada ; Donnelly Centre , University of Toronto , Toronto , Canada. Torti Dax D Tsui Kyle K Gebbia Marinella M Durbic Tanja T Bader Gary D GD Giaever Guri G Nislow Corey C eng Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't 2012 12 13

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1875-9777 11 6 2012 Dec 7 Cell Stem Cell Attenuation of miR-126 activity expands HSC in vivo without exhaustion. 799-811 10.1016/j.stem.2012.09.001 S1934-5909(12)00537-1 Lifelong blood cell production is governed through the poorly understood integration of cell-intrinsic and -extrinsic control of hematopoietic stem cell (HSC) quiescence and activation. MicroRNAs (miRNAs) coordinately regulate multiple targets within signaling networks, making them attractive candidate HSC regulators. We report that miR-126, a miRNA expressed in HSC and early progenitors, plays a pivotal role in restraining cell-cycle progression of HSC in vitro and in vivo. miR-126 knockdown by using lentiviral sponges increased HSC proliferation without inducing exhaustion, resulting in expansion of mouse and human long-term repopulating HSC. Conversely, enforced miR-126 expression impaired cell-cycle entry, leading to progressively reduced hematopoietic contribution. In HSC/early progenitors, miR-126 regulates multiple targets within the PI3K/AKT/GSK3β pathway, attenuating signal transduction in response to extrinsic signals. These data establish that miR-126 sets a threshold for HSC activation and thus governs HSC pool size, demonstrating the importance of miRNA in the control of HSC function. Copyright © 2012 Elsevier Inc. All rights reserved. Lechman Eric R ER Campbell Family Institute, Ontario Cancer Institute, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada. Gentner Bernhard B van Galen Peter P Giustacchini Alice A Saini Massimo M Boccalatte Francesco E FE Hiramatsu Hidefumi H Restuccia Umberto U Bachi Angela A Voisin Veronique V Bader Gary D GD Dick John E JE Naldini Luigi L eng P41 GM103504 GM NIGMS NIH HHS United States TGT11D02 Telethon Italy Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't 2012 11 08

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1548-7105 9 11 2012 Nov Nat. Methods A travel guide to Cytoscape plugins. 1069-76 10.1038/nmeth.2212 Cytoscape is open-source software for integration, visualization and analysis of biological networks. It can be extended through Cytoscape plugins, enabling a broad community of scientists to contribute useful features. This growth has occurred organically through the independent efforts of diverse authors, yielding a powerful but heterogeneous set of tools. We present a travel guide to the world of plugins, covering the 152 publicly available plugins for Cytoscape 2.5-2.8. We also describe ongoing efforts to distribute, organize and maintain the quality of the collection. Saito Rintaro R Department of Medicine, University of California, San Diego, La Jolla, California, USA. Smoot Michael E ME Ono Keiichiro K Ruscheinski Johannes J Wang Peng-Liang PL Lotia Samad S Pico Alexander R AR Bader Gary D GD Ideker Trey T eng P41 GM103504 GM NIGMS NIH HHS United States P41 RR031228 RR NCRR NIH HHS United States P50 GM085764 GM NIGMS NIH HHS United States Journal Article Research Support, N.I.H., Extramural 2012 11 06

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1752-0509 6 2012 BMC Syst Biol Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study. 112 10.1186/1752-0509-6-112 The use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment. Our findings demonstrate that a miRNA's functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set. We describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment. Alshalalfa Mohammed M Department of Computer Science, University of Calgary, Calgary, AB, Canada. msalshal@ucalgary.ca Bader Gary D GD Goldenberg Anna A Morris Quaid Q Alhajj Reda R eng P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't 2012 08 28

England BMC Syst Biol 101301827 1752-0509 0 MicroRNAs 0 Tumor Markers, Biological IM Mol Cancer Ther. 2011 Oct;10(10):1857-66 21768329 PLoS One. 2011;6(9):e24950 21980368 Nucleic Acids Res. 2011 Nov 1;39(20):e136 21835775 Bioinformatics. 2011 Nov 15;27(22):3166-72 21965819 Int J Cancer. 2012 Feb 1;130(3):611-21 21400514 Mol Biosyst. 2012 Apr;8(5):1492-8 22362105 Nucleic Acids Res. 2012 Apr;40(8):3689-703 22210864 Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5116-21 11309499 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D497-501 14681466 Cell. 2003 Dec 26;115(7):787-98 14697198 Nat Genet. 2004 Jun;36(6):559-64 15167932 Nat Rev Genet. 2004 Jul;5(7):522-31 15211354 Nature. 2004 Sep 16;431(7006):350-5 15372042 J Biopharm Stat. 2004 Aug;14(3):701-21 15468760 Biosystems. 2006 Feb-Mar;83(2-3):81-90 16426740 Nat Rev Cancer. 2006 Apr;6(4):259-69 16557279 Mol Syst Biol. 2006;2:46 16969338 Nat Rev Cancer. 2006 Nov;6(11):857-66 17060945 Biochem Biophys Res Commun. 2007 Jan 19;352(3):733-8 17141185 Mol Cancer Ther. 2007 May;6(5):1483-91 17483436 Mol Cell. 2007 Jul 6;27(1):91-105 17612493 RNA. 2007 Sep;13(9):1402-8 17652130 Nat Genet. 2007 Oct;39(10):1278-84 17893677 Mol Syst Biol. 2007;3:140 17940530 Nat Methods. 2007 Dec;4(12):1045-9 18026111 Mol Syst Biol. 2007;3:152 18091723 Oncogene. 2008 Mar 13;27(12):1788-93 17891175 PLoS Genet. 2007 Dec;3(12):e215 18085825 Proteomics. 2008 May;8(10):1975-9 18491312 Genome Biol. 2008;9 Suppl 1:S4 18613948 PLoS One. 2008;3(10):e3420 18923704 Nucleic Acids Res. 2009 Jan;37(Database issue):D98-104 18927107 Nucleic Acids Res. 2009 Jan;37(Database issue):D105-10 18996891 J Biomed Inform. 2009 Aug;42(4):685-91 19535005 Bioinformatics. 2009 Aug 1;25(15):1991-3 19435746 Oncogene. 2008 Dec;27 Suppl 2:S52-7 19956180 Int J Cancer. 2010 Mar 1;126(5):1166-76 19676045 BMC Med Genomics. 2010;3:8 20233430 Mol Syst Biol. 2010 Apr 20;6:363 20404830 Acta Biochim Biophys Sin (Shanghai). 2010 Jun 15;42(6):363-9 20539944 Genome Biol. 2010;11(5):R53 20482850 Cancer Cell. 2010 Jul 13;18(1):11-22 20579941 Prostate Cancer Prostatic Dis. 2010 Sep;13(3):208-17 20585343 Nucleic Acids Res. 2010 Aug;38(15):e160 20576699 PLoS One. 2010;5(11):e13984 21085593 Nucleic Acids Res. 2011 Jan;39(Database issue):D163-9 21071411 Bioinformatics. 2011 Feb 1;27(3):431-2 21149340 BMC Med Genomics. 2011;4:8 21241464 Nat Commun. 2010;1:34 20975711 BMC Genomics. 2011;12:138 21375780 Nat Genet. 2011 Apr;43(4):288-9 21445071 Biosystems. 2011 Sep;105(3):201-9 21524683 Nat Genet. 2011 Sep;43(9):854-9 21857679 BMC Syst Biol. 2011;5:136 21867514 Cell. 2011 Oct 14;147(2):370-81 22000015 Computational Biology methods Gene Expression Regulation, Neoplastic Humans Male MicroRNAs genetics metabolism Prognosis Prostatic Neoplasms diagnosis genetics metabolism pathology Protein Interaction Mapping Protein Interaction Maps Recurrence Tumor Markers, Biological metabolism PMC3490713 2012 5 23 2012 8 14 2012 8 28 2012 8 30 6 0 2012 8 30 6 0 2013 2 21 6 0 epublish 1752-0509-6-112 10.1186/1752-0509-6-112 22929553 PMC3490713 22832581 2012 08 03 2012 09 04 2015 07 08

1476-4687 488 7409 2012 Aug 2 Nature Subgroup-specific structural variation across 1,000 medulloblastoma genomes. 49-56 10.1038/nature11327 Medulloblastoma, the most common malignant paediatric brain tumour, is currently treated with nonspecific cytotoxic therapies including surgery, whole-brain radiation, and aggressive chemotherapy. As medulloblastoma exhibits marked intertumoural heterogeneity, with at least four distinct molecular variants, previous attempts to identify targets for therapy have been underpowered because of small samples sizes. Here we report somatic copy number aberrations (SCNAs) in 1,087 unique medulloblastomas. SCNAs are common in medulloblastoma, and are predominantly subgroup-enriched. The most common region of focal copy number gain is a tandem duplication of SNCAIP, a gene associated with Parkinson's disease, which is exquisitely restricted to Group 4α. Recurrent translocations of PVT1, including PVT1-MYC and PVT1-NDRG1, that arise through chromothripsis are restricted to Group 3. Numerous targetable SCNAs, including recurrent events targeting TGF-β signalling in Group 3, and NF-κB signalling in Group 4, suggest future avenues for rational, targeted therapy. Northcott Paul A PA Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Shih David J H DJ Peacock John J Garzia Livia L Morrissy A Sorana AS Zichner Thomas T Stütz Adrian M AM Korshunov Andrey A Reimand Jüri J Schumacher Steven E SE Beroukhim Rameen R Ellison David W DW Marshall Christian R CR Lionel Anath C AC Mack Stephen S Dubuc Adrian A Yao Yuan Y Ramaswamy Vijay V Luu Betty B Rolider Adi A Cavalli Florence M G FM Wang Xin X Remke Marc M Wu Xiaochong X Chiu Readman Y B RY Chu Andy A Chuah Eric E Corbett Richard D RD Hoad Gemma R GR Jackman Shaun D SD Li Yisu Y Lo Allan A Mungall Karen L KL Nip Ka Ming KM Qian Jenny Q JQ Raymond Anthony G J AG Thiessen Nina T NT Varhol Richard J RJ Birol Inanc I Moore Richard A RA Mungall Andrew J AJ Holt Robert R Kawauchi Daisuke D Roussel Martine F MF Kool Marcel M Jones David T W DT Witt Hendrick H Fernandez-L Africa A Kenney Anna M AM Wechsler-Reya Robert J RJ Dirks Peter P Aviv Tzvi T Grajkowska Wieslawa A WA Perek-Polnik Marta M Haberler Christine C CC Delattre Olivier O Reynaud Stéphanie S SS Doz François F FF Pernet-Fattet Sarah S SS Cho Byung-Kyu BK Kim Seung-Ki SK Wang Kyu-Chang KC Scheurlen Wolfram W Eberhart Charles G CG Fèvre-Montange Michelle M Jouvet Anne A Pollack Ian F IF Fan Xing X Muraszko Karin M KM Gillespie G Yancey GY Di Rocco Concezio C Massimi Luca L Michiels Erna M C EM Kloosterhof Nanne K NK French Pim J PJ Kros Johan M JM Olson James M JM Ellenbogen Richard G RG Zitterbart Karel K Kren Leos L Thompson Reid C RC Cooper Michael K MK Lach Boleslaw B McLendon Roger E RE Bigner Darell D DD Fontebasso Adam A Albrecht Steffen S Jabado Nada N Lindsey Janet C JC Bailey Simon S Gupta Nalin N Weiss William A WA Bognár László L Klekner Almos A Van Meter Timothy E TE Kumabe Toshihiro T Tominaga Teiji T Elbabaa Samer K SK Leonard Jeffrey R JR Rubin Joshua B JB Liau Linda M LM Van Meir Erwin G EG Fouladi Maryam M Nakamura Hideo H Cinalli Giuseppe G Garami Miklós M Hauser Peter P Saad Ali G AG Iolascon Achille A Jung Shin S Carlotti Carlos G CG Vibhakar Rajeev R Ra Young Shin YS Robinson Shenandoah S Zollo Massimo M Faria Claudia C CC Chan Jennifer A JA Levy Michael L ML Sorensen Poul H B PH Meyerson Matthew M Pomeroy Scott L SL Cho Yoon-Jae YJ Bader Gary D GD Tabori Uri U Hawkins Cynthia E CE Bouffet Eric E Scherer Stephen W SW Rutka James T JT Malkin David D Clifford Steven C SC Jones Steven J M SJ Korbel Jan O JO Pfister Stefan M SM Marra Marco A MA Taylor Michael D MD eng GEO GSE37385 AT1-112286 Canadian Institutes of Health Research Canada CA116804 CA NCI NIH HHS United States CA138292 CA NCI NIH HHS United States CA159859 CA NCI NIH HHS United States CA86335 CA NCI NIH HHS United States K08 NS059790 NS NINDS NIH HHS United States P20 CA151129 CA NCI NIH HHS United States P30 CA138292 CA NCI NIH HHS United States P30 HD018655 HD NICHD NIH HHS United States P41 GM103504 GM NIGMS NIH HHS United States R01 CA086335 CA NCI NIH HHS United States R01 CA109467 CA NCI NIH HHS United States R01 CA114567 CA NCI NIH HHS United States R01 CA116804 CA NCI NIH HHS United States R01 CA148621 CA NCI NIH HHS United States R01 CA155360 CA NCI NIH HHS United States R01 CA159859 CA NCI NIH HHS United States R01 CA163737 CA NCI NIH HHS United States R01 NS061070 NS NINDS NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

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1553-7358 8 6 2012 PLoS Comput. Biol. Multiple genetic interaction experiments provide complementary information useful for gene function prediction. e1002559 10.1371/journal.pcbi.1002559 Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens. Michaut Magali M The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. Bader Gary D GD eng P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't 2012 06 21

United States PLoS Comput Biol 101238922 1553-734X IM Science. 2004 Feb 6;303(5659):832-5 14764878 Science. 2004 Feb 6;303(5659):808-13 14764870 Genome Biol. 2005;6(4):R38 15833125 Cell. 2005 Nov 4;123(3):507-19 16269340 Nat Genet. 2006 Aug;38(8):896-903 16845399 Nat Genet. 2007 Feb;39(2):199-206 17206143 Mol Syst Biol. 2007;3:96 17389876 Nature. 2007 Apr 12;446(7137):806-10 17314980 Nat Methods. 2007 Oct;4(10):861-6 17893680 Genome Biol. 2007;8(9):R186 17845715 Proc Natl Acad Sci U S A. 2008 Mar 4;105(9):3461-6 18305163 Science. 2008 Apr 18;320(5874):362-5 18420932 Genome Biol. 2008;9 Suppl 1:S4 18613948 Proc Natl Acad Sci U S A. 2008 Oct 28;105(43):16653-8 18931302 Nat Methods. 2008 Sep;5(9):789-95 18677321 Science. 2009 Mar 27;323(5922):1693-7 19325107 PLoS Comput Biol. 2009 Apr;5(4):e1000347 19343223 J Biol. 2009;8(4):35 19486505 J Cell Biol. 2009 Jun 15;185(6):1097-110 19506040 J Cell Biol. 2010 Jan 11;188(1):69-81 20065090 Science. 2010 Jan 22;327(5964):425-31 20093466 PLoS Comput Biol. 2010 Mar;6(3):e1000698 20221257 BMC Genomics. 2010;11:493 20831804 Bioinformatics. 2010 Nov 15;26(22):2927-8 20926419 Nat Methods. 2010 Dec;7(12):1017-24 21076421 Science. 2010 Dec 3;330(6009):1385-9 21127252 Nat Methods. 2011 Apr;8(4):341-6 21378980 BMC Syst Biol. 2011;5:45 21435228 Mol Syst Biol. 2012;8:565 22252388 J Biol. 2007;6(3):8 17897480 Genome Res. 2003 Nov;13(11):2498-504 14597658 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D258-61 14681407 Genome Res. 2004 Jun;14(6):1085-94 15173114 Computational Biology Computer Simulation Epistasis, Genetic Gene Regulatory Networks Genome, Fungal Models, Genetic Mutation Phenotype Saccharomyces cerevisiae genetics growth & development PMC3380825 2011 11 2 2012 5 1 2012 6 21 2012 6 28 6 0 2012 6 28 6 0 2012 10 26 6 0 ppublish 10.1371/journal.pcbi.1002559 PCOMPBIOL-D-11-01633 22737063 PMC3380825 22696458 2012 07 25 2013 02 28 2014 10 16

1549-4918 30 8 2012 Aug Stem Cells A comparative transcriptomic analysis reveals conserved features of stem cell pluripotency in planarians and mammals. 1734-45 10.1002/stem.1144 Many long-lived species of animals require the function of adult stem cells throughout their lives. However, the transcriptomes of stem cells in invertebrates and vertebrates have not been compared, and consequently, ancestral regulatory circuits that control stem cell populations remain poorly defined. In this study, we have used data from high-throughput RNA sequencing to compare the transcriptomes of pluripotent adult stem cells from planarians with the transcriptomes of human and mouse pluripotent embryonic stem cells. From a stringently defined set of 4,432 orthologs shared between planarians, mice and humans, we identified 123 conserved genes that are ≥5-fold differentially expressed in stem cells from all three species. Guided by this gene set, we used RNAi screening in adult planarians to discover novel stem cell regulators, which we found to affect the stem cell-associated functions of tissue homeostasis, regeneration, and stem cell maintenance. Examples of genes that disrupted these processes included the orthologs of TBL3, PSD12, TTC27, and RACK1. From these analyses, we concluded that by comparing stem cell transcriptomes from diverse species, it is possible to uncover conserved factors that function in stem cell biology. These results provide insights into which genes comprised the ancestral circuitry underlying the control of stem cell self-renewal and pluripotency. Copyright © 2012 AlphaMed Press. Labbé Roselyne M RM The Hospital for Sick Children, Program in Developmental and Stem Cell Biology, University of Toronto, Toronto, Ontario, Canada. Irimia Manuel M Currie Ko W KW Lin Alexander A Zhu Shu Jun SJ Brown David D R DD Ross Eric J EJ Voisin Veronique V Bader Gary D GD Blencowe Benjamin J BJ Pearson Bret J BJ eng P41 GM103504 GM NIGMS NIH HHS United States Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't

United States Stem Cells 9304532 1066-5099 IM Genome Biol. 2012;13(3):R19 22439894 Cancer Res. 2012 Apr 1;72(7):1717-27 22337995 Development. 2002 Dec;129(24):5659-65 12421706 Proc Natl Acad Sci U S A. 2003 Sep 30;100 Suppl 1:11861-5 12917490 Zoolog Sci. 2004 Mar;21(3):275-83 15056922 EMBO J. 1998 Dec 1;17(23):7078-90 9843512 Dev Genes Evol. 2005 Mar;215(3):143-57 15657737 Dev Cell. 2005 May;8(5):635-49 15866156 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 Science. 2005 Nov 25;310(5752):1327-30 16311336 Dev Growth Differ. 2006 Aug;48(6):371-80 16872450 Dev Cell. 2006 Aug;11(2):159-69 16890156 PLoS One. 2007;2(4):e383 17440619 RNA. 2008 Jun;14(6):1174-86 18456843 Cell Stem Cell. 2008 Sep 11;3(3):327-39 18786419 Nature. 2008 Sep 18;455(7211):401-5 18724358 Dev Dyn. 2009 Feb;238(2):443-50 19161223 Bioessays. 2009 Apr;31(4):478-86 19274660 Genome Biol. 2009;10(3):R25 19261174 Cell. 2009 Sep 4;138(5):822-9 19737509 Semin Cell Dev Biol. 2009 Dec;20(9):1114-25 19761866 Development. 2010 Jan;137(2):213-21 20040488 Dev Growth Differ. 2010 Jan;52(1):131-44 20078655 J Cell Sci. 2010 Mar 1;123(Pt 5):690-8 20124416 Proc Natl Acad Sci U S A. 2010 Mar 16;107(11):5254-9 20194744 Nat Biotechnol. 2010 May;28(5):503-10 20436462 PLoS One. 2010;5(11):e13984 21085593 PLoS One. 2010;5(12):e15617 21179477 BMC Genomics. 2010;11:731 21194483 Science. 2011 May 13;332(6031):811-6 21566185 BMC Biol. 2011;9:41 21649942 Cell. 2011 Jun 10;145(6):851-62 21663791 Nat Biotechnol. 2011 Jul;29(7):644-52 21572440 Methods Mol Biol. 2011;781:257-77 21877285 Nat Med. 2011 Sep;17(9):1086-93 21873988 Development. 2011 Oct;138(20):4387-98 21937596 Nature. 2011 Oct 20;478(7369):343-8 22012392 Cancer Sci. 2011 Nov;102(11):2007-13 21848913 Genome Biol. 2011;12(8):R76 21846378 Nature. 2001 Nov 1;414(6859):105-11 11689955 Animals Cell Differentiation genetics Gene Expression Profiling Humans Mammals Mice Planarians Pluripotent Stem Cells cytology physiology NIHMS622648 PMC4161212 2012 6 15 6 0 2012 6 15 6 0 2013 3 1 6 0 ppublish 10.1002/stem.1144 22696458 PMC4161212 NIHMS622648 22561014 2012 08 07 2012 10 29

1873-3468 586 17 2012 Aug 14 FEBS Lett. Domain-mediated protein interaction prediction: From genome to network. 2751-63 10.1016/j.febslet.2012.04.027 Protein-protein interactions (PPIs), involved in many biological processes such as cellular signaling, are ultimately encoded in the genome. Solving the problem of predicting protein interactions from the genome sequence will lead to increased understanding of complex networks, evolution and human disease. We can learn the relationship between genomes and networks by focusing on an easily approachable subset of high-resolution protein interactions that are mediated by peptide recognition modules (PRMs) such as PDZ, WW and SH3 domains. This review focuses on computational prediction and analysis of PRM-mediated networks and discusses sequence- and structure-based interaction predictors, techniques and datasets for identifying physiologically relevant PPIs, and interpreting high-resolution interaction networks in the context of evolution and human disease. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved. Reimand Jüri J The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario, Canada. Juri.Reimand@utoronto.ca Hui Shirley S Jain Shobhit S Law Brian B Bader Gary D GD eng Journal Article Review 2012 05 03

Netherlands FEBS Lett 0155157 0014-5793 0 Peptides 0 Proteins 9007-49-2 DNA IM Amino Acid Sequence Animals Artificial Intelligence Computational Biology methods DNA chemistry Genome Humans Molecular Conformation Molecular Sequence Data Mutation Peptides chemistry Protein Binding Protein Interaction Domains and Motifs Protein Interaction Mapping methods Protein Structure, Tertiary Proteins chemistry src Homology Domains 2012 3 25 2012 4 17 2012 5 3 2012 5 8 6 0 2012 5 9 6 0 2012 10 30 6 0 ppublish S0014-5793(12)00316-X 10.1016/j.febslet.2012.04.027 22561014 22460789 2012 04 12 2012 06 04 2014 11 20

1091-6490 109 15 2012 Apr 10 Proc. Natl. Acad. Sci. U.S.A. Seventeen-gene signature from enriched Her2/Neu mammary tumor-initiating cells predicts clinical outcome for human HER2+:ERα- breast cancer. 5832-7 10.1073/pnas.1201105109 Human Epidermal Growth Factor Receptor 2-positive (HER2(+)) breast cancer (BC) is a highly aggressive disease commonly treated with chemotherapy and anti-HER2 drugs, including trastuzumab. There is currently no way to predict which HER2(+) BC patients will benefit from these treatments. Previous prognostic signatures for HER2(+) BC were developed irrespective of the subtype or the hierarchical organization of cancer in which only a fraction of cells, tumor-initiating cells (TICs), can sustain tumor growth. Here, we used serial dilution and single-cell transplantation assays to identify MMTV-Her2/Neu mouse mammary TICs as CD24(+):JAG1(-) at a frequency of 2-4.5%. A 17-gene Her2-TIC-enriched signature (HTICS), generated on the basis of differentially expressed genes in TIC versus non-TIC fractions and trained on one HER2(+) BC cohort, predicted clinical outcome on multiple independent HER2(+) cohorts. HTICS included up-regulated genes involved in S/G2/M transition and down-regulated genes involved in immune response. Its prognostic power was independent of other predictors, stratified lymph node(+) HER2(+) BC into low and high-risk subgroups, and was specific for HER2(+):estrogen receptor alpha-negative (ERα(-)) patients (10-y overall survival of 83.6% for HTICS(-) and 24.0% for HTICS(+) tumors; hazard ratio = 5.57; P = 0.002). Whereas HTICS was specific to HER2(+):ERα(-) tumors, a previously reported stroma-derived signature was predictive for HER2(+):ERα(+) BC. Retrospective analyses revealed that patients with HTICS(+) HER2(+):ERα(-) tumors resisted chemotherapy but responded to chemotherapy plus trastuzumab. HTICS is, therefore, a powerful prognostic signature for HER2(+):ERα(-) BC that can be used to identify high risk patients that would benefit from anti-HER2 therapy. Liu Jeff C JC Division of Cell and Molecular Biology, Toronto General Research Institute, University Health Network, Toronto, ON, Canada M5G 2M9. Voisin Veronique V Bader Gary D GD Deng Tao T Pusztai Lajos L Symmans William Fraser WF Esteva Francisco J FJ Egan Sean E SE Zacksenhaus Eldad E eng GEO GSE29590 GSE29616 P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't 2012 03 28

United States Proc Natl Acad Sci U S A 7505876 0027-8424 0 Antibodies, Monoclonal, Humanized 0 Antigens, CD24 0 Antineoplastic Agents 0 Calcium-Binding Proteins 0 Estrogen Receptor alpha 0 Intercellular Signaling Peptides and Proteins 0 Membrane Proteins 0 estrogen receptor alpha, human 134324-36-0 Serrate proteins EC 2.7.10.1 Receptor, ErbB-2 P188ANX8CK trastuzumab IM N Engl J Med. 2001 Mar 15;344(11):783-92 11248153 Breast Cancer Res Treat. 2012 Apr;132(3):781-91 21373875 Proc Natl Acad Sci U S A. 1992 Nov 15;89(22):10578-82 1359541 N Engl J Med. 2004 Dec 30;351(27):2817-26 15591335 Cancer Res. 2005 Sep 15;65(18):8530-7 16166334 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 Nat Rev Cancer. 2006 Feb;6(2):99-106 16491069 N Engl J Med. 2007 Jan 18;356(3):217-26 17229949 Eur J Immunol. 2007 Mar;37(3):675-85 17304628 Cancer Res. 2007 Sep 15;67(18):8671-81 17875707 Nat Biotechnol. 2008 Mar;26(3):317-25 18278033 Nat Med. 2008 May;14(5):518-27 18438415 Clin Cancer Res. 2008 Aug 15;14(16):5158-65 18698033 Oncogene. 2008 Aug 28;27(37):5019-32 18469855 Cancer Res. 2008 Oct 1;68(19):7711-7 18829523 Oncogene. 2008 Oct 16;27(47):6120-30 18591932 Eur J Cancer. 2008 Dec;44(18):2806-12 19022660 Oncologist. 2009 Jan;14(1):1-11 19147689 Semin Radiat Oncol. 2009 Apr;19(2):71-7 19249644 Cancer Res. 2009 Apr 15;69(8):3405-14 19351845 Cell. 2009 Sep 18;138(6):1083-95 19766563 J Clin Oncol. 2010 Apr 10;28(11):1813-20 20231686 J Clin Invest. 2010 Sep;120(9):3296-309 20679727 PLoS One. 2010;5(11):e13984 21085593 Nature. 2011 Jan 20;469(7330):362-7 21248843 Clin Cancer Res. 2011 Mar 1;17(5):952-8 21248299 Lancet Oncol. 2011 Mar;12(3):236-44 21354370 Cell. 2011 Oct 14;147(2):275-92 22000009 Cell. 1988 Jul 1;54(1):105-15 2898299 Animals Antibodies, Monoclonal, Humanized pharmacology therapeutic use Antigens, CD24 metabolism Antineoplastic Agents pharmacology therapeutic use Breast Neoplasms drug therapy genetics Calcium-Binding Proteins metabolism Cell Differentiation drug effects Cell Division drug effects Estrogen Receptor alpha metabolism Female Gene Expression Profiling Gene Expression Regulation, Neoplastic drug effects Genes, Neoplasm genetics Humans Intercellular Signaling Peptides and Proteins metabolism Membrane Proteins metabolism Mice Neoadjuvant Therapy Neoplastic Stem Cells drug effects metabolism pathology Prognosis Receptor, ErbB-2 metabolism Signal Transduction drug effects Treatment Outcome PMC3326451 2012 3 28 2012 3 31 6 0 2012 3 31 6 0 2012 6 5 6 0 ppublish 1201105109 10.1073/pnas.1201105109 22460789 PMC3326451 22453906 2012 03 28 2012 06 25

1548-7105 9 4 2012 Apr Nat. Methods Phosphorylation sites of higher stoichiometry are more conserved. 317; author reply 318 10.1038/nmeth.1941 Tan Chris Soon Heng CS Bader Gary D GD eng MOP-84324 Canadian Institutes of Health Research Canada Comment Letter Research Support, Non-U.S. Gov't 2012 03 27

United States Nat Methods 101215604 1548-7091 0 Proteome EC 3.1.3.- Phosphoric Monoester Hydrolases IM Nat Methods. 2011 Aug;8(8):677-83 21725298 Isotope Labeling methods Mass Spectrometry methods Phosphoric Monoester Hydrolases chemistry metabolism Phosphorylation physiology Proteome chemistry metabolism 2012 3 29 6 0 2012 3 29 6 0 2012 6 26 6 0 epublish nmeth.1941 10.1038/nmeth.1941 22453906 22438817 2012 03 22 2012 09 04 2014 10 19

1553-7404 8 3 2012 PLoS Genet. Mapping the Hsp90 genetic interaction network in Candida albicans reveals environmental contingency and rewired circuitry. e1002562 10.1371/journal.pgen.1002562 The molecular chaperone Hsp90 regulates the folding of diverse signal transducers in all eukaryotes, profoundly affecting cellular circuitry. In fungi, Hsp90 influences development, drug resistance, and evolution. Hsp90 interacts with -10% of the proteome in the model yeast Saccharomyces cerevisiae, while only two interactions have been identified in Candida albicans, the leading fungal pathogen of humans. Utilizing a chemical genomic approach, we mapped the C. albicans Hsp90 interaction network under diverse stress conditions. The chaperone network is environmentally contingent, and most of the 226 genetic interactors are important for growth only under specific conditions, suggesting that they operate downstream of Hsp90, as with the MAPK Hog1. Few interactors are important for growth in many environments, and these are poised to operate upstream of Hsp90, as with the protein kinase CK2 and the transcription factor Ahr1. We establish environmental contingency in the first chaperone network of a fungal pathogen, novel effectors upstream and downstream of Hsp90, and network rewiring over evolutionary time. Diezmann Stephanie S Department of Molecular Genetics, University of Toronto, Toronto, Canada. Michaut Magali M Shapiro Rebecca S RS Bader Gary D GD Cowen Leah E LE eng MOP-86452 Canadian Institutes of Health Research Canada P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't 2012 03 15

United States PLoS Genet 101239074 1553-7390 0 Benzoquinones 0 Culture Media 0 HSP90 Heat-Shock Proteins 0 Lactams, Macrocyclic 8L70Q75FXE Adenosine Triphosphate EC 2.7.- Phosphotransferases Z3K3VJ16KU geldanamycin IM Clin Infect Dis. 2005 Nov 1;41(9):1232-9 16206095 Science. 2005 Sep 30;309(5744):2185-9 16195452 Nature. 2005 Nov 3;438(7064):108-12 16267557 PLoS Pathog. 2006 Apr;2(4):e35 16652171 Proteomics. 2009 Oct;9(20):4686-703 19824012 PLoS Genet. 2009 Dec;5(12):e1000783 20041210 Crit Rev Microbiol. 2010;36(1):1-53 20088682 PLoS Pathog. 2010 Feb;6(2):e1000752 20140194 Circ Res. 2010 Apr 30;106(8):1404-12 20299663 Nat Rev Mol Cell Biol. 2010 Jul;11(7):515-28 20531426 Nat Genet. 2010 Jul;42(7):590-8 20543849 Nat Rev Cancer. 2010 Aug;10(8):537-49 20651736 PLoS Pathog. 2010;6(8):e1001069 20865172 Annu Rev Genet. 2010;44:189-216 21047258 J Biol Chem. 2010 Dec 3;285(49):37964-75 20837488 Annu Rev Biochem. 2006;75:271-94 16756493 PLoS Pathog. 2006 Jul;2(7):e63 16839200 Cell Microbiol. 2007 Jan;9(1):233-45 16939537 Clin Microbiol Rev. 2007 Jan;20(1):133-63 17223626 Eukaryot Cell. 2007 Mar;6(3):521-32 17220467 Cell Microbiol. 2007 Jul;9(7):1647-59 17346314 Cell. 2007 Oct 5;131(1):121-35 17923092 PLoS Comput Biol. 2007 Sep;3(9):1701-15 17941702 Nat Rev Microbiol. 2008 Jan;6(1):67-78 18079743 Nat Rev Microbiol. 2008 Mar;6(3):187-98 18246082 Eukaryot Cell. 2008 May;7(5):747-64 18375617 J Biol Chem. 2008 Jul 4;283(27):18473-7 18442971 Cell Stress Chaperones. 2009 Jan;14(1):83-94 18636345 BMC Genomics. 2008;9:578 19055720 Proc Natl Acad Sci U S A. 2009 Feb 24;106(8):2818-23 19196973 Curr Biol. 2009 Apr 28;19(8):621-9 19327993 PLoS Pathog. 2009 Jul;5(7):e1000532 19649312 Mol Microbiol. 2011 Feb;79(4):940-53 21299649 Science. 2010 Dec 24;330(6012):1820-4 21205668 Bioinformatics. 2009 Nov 15;25(22):3043-4 19717575 Mol Cell. 2011 Mar 18;41(6):672-81 21419342 Oncotarget. 2011 May;2(5):407-17 21576760 BMC Microbiol. 2011;11:162 21745372 EMBO J. 2002 May 15;21(10):2343-53 12006487 Nature. 2002 Jun 6;417(6889):618-24 12050657 J Biol Chem. 2003 Jan 31;278(5):2829-36 12435747 Mol Biol Cell. 2003 Oct;14(10):4296-305 14517337 Genome Res. 2003 Nov;13(11):2498-504 14597658 J Biol Chem. 1992 Apr 5;267(10):7042-7 1551911 Proc Natl Acad Sci U S A. 1994 Aug 30;91(18):8324-8 8078881 Nature. 1998 Nov 26;396(6709):336-42 9845070 J Bacteriol. 1999 May;181(10):3058-68 10322006 Nat Biotechnol. 1999 Oct;17(10):1030-2 10504710 Bioinformatics. 2004 Nov 22;20(17):3246-8 15180930 Cell. 2005 Mar 11;120(5):715-27 15766533 Mol Microbiol. 2005 Apr;56(2):559-73 15813744 Eukaryot Cell. 2005 May;4(5):849-60 15879519 Nature. 2005 Nov 3;438(7064):103-7 16267556 Adenosine Triphosphate metabolism Benzoquinones pharmacology Candida albicans genetics growth & development metabolism Culture Media Environmental Microbiology Gene Expression Regulation, Bacterial Gene Regulatory Networks drug effects genetics HSP90 Heat-Shock Proteins genetics metabolism Lactams, Macrocyclic pharmacology Phosphotransferases metabolism Protein Interaction Maps drug effects genetics Saccharomyces cerevisiae genetics metabolism Signal Transduction drug effects genetics Stress, Physiological genetics PMC3305360 2011 8 17 2012 1 13 2012 3 15 2012 3 23 6 0 2012 3 23 6 0 2012 9 5 6 0 ppublish 10.1371/journal.pgen.1002562 PGENETICS-D-11-01760 22438817 PMC3305360 22210894 2012 03 29 2012 05 29 2014 10 19

1362-4962 40 6 2012 Mar Nucleic Acids Res. MUSI: an integrated system for identifying multiple specificity from very large peptide or nucleic acid data sets. e47 10.1093/nar/gkr1294 Peptide recognition domains and transcription factors play crucial roles in cellular signaling. They bind linear stretches of amino acids or nucleotides, respectively, with high specificity. Experimental techniques that assess the binding specificity of these domains, such as microarrays or phage display, can retrieve thousands of distinct ligands, providing detailed insight into binding specificity. In particular, the advent of next-generation sequencing has recently increased the throughput of such methods by several orders of magnitude. These advances have helped reveal the presence of distinct binding specificity classes that co-exist within a set of ligands interacting with the same target. Here, we introduce a software system called MUSI that can rapidly analyze very large data sets of binding sequences to determine the relevant binding specificity patterns. Our pipeline provides two major advances. First, it can detect previously unrecognized multiple specificity patterns in any data set. Second, it offers integrated processing of very large data sets from next-generation sequencing machines. The results are visualized as multiple sequence logos describing the different binding preferences of the protein under investigation. We demonstrate the performance of MUSI by analyzing recent phage display data for human SH3 domains as well as microarray data for mouse transcription factors. Kim Taehyung T The Donnelly Centre, Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada M5S 3E1. Tyndel Marc S MS Huang Haiming H Sidhu Sachdev S SS Bader Gary D GD Gfeller David D Kim Philip M PM eng MOP-84324 Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't Validation Studies 2011 12 30

England Nucleic Acids Res 0411011 0305-1048 0 Ligands 0 Peptide Library 0 Peptides 0 Transcription Factors IM PLoS Biol. 2009 Oct;7(10):e1000218 19841731 BMC Bioinformatics. 2010;11:243 20459839 J Cell Sci. 2001 Apr;114(Pt 7):1253-63 11256992 J Cell Sci. 2001 Sep;114(Pt 18):3219-31 11591811 Mol Cell. 2001 Nov;8(5):937-46 11741530 J Comput Biol. 2002;9(2):447-64 12015892 Science. 2003 Apr 18;300(5618):445-52 12702867 Nucleic Acids Res. 2003 Jul 1;31(13):3586-8 12824371 Nucleic Acids Res. 2003 Jul 1;31(13):3635-41 12824383 Mol Cell Biol. 2004 Mar;24(6):2546-59 14993291 Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W199-203 15215380 J Mol Biol. 2004 Oct 22;343(3):703-18 15465056 Science. 1989 Jul 28;245(4916):371-8 2667136 Nucleic Acids Res. 1990 Oct 25;18(20):6097-100 2172928 Proc Int Conf Intell Syst Mol Biol. 1995;3:21-9 7584439 Proc Int Conf Intell Syst Mol Biol. 1994;2:28-36 7584402 Cell. 1996 Jun 28;85(7):1067-76 8674113 Comput Appl Biosci. 1997 Aug;13(4):365-76 9283751 EMBO J. 1998 Jun 1;17(11):3091-100 9606191 Mol Cell Biol. 1998 Dec;18(12):7038-51 9819391 Nat Genet. 2004 Dec;36(12):1331-9 15543148 Nat Methods. 2004 Oct;1(1):27-9 15782149 Bioinformatics. 2005 Jun 1;21(11):2657-66 15797905 Annu Rev Cell Dev Biol. 2005;21:659-93 16212511 Nat Protoc. 2007;2(6):1368-86 17545975 Science. 2007 Jul 20;317(5836):364-9 17641200 Cell. 2008 Jun 27;133(7):1266-76 18585359 Sci Signal. 2008;1(35):ra2 18765831 Bioinformatics. 2008 Sep 15;24(18):2096-7 18689808 Nat Biotechnol. 2008 Sep;26(9):1041-5 18711339 Nucleic Acids Res. 2009 Jan;37(Database issue):D77-82 18842628 Nat Methods. 2008 Sep;5(9):829-34 19160518 Nat Chem Biol. 2009 Apr;5(4):217-9 19252499 Science. 2009 Jun 26;324(5935):1720-3 19443739 Nat Rev Genet. 2009 Sep;10(9):605-16 19668247 PLoS Comput Biol. 2009 Dec;5(12):e1000590 19997485 Nucleic Acids Res. 2010 Jan;38(Database issue):D105-10 19906716 Sci Signal. 2010;3(104):ra3 20068231 Proc Natl Acad Sci U S A. 2010 Mar 9;107(10):4544-9 20176964 EMBO J. 2010 Jul 7;29(13):2147-60 20517297 Bioinformatics. 2010 Aug 1;26(15):1899-900 20427515 Nat Methods. 2010 Sep;7(9):741-6 20711194 Mol Biosyst. 2010 Oct;6(10):1782-90 20714644 Mol Syst Biol. 2011 Apr 26;7:484 21525870 Nucleic Acids Res. 2010 Apr;38(6):1767-71 20015970 Proc Natl Acad Sci U S A. 1999 Nov 9;96(23):13130-5 10557285 Animals Binding Sites High-Throughput Nucleotide Sequencing Humans Ligands Mice Peptide Library Peptides chemistry Position-Specific Scoring Matrices Protein Interaction Domains and Motifs Sequence Analysis, Protein Software Transcription Factors metabolism src Homology Domains PMC3315295 2011 12 30 2012 1 3 6 0 2012 1 3 6 0 2012 5 30 6 0 ppublish gkr1294 10.1093/nar/gkr1294 22210894 PMC3315295 23552839 2013 04 04 2013 04 04 2015 07 01

2157-9024 1 2012 Oncogenesis Disruption of Abi1/Hssh3bp1 expression induces prostatic intraepithelial neoplasia in the conditional Abi1/Hssh3bp1 KO mice. e26 10.1038/oncsis.2012.28 Prostate cancer is one of the leading causes of cancer-related deaths in the United States and a leading diagnosed non-skin cancer in American men. Genetic mutations underlying prostate tumorigenesis include alterations of tumor suppressor genes. We tested the tumor suppressor hypothesis for ABI1/hSSH3BP1 by searching for gene mutations in primary prostate tumors from patients, and by analyzing the consequences of prostate-specific disruption of the mouse Abi1/Hssh3bp1 ortholog. We sequenced the ABI1/hSSH3BP1 gene and identified recurring mutations in 6 out of 35 prostate tumors. Moreover, complementation and anchorage-independent growth, proliferation, cellular adhesion and xenograft assays using the LNCaP cell line, which contains a loss-of-function Abi1 mutation, and a stably expressed wild-type or mutated ABI gene, were consistent with the tumor suppressor hypothesis. To test the hypothesis further, we disrupted the gene in the mouse prostate by breeding the Abi1 floxed strain with the probasin promoter-driven Cre recombinase strain. Histopathological evaluation of mice indicated development of prostatic intraepithelial neoplasia (PIN) in Abi1/Hssh3bp1 knockout mouse as early as the eighth month, but no progression beyond PIN was observed in mice as old as 12 months. Observed decreased levels of E-cadherin, β-catenin and WAVE2 in mouse prostate suggest abnormal cellular adhesion as the mechanism underlying PIN development owing to Abi1 disruption. Analysis of syngeneic cell lines point to the possibility that upregulation of phospho-Akt underlies the enhanced cellular proliferation phenotype of cells lacking Abi1. This study provides proof-of-concept for the hypothesis that Abi1 downregulation has a role in the development of prostate cancer. Xiong X X Laboratory of Cell Signaling, New York Blood Center, New York, NY, USA. Chorzalska A A Dubielecka P M PM White J R JR Vedvyas Y Y Hedvat C V CV Haimovitz-Friedman A A Koutcher J A JA Reimand J J Bader G D GD Sawicki J A JA Kotula L L eng R01 NS044968 NS NINDS NIH HHS United States Journal Article 2012 09 03

United States Oncogenesis 101580004 2157-9024 PMC3503296 2013 4 5 6 0 2012 1 1 0 0 2012 1 1 0 1 epublish oncsis201228 10.1038/oncsis.2012.28 23552839 PMC3503296 22180826 2011 12 19 2012 08 23

1944-3277 5 2 2011 Nov 30 Stand Genomic Sci Meeting report from the first meetings of the Computational Modeling in Biology Network (COMBINE). 230-42 10.4056/sigs.2034671 The Computational Modeling in Biology Network (COMBINE), is an initiative to coordinate the development of the various community standards and formats in computational systems biology and related fields. This report summarizes the activities pursued at the first annual COMBINE meeting held in Edinburgh on October 6-9 2010 and the first HARMONY hackathon, held in New York on April 18-22 2011. The first of those meetings hosted 81 attendees. Discussions covered both official COMBINE standards-(BioPAX, SBGN and SBML), as well as emerging efforts and interoperability between different formats. The second meeting, oriented towards software developers, welcomed 59 participants and witnessed many technical discussions, development of improved standards support in community software systems and conversion between the standards. Both meetings were resounding successes and showed that the field is now mature enough to develop representation formats and related standards in a coordinated manner. Le Novère Nicolas N Hucka Michael M Anwar Nadia N Bader Gary D GD Demir Emek E Moodie Stuart S Sorokin Anatoly A eng R01 GM070923 GM NIGMS NIH HHS United States Journal Article

United States Stand Genomic Sci 101530505 1944-3277 PMC3235518 2011 12 20 6 0 2011 12 20 6 0 2011 12 20 6 1 ppublish 10.4056/sigs.2034671 sigs.2034671 22180826 PMC3235518 22070249 2012 01 23 2012 05 14 2014 10 21

1471-2105 12 2011 BMC Bioinformatics clusterMaker: a multi-algorithm clustering plugin for Cytoscape. 436 10.1186/1471-2105-12-436 In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL. Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. The Cytoscape plugin clusterMaker provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the clusterMaker plugin. clusterMaker is available via the Cytoscape plugin manager. Morris John H JH Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. scooter@cgl.ucsf.edu Apeltsin Leonard L Newman Aaron M AM Baumbach Jan J Wittkop Tobias T Su Gang G Bader Gary D GD Ferrin Thomas E TE eng P41 GM103504 GM NIGMS NIH HHS United States P41 RR001081 RR NCRR NIH HHS United States Journal Article Research Support, N.I.H., Extramural 2011 11 09

England BMC Bioinformatics 100965194 1471-2105 EC 5.1.- Racemases and Epimerases EC 5.1.99.1 methylmalonyl-coenzyme A racemase IM Proc Natl Acad Sci U S A. 2003 Feb 4;100(3):1128-33 12538875 Biotechniques. 2003 Feb;34(2):374-8 12613259 BMC Bioinformatics. 2003 Jan 13;4:2 12525261 Genome Res. 2003 Nov;13(11):2498-504 14597658 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D112-4 14681371 J Mol Biol. 1990 Oct 5;215(3):403-10 2231712 Bioinformatics. 2004 Nov 22;20(17):3013-20 15180928 Bioinformatics. 2004 Nov 22;20(17):3246-8 15180930 Bioinformatics. 2005 Aug 15;21(16):3448-9 15972284 Cell. 2005 Nov 4;123(3):507-19 16269340 Biochemistry. 2006 Feb 28;45(8):2545-55 16489747 Nature. 2006 Mar 30;440(7084):631-6 16429126 Nature. 2006 Mar 30;440(7084):637-43 16554755 Methods Enzymol. 2006;411:134-93 16939790 Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8 9843981 Science. 2007 Feb 16;315(5814):972-6 17218491 Mol Cell Proteomics. 2007 Mar;6(3):439-50 17200106 Nature. 2007 Apr 12;446(7137):806-10 17314980 Nat Protoc. 2007;2(10):2366-82 17947979 BMC Bioinformatics. 2007;8:396 17941985 Curr Protein Pept Sci. 2008 Feb;9(1):70-95 18336324 Mol Cell. 2008 Dec 5;32(5):735-46 19061648 Nat Protoc. 2009;4(1):44-57 19131956 J Theor Biol. 2009 Feb 21;256(4):625-31 19049810 BMC Bioinformatics. 2008;9:462 18959783 BMC Bioinformatics. 2009;10:99 19331680 Bioinformatics. 2009 Aug 1;25(15):1994-6 19454618 PLoS Biol. 2009 Sep;7(9):e1000205 19787035 Science. 2010 Jan 22;327(5964):425-31 20093466 BMC Bioinformatics. 2010;11:120 20214776 BMC Bioinformatics. 2010;11:117 20202218 Amino Acids. 2010 Apr;38(4):1237-52 19669079 Nat Methods. 2010 Jun;7(6):419-20 20508635 Bioinformatics. 2010 Dec 15;26(24):3135-7 21123224 Bioinformatics. 2011 Feb 1;27(3):326-33 21118823 Nat Protoc. 2011 Mar;6(3):285-95 21372810 Genome Res. 1999 Nov;9(11):1106-15 10568750 Nucleic Acids Res. 2000 Jan 1;28(1):49-55 10592179 Proc Natl Acad Sci U S A. 2000 Feb 1;97(3):1143-7 10655498 Nature. 2000 Feb 10;403(6770):623-7 10688190 Bioinformatics. 2000 May;16(5):451-7 10871267 Biochemistry. 2000 Nov 14;39(45):13625-32 11076500 Nucleic Acids Res. 2001 Jan 1;29(1):33-6 11125042 Nucleic Acids Res. 2001 Jan 1;29(1):44-8 11125045 Bioinformatics. 2001 Mar;17(3):282-3 11294794 Curr Opin Struct Biol. 2001 Jun;11(3):334-9 11406384 Nucleic Acids Res. 2002 Jan 1;30(1):299-300 11752319 Nature. 2002 Jan 10;415(6868):180-3 11805837 Nucleic Acids Res. 2002 Apr 1;30(7):1575-84 11917018 Bioinformatics. 2002 Jul;18(7):908-21 12117788 Bioinformatics. 2002;18 Suppl 1:S233-40 12169552 Protein Eng. 2002 Aug;15(8):643-9 12364578 FEBS Lett. 2002 Nov 6;531(2):259-64 12417323 Nucleic Acids Res. 2003 Jan 1;31(1):348-52 12520020 Algorithms Animals Cluster Analysis Genomics Mice Protein Interaction Maps Racemases and Epimerases genetics Saccharomyces cerevisiae enzymology genetics Software PMC3262844 2011 8 8 2011 11 9 2011 11 9 2011 11 11 6 0 2011 11 11 6 0 2012 5 15 6 0 epublish 1471-2105-12-436 10.1186/1471-2105-12-436 22070249 PMC3262844 22035796 2011 10 31 2012 04 02 2014 10 21

1879-1301 18 10 2011 Oct 28 Chem. Biol. Compound prioritization methods increase rates of chemical probe discovery in model organisms. 1273-83 10.1016/j.chembiol.2011.07.018 Preselection of compounds that are more likely to induce a phenotype can increase the efficiency and reduce the costs for model organism screening. To identify such molecules, we screened ~81,000 compounds in Saccharomyces cerevisiae and identified ~7500 that inhibit cell growth. Screening these growth-inhibitory molecules across a diverse panel of model organisms resulted in an increased phenotypic hit-rate. These data were used to build a model to predict compounds that inhibit yeast growth. Empirical and in silico application of the model enriched the discovery of bioactive compounds in diverse model organisms. To demonstrate the potential of these molecules as lead chemical probes, we used chemogenomic profiling in yeast and identified specific inhibitors of lanosterol synthase and of stearoyl-CoA 9-desaturase. As community resources, the ~7500 growth-inhibitory molecules have been made commercially available and the computational model and filter used are provided. Copyright © 2011 Elsevier Ltd. All rights reserved. Wallace Iain M IM Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada. Urbanus Malene L ML Luciani Genna M GM Burns Andrew R AR Han Mitchell K L MK Wang Hao H Arora Kriti K Heisler Lawrence E LE Proctor Michael M St Onge Robert P RP Roemer Terry T Roy Peter J PJ Cummins Carolyn L CL Bader Gary D GD Nislow Corey C Giaever Guri G eng MOP-68813 Canadian Institutes of Health Research Canada MOP-97904 Canadian Institutes of Health Research Canada MOPS-81340 Canadian Institutes of Health Research Canada MOPS-84305 Canadian Institutes of Health Research Canada R01 HG003317 HG NHGRI NIH HHS United States R01 HG003317-02 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

United States Chem Biol 9500160 1074-5521 0 Benzofurans 0 ERG7.153 0 Enzyme Inhibitors 0 Piperazines 0 Small Molecule Libraries EC 1.14.19.- Fatty Acid Desaturases EC 1.14.99.- delta-9 fatty acid desaturase EC 5.4.- Intramolecular Transferases EC 5.4.99.7 lanosterol synthase IM J Biol Chem. 2009 Jul 3;284(27):17968-74 19416971 Cancer Cell. 2008 May;13(5):454-63 18455128 Bioorg Med Chem. 2008 Jun 1;16(11):6218-32 18467104 Nat Methods. 2008 Aug;5(8):719-25 18622398 Drug Discov Today. 2009 Dec;14(23-24):1150-8 19793546 Nucleic Acids Res. 2010 Jan;38(Database issue):D255-66 19933261 FEBS J. 2010 Feb;277(3):540-9 19961541 Proc Natl Acad Sci U S A. 2010 Mar 2;107(9):4004-9 20142500 OMICS. 2010 Apr;14(2):211-27 20337531 Nature. 2010 May 20;465(7296):305-10 20485427 Nat Chem Biol. 2010 Jul;6(7):549-57 20512140 Chem Biol. 2010 Jun 25;17(6):561-77 20609406 Mycoses. 2010 Nov;53(6):481-7 19549106 Appl Environ Microbiol. 2011 Mar;77(5):1770-6 21193664 Nat Rev Drug Discov. 2011 Mar;10(3):197-208 21358739 Curr Pharm Des. 2011;17(4):325-31 21375498 Methods Mol Biol. 2011;759:239-69 21863492 J Biol Chem. 2009 Jul 17;284(29):19754-64 19487691 Nat Cell Biol. 1999 Dec;1(8):E201-2 10587658 Proc Natl Acad Sci U S A. 2000 Jun 6;97(12):6640-5 10829079 Yeast. 2001 Jan 30;18(2):163-72 11169758 Adv Drug Deliv Rev. 2001 Mar 1;46(1-3):3-26 11259830 J Mol Microbiol Biotechnol. 2001 Apr;3(2):207-14 11321575 Curr Opin Cell Biol. 2002 Apr;14(2):155-9 11891113 Proc Natl Acad Sci U S A. 2002 Aug 20;99(17):11482-6 12177411 J Chem Inf Comput Sci. 2002 Nov-Dec;42(6):1273-80 12444722 Antimicrob Agents Chemother. 2003 Feb;47(2):676-81 12543677 Med Res Rev. 2003 May;23(3):302-21 12647312 Mol Microbiol. 2003 Oct;50(1):167-81 14507372 Cell. 2004 Jan 9;116(1):121-37 14718172 Proc Natl Acad Sci U S A. 2004 Jan 20;101(3):793-8 14718668 Proc Natl Acad Sci U S A. 2004 Mar 30;101(13):4525-30 15070751 Chem Biol. 2004 Oct;11(10):1423-30 15489169 J Biol Chem. 2004 Oct 22;279(43):45226-34 15316012 Science. 1988 Jun 10;240(4858):1439-43 3287619 Klin Wochenschr. 1988 May 16;66(10):421-7 3294493 Biochem Soc Trans. 1990 Feb;18(1):47-8 2185085 Science. 1993 Nov 26;262(5138):1444-8 8248783 Lipids. 1995 Mar;30(3):221-6 7791529 J Bacteriol. 1998 Aug;180(16):4177-83 9696767 Appl Environ Microbiol. 1998 Oct;64(10):4047-52 9758839 Microb Drug Resist. 1998 Fall;4(3):143-58 9818966 Nat Genet. 1999 Mar;21(3):278-83 10080179 Science. 2004 Nov 12;306(5699):1138-9 15542455 Nature. 2004 Dec 16;432(7019):855-61 15602551 J Chem Inf Model. 2005 Jan-Feb;45(1):177-82 15667143 Antimicrob Agents Chemother. 2005 Jun;49(6):2558-60 15917573 Curr Biol. 2005 Jun 7;15(11):1070-6 15936280 Biochim Biophys Acta. 2005 Jun 30;1741(1-2):11-24 15919184 J Biomol Screen. 2005 Oct;10(7):682-6 16170046 Cell. 2005 Nov 4;123(3):507-19 16269340 Nature. 2006 May 4;441(7089):91-5 16672971 PLoS Genet. 2005 Aug;1(2):e24 16121259 PLoS Genet. 2006 Jul;2(7):e108 16839188 Drugs. 2007;67(1):11-6 17209661 Nat Protoc. 2006;1(3):1112-6 17406391 PLoS Pathog. 2007 Mar;3(3):e24 17352532 PLoS Pathog. 2007 Jun;3(6):e92 17604452 Eukaryot Cell. 2007 Nov;6(11):2102-11 17873082 Nat Protoc. 2007;2(11):2958-74 18007632 Ecotoxicology. 2008 Apr;17(3):165-71 17978872 Science. 2008 Apr 18;320(5874):362-5 18420932 PLoS Genet. 2008;4(8):e1000151 18688276 Bioinformatics. 2008 Nov 1;24(21):2518-25 18784118 Proc Natl Acad Sci U S A. 2008 Dec 30;105(52):20740-5 19074277 Dev Dyn. 2009 Jun;238(6):1287-308 19441060 Nat Chem Biol. 2009 Jul;5(7):441-7 19536101 Chem Biol. 2011 Oct 28;18(10):1204-5 22035786 Bacillus subtilis drug effects growth & development Bayes Theorem Benzofurans chemistry metabolism pharmacology Candida albicans drug effects growth & development Computer Simulation Enzyme Inhibitors chemistry pharmacology Escherichia coli drug effects growth & development Fatty Acid Desaturases antagonists & inhibitors metabolism HeLa Cells Humans Intramolecular Transferases antagonists & inhibitors metabolism Models, Biological Phenotype Piperazines chemistry metabolism pharmacology Saccharomyces cerevisiae chemistry growth & development metabolism Small Molecule Libraries NIHMS330010 PMC4193902 2010 11 22 2011 6 29 2011 7 15 2011 11 1 6 0 2011 11 1 6 0 2012 4 3 6 0 ppublish S1074-5521(11)00276-6 10.1016/j.chembiol.2011.07.018 22035796 PMC4193902 NIHMS330010 21959865 2011 09 30 2012 01 18 2015 02 09

1758-0463 2011 2011 Database (Oxford) NetSlim: high-confidence curated signaling maps. bar032 10.1093/database/bar032 We previously developed NetPath as a resource for comprehensive manually curated signal transduction pathways. The pathways in NetPath contain a large number of molecules and reactions which can sometimes be difficult to visualize or interpret given their complexity. To overcome this potential limitation, we have developed a set of more stringent curation and inclusion criteria for pathway reactions to generate high-confidence signaling maps. NetSlim is a new resource that contains this 'core' subset of reactions for each pathway for easy visualization and manipulation. The pathways in NetSlim are freely available at http://www.netpath.org/netslim. Raju Rajesh R Institute of Bioinformatics, International Tech Park, Bangalore, India. Nanjappa Vishalakshi V Balakrishnan Lavanya L Radhakrishnan Aneesha A Thomas Joji Kurian JK Sharma Jyoti J Tian Maozhen M Palapetta Shyam Mohan SM Subbannayya Tejaswini T Sekhar Nirujogi Raja NR Muthusamy Babylakshmi B Goel Renu R Subbannayya Yashwanth Y Telikicherla Deepthi D Bhattacharjee Mitali M Pinto Sneha M SM Syed Nazia N Srikanth Manda Srinivas MS Sathe Gajanan J GJ Ahmad Sartaj S Chavan Sandip N SN Kumar Ghantasala S Sameer GS Marimuthu Arivusudar A Prasad T S K TS Harsha H C HC Rahiman B Abdul BA Ohara Osamu O Bader Gary D GD Sujatha Mohan S S Schiemann William P WP Pandey Akhilesh A eng Journal Article Research Support, Non-U.S. Gov't 2011 09 29

England Database (Oxford) 101517697 1758-0463 0 Transforming Growth Factor beta IM Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9 18832364 EMBO J. 2007 Sep 5;26(17):3957-67 17673906 Future Oncol. 2009 Mar;5(2):259-71 19284383 Nat Immunol. 2009 Apr;10(4):327-31 19295628 Nat Cell Biol. 2009 Jul;11(7):881-9 19543271 Nat Biotechnol. 2009 Aug;27(8):735-41 19668183 Bioinformatics. 2009 Nov 1;25(21):2860-2 19628504 Methods Enzymol. 2009;467:281-306 19897097 BMC Bioinformatics. 2009;10:370 19895694 Breast Cancer Res. 2009;11(5):R68 19740433 Bioinformatics. 2010 Feb 1;26(3):429-31 20007251 Genome Biol. 2010;11(1):R3 20067622 Nucleic Acids Res. 2010 Jul;38(Web Server issue):W150-4 20460470 Nat Biotechnol. 2010 Sep;28(9):935-42 20829833 Cell. 1992 Dec 11;71(6):1003-14 1333888 Mol Cell Biol. 1993 Dec;13(12):7239-47 8246946 Nature. 1994 Aug 4;370(6488):341-7 8047140 Cell Mol Biol (Noisy-le-grand). 1994 May;40(3):337-49 7920178 Proc Natl Acad Sci U S A. 1995 Feb 28;92(5):1565-9 7878020 Cell. 1997 Jun 27;89(7):1165-73 9215638 EMBO J. 1997 Sep 1;16(17):5353-62 9311995 Nature. 1997 Oct 9;389(6651):631-5 9335507 J Biol Chem. 1997 Oct 31;272(44):27678-85 9346908 Cell. 1998 Dec 11;95(6):779-91 9865696 J Biol Chem. 2005 Mar 18;280(11):10870-6 15657037 N Engl J Med. 2000 May 4;342(18):1350-8 10793168 Mol Cell Biol. 2000 May;20(9):3157-67 10757800 IEEE Trans Vis Comput Graph. 2005 Jul-Aug;11(4):443-56 16138554 Mol Cell Biol. 2000 Dec;20(24):9346-55 11094085 EMBO J. 2001 Jun 1;20(11):2789-801 11387212 J Biol Chem. 2001 Jul 6;276(27):24627-37 11323414 Ann N Y Acad Sci. 2002 Oct;971:585-7 12438188 J Cell Sci. 2003 Jan 15;116(Pt 2):217-24 12482908 Biol Cell. 2002 Nov;94(7-8):535-43 12566226 Oncogene. 2003 Jul 10;22(28):4314-32 12853969 Mol Biol Cell. 2003 Oct;14(10):3977-88 14517312 Genome Res. 2003 Nov;13(11):2498-504 14597658 EMBO J. 2003 Dec 15;22(24):6458-70 14657019 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D277-80 14681412 Nat Biotechnol. 2004 Feb;22(2):177-83 14755292 BMC Cell Biol. 2004 Jan 19;5:2 14728725 Nature. 2004 Sep 9;431(7005):205-11 15356634 Cancer Metastasis Rev. 2005 Sep;24(3):395-402 16258727 Genes Dev. 2005 Dec 1;19(23):2783-810 16322555 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6 16381921 Future Oncol. 2006 Dec;2(6):743-63 17155901 Nucleic Acids Res. 2007 Jan;35(Database issue):D247-52 17130144 Breast Cancer Res. 2006;8(4):R42 16859511 Expert Rev Anticancer Ther. 2007 May;7(5):609-11 17492924 BMC Bioinformatics. 2007;8:217 17588266 J Cell Biol. 2007 Jul 30;178(3):437-51 17646396 Nucleic Acids Res. 2009 Jan;37(Database issue):D619-22 18981052 Database Management Systems Databases, Factual Internet Signal Transduction Transforming Growth Factor beta metabolism User-Computer Interface PMC3263596 2011 2011 10 1 6 0 2011 10 1 6 0 2012 1 19 6 0 epublish bar032 10.1093/database/bar032 21959865 PMC3263596 21877291 2011 08 30 2012 01 24 2013 09 19

1940-6029 781 2011 Methods Mol. Biol. Displaying chemical information on a biological network using Cytoscape. 363-76 10.1007/978-1-61779-276-2_18 Cytoscape is an open-source software package that is widely used to integrate and visualize diverse data sets in biology. This chapter explains how to use Cytoscape to integrate open-source chemical information with a biological network. By visualizing information about known compound-target interactions in the context of a biological network of interest, one can rapidly identify novel avenues to perturb the system with compounds and, for example, potentially identify therapeutically relevant targets. Herein, two different protocols are explained in detail, with no prior knowledge of Cytoscape assumed, which demonstrate how to incorporate data from the ChEMBL database with either a gene-gene or a protein-protein interaction network. ChEMBL is a very large, open-source repository of compound-target information available from the European Molecular Biology Laboratory. Wallace Iain M IM Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada. Bader Gary D GD Giaever Guri G Nislow Corey C eng MOPS-81340 Canadian Institutes of Health Research Canada MOPS-84305 Canadian Institutes of Health Research Canada P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't

United States Methods Mol Biol 9214969 1064-3745 IM Computational Biology methods Databases, Genetic Databases, Protein Gene Regulatory Networks drug effects Molecular Targeted Therapy Protein Interaction Maps drug effects Software 2011 8 31 6 0 2011 8 31 6 0 2012 1 25 6 0 ppublish 10.1007/978-1-61779-276-2_18 21877291 21877285 2011 08 30 2012 01 24 2013 09 19

1940-6029 781 2011 Methods Mol. Biol. Visualizing gene-set enrichment results using the Cytoscape plug-in enrichment map. 257-77 10.1007/978-1-61779-276-2_12 Gene-set enrichment analysis finds functionally coherent gene-sets, such as pathways, that are statistically overrepresented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of -gene-sets used by many current enrichment analysis resources work against this ideal. "Enrichment Map" is a Cytoscape plug-in that helps overcome gene-set redundancy and aids in the interpretation of enrichment results. Gene-sets are organized in a network, where each set is a node and links represent gene overlap between sets. Automated network layout groups related gene-sets into -network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret enrichment results. Merico Daniele D Banting and Best Department of Medical Research, Centre for Cellular and Biomolecular Research (CCBR), Toronto, ON, Canada. Isserlin Ruth R Bader Gary D GD eng P41 GM103504 GM NIGMS NIH HHS United States Journal Article

United States Methods Mol Biol 9214969 1064-3745 IM Computational Biology methods Databases, Genetic Gene Regulatory Networks Software 2011 8 31 6 0 2011 8 31 6 0 2012 1 25 6 0 ppublish 10.1007/978-1-61779-276-2_12 21877285 21840481 2011 08 15 2011 10 13 2014 10 22

1878-3686 20 2 2011 Aug 16 Cancer Cell Delineation of two clinically and molecularly distinct subgroups of posterior fossa ependymoma. 143-57 10.1016/j.ccr.2011.07.007 Despite the histological similarity of ependymomas from throughout the neuroaxis, the disease likely comprises multiple independent entities, each with a distinct molecular pathogenesis. Transcriptional profiling of two large independent cohorts of ependymoma reveals the existence of two demographically, transcriptionally, genetically, and clinically distinct groups of posterior fossa (PF) ependymomas. Group A patients are younger, have laterally located tumors with a balanced genome, and are much more likely to exhibit recurrence, metastasis at recurrence, and death compared with Group B patients. Identification and optimization of immunohistochemical (IHC) markers for PF ependymoma subgroups allowed validation of our findings on a third independent cohort, using a human ependymoma tissue microarray, and provides a tool for prospective prognostication and stratification of PF ependymoma patients. Copyright © 2011 Elsevier Inc. All rights reserved. Witt Hendrik H Division Molecular Genetics, German Cancer Research Center, 69120 Heidelberg, Germany. Mack Stephen C SC Ryzhova Marina M Bender Sebastian S Sill Martin M Isserlin Ruth R Benner Axel A Hielscher Thomas T Milde Till T Remke Marc M Jones David T W DT Northcott Paul A PA Garzia Livia L Bertrand Kelsey C KC Wittmann Andrea A Yao Yuan Y Roberts Stephen S SS Massimi Luca L Van Meter Tim T Weiss William A WA Gupta Nalin N Grajkowska Wiesia W Lach Boleslaw B Cho Yoon-Jae YJ von Deimling Andreas A Kulozik Andreas E AE Witt Olaf O Bader Gary D GD Hawkins Cynthia E CE Tabori Uri U Guha Abhijit A Rutka James T JT Lichter Peter P Korshunov Andrey A Taylor Michael D MD Pfister Stefan M SM eng P41 GM103504 GM NIGMS NIH HHS United States R01 CA121941 CA NCI NIH HHS United States Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't Validation Studies

United States Cancer Cell 101130617 1535-6108 IM J Clin Oncol. 2001 Mar 1;19(5):1288-96 11230470 Cancer Res. 2011 May 15;71(10):3447-52 21270108 Br J Cancer. 2002 Mar 18;86(6):929-39 11953826 Bioinformatics. 2002;18 Suppl 1:S96-104 12169536 J Neurooncol. 2002 Jul;58(3):255-70 12187959 Clin Cancer Res. 2002 Oct;8(10):3054-64 12374672 Am J Pathol. 2002 Dec;161(6):2133-41 12466129 Bioinformatics. 2004 Jan 1;20(1):93-9 14693814 Proc Natl Acad Sci U S A. 2004 Mar 23;101(12):4164-9 15016911 J Clin Oncol. 2004 Aug 1;22(15):3156-62 15284268 Genome Biol. 2004;5(10):R80 15461798 Genes Chromosomes Cancer. 1997 Dec;20(4):399-407 9408757 Med Pediatr Oncol. 1998 Jun;30(6):319-29; discussion 329-31 9589080 Am J Pathol. 1999 Aug;155(2):627-32 10433955 Acta Neuropathol. 2005 Feb;109(2):211-6 15614581 Genes Chromosomes Cancer. 2005 Jul;43(3):294-301 15834944 Cancer Cell. 2005 Oct;8(4):323-35 16226707 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 Clin Cancer Res. 2006 Apr 1;12(7 Pt 1):2070-9 16609018 Nat Genet. 2006 May;38(5):500-1 16642009 Pediatr Blood Cancer. 2007 Jul;49(1):34-40 16874765 Acta Neuropathol. 2007 Aug;114(2):97-109 17618441 PLoS One. 2007;2(11):e1195 18030330 Br J Cancer. 2008 Oct 7;99(7):1136-43 18781180 Lancet Oncol. 2009 Mar;10(3):258-66 19274783 J Clin Oncol. 2009 Apr 10;27(11):1884-92 19289631 Nature. 2009 May 7;459(7243):98-102 19305393 Mol Cancer Res. 2009 Jun;7(6):765-86 19531565 Science. 2009 Dec 4;326(5958):1406-10 19933103 Cancer Cell. 2010 Jan 19;17(1):98-110 20129251 Bioinformatics. 2010 Jun 15;26(12):1572-3 20427518 J Clin Oncol. 2010 Jul 1;28(19):3182-90 20516456 Nature. 2010 Jul 29;466(7306):632-6 20639864 PLoS One. 2010;5(11):e13984 21085593 Nature. 2010 Dec 23;468(7327):1095-9 21150899 Cancer Cell. 2011 Aug 16;20(2):133-4 21840477 Adult Brain Neoplasms classification genetics Chromosome Aberrations Cranial Fossa, Posterior pathology Ependymoma classification genetics Female Gene Expression Profiling Humans Male Middle Aged NIHMS622647 PMC4154494 2011 2 15 2011 5 30 2011 7 11 2011 8 16 6 0 2011 8 16 6 0 2011 10 14 6 0 ppublish S1535-6108(11)00262-5 10.1016/j.ccr.2011.07.007 21840481 PMC4154494 NIHMS622647 21742767 2011 07 11 2011 09 20 2015 02 09

1758-0463 2011 2011 Database (Oxford) A comprehensive manually curated reaction map of RANKL/RANK-signaling pathway. bar021 10.1093/database/bar021 Receptor activator of nuclear factor-kappa B ligand (RANKL) is a member of tumor necrosis factor (TNF) superfamily that plays a key role in the regulation of differentiation, activation and survival of osteoclasts and also in tumor cell migration and bone metastasis. Osteoclast activation induced by RANKL regulates hematopoietic stem cell mobilization as part of homeostasis and host defense mechanisms thereby linking regulation of hematopoiesis with bone remodeling. Binding of RANKL to its receptor, Receptor activator of nuclear factor-kappa B (RANK) activates molecules such as NF-kappa B, mitogen activated protein kinase (MAPK), nuclear factor of activated T cells (NFAT) and phosphatidyl 3-kinase (PI3K). Although the molecular and cellular roles of these molecules have been reported previously, a systematic cataloging of the molecular events induced by RANKL/RANK interaction has not been attempted. Here, we present a comprehensive reaction map of the RANKL/RANK-signaling pathway based on an extensive manual curation of the published literature. We hope that the curated RANKL/RANK-signaling pathway model would enable new biomedical discoveries, which can provide novel insights into disease processes and development of novel therapeutic interventions. Raju Rajesh R Institute of Bioinformatics, International Technology Park, Bangalore 560066, India. Balakrishnan Lavanya L Nanjappa Vishalakshi V Bhattacharjee Mitali M Getnet Derese D Muthusamy Babylakshmi B Kurian Thomas Joji J Sharma Jyoti J Rahiman B Abdul BA Harsha H C HC Shankar Subramanian S Prasad T S Keshava TS Mohan S Sujatha SS Bader Gary D GD Wani Mohan R MR Pandey Akhilesh A eng Wellcome Trust United Kingdom Journal Article Research Support, Non-U.S. Gov't 2011 07 08

England Database (Oxford) 101517697 1758-0463 0 RANK Ligand 0 Receptor Activator of Nuclear Factor-kappa B 0 TNFRSF11A protein, human 0 TNFSF11 protein, human IM Genome Res. 2003 Nov;13(11):2498-504 14597658 Nat Biotechnol. 2004 Feb;22(2):177-83 14755292 J Clin Invest. 2004 Aug;114(4):475-84 15314684 Cell. 1991 Feb 22;64(4):693-702 1997203 Cell. 1997 Apr 18;89(2):309-19 9108485 J Biol Chem. 2001 Jun 8;276(23):20659-72 11274143 J Mol Med (Berl). 2001 Jun;79(5-6):243-53 11485016 J Biol Chem. 2001 Aug 10;276(32):30011-7 11406619 J Clin Invest. 2001 Oct;108(7):971-9 11581298 Blood. 2001 Dec 15;98(13):3527-33 11739153 Mol Cell Biol. 2002 Feb;22(4):992-1000 11809792 J Biol Chem. 2002 Feb 22;277(8):6631-6 11733492 J Bone Miner Res. 2002 Apr;17(4):612-21 11918218 J Pathol. 2002 Oct;198(2):228-36 12237883 J Cell Sci. 2002 Nov 15;115(Pt 22):4317-25 12376563 J Clin Pathol. 2002 Nov;55(11):877-8 12401833 Blood. 2002 Nov 15;100(10):3646-55 12393586 J Biol Chem. 1997 Oct 3;272(40):25190-4 9312132 Nature. 1997 Nov 13;390(6656):175-9 9367155 J Exp Med. 1997 Dec 15;186(12):2075-80 9396779 Endocrinology. 1998 Mar;139(3):1329-37 9492069 Proc Natl Acad Sci U S A. 1998 Mar 31;95(7):3597-602 9520411 Cell. 1998 Apr 17;93(2):165-76 9568710 J Exp Med. 1998 Sep 7;188(5):997-1001 9730902 J Biol Chem. 1999 May 7;274(19):13613-8 10224132 J Biol Chem. 2004 Dec 24;279(52):54841-8 15485831 J Exp Med. 2005 May 16;201(10):1677-87 15897281 J Bone Miner Res. 2008 Jun;23(6):907-14 18251700 J Biol Chem. 2008 Nov 7;283(45):30861-7 18768464 Bioinformatics. 2009 Jan 15;25(2):288-9 19033274 Proc Natl Acad Sci U S A. 2009 Feb 17;106(7):2325-30 19171907 Keio J Med. 2009 Mar;58(1):29-40 19398882 Cancer Biol Ther. 2009 Jan;8(1):36-46 18981721 Bioinformatics. 2009 Nov 1;25(21):2860-2 19628504 Mol Oncol. 2009 Dec;3(5-6):439-50 19632164 Nature. 2009 Nov 26;462(7272):505-9 19940926 J Cell Biochem. 2009 Dec 15;108(6):1337-45 19830703 Methods Mol Biol. 2010;604:309-18 20013380 Bioinformatics. 2010 Feb 1;26(3):429-31 20007251 Genome Biol. 2010;11(1):R3 20067622 J Immunol. 2010 Jun 15;184(12):6910-9 20483727 Nucleic Acids Res. 2010 Jul;38(Web Server issue):W150-4 20460470 Nat Biotechnol. 2010 Sep;28(9):935-42 20829833 Nucleic Acids Res. 2009 Jan;37(Database issue):D767-72 18988627 Bone. 1999 Nov;25(5):525-34 10574572 Mol Cell. 1999 Dec;4(6):1041-9 10635328 Bone. 2000 Jul;27(1):29-40 10865206 J Biol Chem. 2000 Oct 6;275(40):31155-61 10859303 Cell. 2000 Sep 29;103(1):41-50 11051546 J Biol Chem. 2001 May 4;276(18):14665-74 11278735 J Clin Invest. 2005 Jul;115(7):1848-54 15937549 J Biol Chem. 2005 Dec 16;280(50):41700-6 16234249 Mol Cell Biol. 2006 Feb;26(3):1002-13 16428453 Mol Syst Biol. 2005;1:2005.0010 16729045 Mol Syst Biol. 2006;2:2006.0015 16738560 Proc Natl Acad Sci U S A. 2006 Jun 27;103(26):9773-8 16785428 J Exp Med. 2007 Jun 11;204(6):1267-72 17502664 J Biol Chem. 2007 Jun 22;282(25):18245-53 17485464 BMC Bioinformatics. 2007;8:217 17588266 Nat Genet. 2007 Aug;39(8):960-2 17632511 J Immunol. 2007 Dec 1;179(11):7514-22 18025196 Proc Natl Acad Sci U S A. 2008 Mar 11;105(10):3897-902 18322009 J Clin Invest. 2008 May;118(5):1858-66 18382763 J Clin Invest. 2008 Jun;118(6):2088-97 18464930 J Immunol. 2003 Feb 15;170(4):1797-805 12574344 Bioinformatics. 2003 Mar 1;19(4):524-31 12611808 J Biol Chem. 2003 Jun 20;278(25):22331-40 12682046 J Immunol. 2003 Oct 15;171(8):4121-30 14530334 Am J Pathol. 2003 Nov;163(5):2021-31 14578201 Database Management Systems Databases, Factual Humans RANK Ligand genetics metabolism Receptor Activator of Nuclear Factor-kappa B genetics metabolism Signal Transduction PMC3170171 2011 2011 7 12 6 0 2011 7 12 6 0 2011 9 21 6 0 epublish bar021 10.1093/database/bar021 21742767 PMC3170171 21716279 2011 06 30 2011 09 09 2014 10 22

1548-7105 8 7 2011 Jul Nat. Methods PSICQUIC and PSISCORE: accessing and scoring molecular interactions. 528-9 10.1038/nmeth.1637 Aranda Bruno B Blankenburg Hagen H Kerrien Samuel S Brinkman Fiona S L FS Ceol Arnaud A Chautard Emilie E Dana Jose M JM De Las Rivas Javier J Dumousseau Marine M Galeota Eugenia E Gaulton Anna A Goll Johannes J Hancock Robert E W RE Isserlin Ruth R Jimenez Rafael C RC Kerssemakers Jules J Khadake Jyoti J Lynn David J DJ Michaut Magali M O'Kelly Gavin G Ono Keiichiro K Orchard Sandra S Prieto Carlos C Razick Sabry S Rigina Olga O Salwinski Lukasz L Simonovic Milan M Velankar Sameer S Winter Andrew A Wu Guanming G Bader Gary D GD Cesareni Gianni G Donaldson Ian M IM Eisenberg David D Kleywegt Gerard J GJ Overington John J Ricard-Blum Sylvie S Tyers Mike M Albrecht Mario M Hermjakob Henning H eng 086151 Wellcome Trust United Kingdom R01 GM071909 GM NIGMS NIH HHS United States R01 OD010929 OD NIH HHS United States R01 RR024031 RR NCRR NIH HHS United States R01 RR024031-05 RR NCRR NIH HHS United States Letter Research Support, Non-U.S. Gov't 2011 06 29

United States Nat Methods 101215604 1548-7091 0 Proteins IM BMC Biol. 2007;5:44 17925023 Bioinformatics. 2009 May 15;25(10):1321-8 19420069 Proteomics. 2007 Oct;7(19):3436-40 17907277 Nat Biotechnol. 2004 Feb;22(2):177-83 14755292 Genome Res. 2003 Nov;13(11):2498-504 14597658 Animals Computational Biology Databases, Factual Humans Protein Binding Proteins chemistry metabolism Software NIHMS345023 PMC3246345 2011 7 1 6 0 2011 7 1 6 0 2011 9 10 6 0 epublish nmeth.1637 10.1038/nmeth.1637 21716279 PMC3246345 NIHMS345023 21525870 2011 04 28 2011 09 08 2014 08 20

1744-4292 7 2011 Apr 26 Mol. Syst. Biol. The multiple-specificity landscape of modular peptide recognition domains. 484 10.1038/msb.2011.18 Modular protein interaction domains form the building blocks of eukaryotic signaling pathways. Many of them, known as peptide recognition domains, mediate protein interactions by recognizing short, linear amino acid stretches on the surface of their cognate partners with high specificity. Residues in these stretches are usually assumed to contribute independently to binding, which has led to a simplified understanding of protein interactions. Conversely, we observe in large binding peptide data sets that different residue positions display highly significant correlations for many domains in three distinct families (PDZ, SH3 and WW). These correlation patterns reveal a widespread occurrence of multiple binding specificities and give novel structural insights into protein interactions. For example, we predict a new binding mode of PDZ domains and structurally rationalize it for DLG1 PDZ1. We show that multiple specificity more accurately predicts protein interactions and experimentally validate some of the predictions for the human proteins DLG1 and SCRIB. Overall, our results reveal a rich specificity landscape in peptide recognition domains, suggesting new ways of encoding specificity in protein interaction networks. Gfeller David D Banting and Best Department of Medical Research, The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. Butty Frank F Wierzbicka Marta M Verschueren Erik E Vanhee Peter P Huang Haiming H Ernst Andreas A Dar Nisa N Stagljar Igor I Serrano Luis L Sidhu Sachdev S SS Bader Gary D GD Kim Philip M PM eng MOP-84324 Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't

England Mol Syst Biol 101235389 1744-4292 0 Adaptor Proteins, Signal Transducing 0 DLG1 protein, human 0 Membrane Proteins 0 SCRIB protein, human 0 Tumor Suppressor Proteins IM PLoS Biol. 2009 Oct;7(10):e1000218 19841731 Science. 2009 Jun 26;324(5935):1720-3 19443739 Nucleic Acids Res. 2010 Jan;38(Database issue):D167-80 19920119 J Mol Biol. 1999 Dec 17;294(5):1351-62 10600390 J Mol Biol. 2000 Jul 21;300(4):1005-16 10891285 J Cell Sci. 2001 Apr;114(Pt 7):1253-63 11256992 J Cell Sci. 2001 Sep;114(Pt 18):3219-31 11591811 Science. 2002 Jan 11;295(5553):321-4 11743162 Science. 2003 Apr 18;300(5618):445-52 12702867 Nucleic Acids Res. 2003 Jul 1;31(13):3635-41 12824383 Proteomics. 2004 Mar;4(3):643-55 14997488 Nucleic Acids Res. 2004;32(5):1792-7 15034147 J Mol Biol. 2004 Oct 22;343(3):703-18 15465056 Nature. 1994 Nov 24;372(6504):375-9 7802869 Science. 1994 Nov 18;266(5188):1241-7 7526465 Proc Int Conf Intell Syst Mol Biol. 1994;2:28-36 7584402 Cell. 1996 Mar 8;84(5):757-67 8625413 Cell. 1996 Jun 28;85(7):1067-76 8674113 Proc Natl Acad Sci U S A. 1997 May 13;94(10):4937-42 9144168 Bioinformatics. 1998;14(2):121-30 9545443 Protein Eng. 1999 Jan;12(1):3-9 10065704 Science. 1999 Apr 30;284(5415):812-5 10221915 Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W382-8 15980494 Protein Sci. 2007 Apr;16(4):683-94 17384233 Nat Protoc. 2007;2(6):1368-86 17545975 Nat Med. 2007 Jul;13(7):862-7 17589520 Sci Signal. 2009;2(102):ra84 20029029 Acta Crystallogr Sect F Struct Biol Cryst Commun. 2009 Dec 1;65(Pt 12):1254-7 20054121 Sci Signal. 2010;3(104):ra3 20068231 Protein Sci. 2010 Apr;19(4):731-41 20120020 J Biol Chem. 2010 Jun 4;285(23):18051-9 20356847 Nat Protoc. 2010 Jul;5(7):1281-93 20595957 Ann Hum Genet. 2010 Jul;74(4):326-34 20597903 Nat Methods. 2010 Sep;7(9):741-6 20711194 Nat Struct Mol Biol. 2004 Nov;11(11):1122-7 15475968 Bioinformatics. 2005 Mar15;21(6):827-8 15513994 Nat Methods. 2004 Oct;1(1):27-9 15782149 Bioinformatics. 2005 Jun;21 Suppl 1:i204-12 15961459 Mol Biosyst. 2010 Oct;6(10):1782-90 20714644 J Mol Biol. 2010 Sep 17;402(2):460-74 20654621 BMC Bioinformatics. 2010;11:507 20939902 Bioinformatics. 2011 Feb 1;27(3):383-90 21127034 Science. 2007 Jul 20;317(5836):364-9 17641200 Proc Natl Acad Sci U S A. 2007 Aug 21;104(34):13591-6 17690244 J Mol Biol. 2007 Oct 19;373(2):503-19 17825317 J Biol Chem. 2008 Jan 25;283(4):1974-84 18048362 PLoS Comput Biol. 2008;4(8):e1000154 18725950 Sci Signal. 2008;1(35):ra2 18765831 Nat Biotechnol. 2008 Sep;26(9):1041-5 18711339 PLoS Biol. 2008 Sep 30;6(9):e239 18828675 Nucleic Acids Res. 2010 Jan;38(Database issue):D525-31 19850723 Adaptor Proteins, Signal Transducing chemistry metabolism Amino Acid Sequence Animals Binding Sites Cluster Analysis Humans Membrane Proteins chemistry metabolism Mice Models, Molecular Molecular Sequence Data PDZ Domains Protein Binding Protein Interaction Mapping Signal Transduction Systems Biology Tumor Suppressor Proteins chemistry metabolism src Homology Domains PMC3097085 2010 9 27 2011 3 11 2011 4 29 6 0 2011 4 29 6 0 2011 9 9 6 0 ppublish msb201118 10.1038/msb.2011.18 21525870 PMC3097085 21473782 2011 04 28 2011 07 14 2013 09 19

1751-0473 6 2011 Source Code Biol Med WordCloud: a Cytoscape plugin to create a visual semantic summary of networks. 7 10.1186/1751-0473-6-7 When biological networks are studied, it is common to look for clusters, i.e. sets of nodes that are highly inter-connected. To understand the biological meaning of a cluster, the user usually has to sift through many textual annotations that are associated with biological entities. The WordCloud Cytoscape plugin generates a visual summary of these annotations by displaying them as a tag cloud, where more frequent words are displayed using a larger font size. Word co-occurrence in a phrase can be visualized by arranging words in clusters or as a network. WordCloud provides a concise visual summary of annotations which is helpful for network analysis and interpretation. WordCloud is freely available at http://baderlab.org/Software/WordCloudPlugin. Oesper Layla L Department of Computer Science, Brown University, Providence, RI, USA. layla@cs.brown.edu. Merico Daniele D Isserlin Ruth R Bader Gary D GD eng P41 GM103504 GM NIGMS NIH HHS United States Journal Article 2011 04 07

England Source Code Biol Med 101276533 1751-0473 Nat Genet. 2000 May;25(1):25-9 10802651 Genome Res. 2003 Nov;13(11):2498-504 14597658 Nature. 2006 Mar 30;440(7084):637-43 16554755 Brief Bioinform. 2008 May;9(3):189-97 18202032 PLoS One. 2010;5(11):e13984 21085593 Nat Biotechnol. 2009 Oct;27(10):921-4 19816451 Bioinformatics. 2010 Feb 15;26(4):456-63 20007254 Proteomics. 2010 Mar;10(6):1316-27 20127684 Bioinformatics. 2009 Apr 15;25(8):1091-3 19237447 PMC3083346 2011 2 23 2011 4 7 2011 4 7 2011 4 9 6 0 2011 4 9 6 0 2011 4 9 6 1 epublish 1751-0473-6-7 10.1186/1751-0473-6-7 21473782 PMC3083346 21390331 2011 03 10 2011 06 22 2014 08 21

1553-7358 7 2 2011 Feb PLoS Comput. Biol. Protein complexes are central in the yeast genetic landscape. e1001092 10.1371/journal.pcbi.1001092 If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ∼5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available. Michaut Magali M The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. Baryshnikova Anastasia A Costanzo Michael M Myers Chad L CL Andrews Brenda J BJ Boone Charles C Bader Gary D GD eng P41 GM103504 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't 2011 02 24

United States PLoS Comput Biol 101238922 1553-734X 0 Saccharomyces cerevisiae Proteins IM Nat Genet. 2000 May;25(1):25-9 10802651 Genome Res. 2003 Nov;13(11):2498-504 14597658 Science. 2001 Dec 14;294(5550):2364-8 11743205 Nature. 2002 Jul 25;418(6896):387-91 12140549 Nat Genet. 2003 Nov;35(3):204-5 14593402 Science. 2004 Feb 6;303(5659):808-13 14764870 Nucleic Acids Res. 2004;32(18):5539-45 15486203 Nucleic Acids Res. 1993 Jul 11;21(14):3329-30 8341614 Nat Genet. 2005 Jan;37(1):77-83 15592468 Nat Biotechnol. 2005 May;23(5):561-6 15877074 Trends Genet. 2005 Aug;21(8):424-7 15982781 Genome Res. 2005 Oct;15(10):1456-61 16169922 Cell. 2005 Nov 4;123(3):507-19 16269340 Mol Syst Biol. 2005;1:2005.0026 16729061 Nat Genet. 2006 Aug;38(8):896-903 16845399 BMC Genomics. 2006;7:187 16869964 Methods. 2007 Feb;41(2):206-21 17189863 Genome Biol. 2006;7(7):R63 16859555 Mol Syst Biol. 2007;3:104 17437029 Nat Rev Genet. 2007 Jun;8(6):437-49 17510664 BMC Bioinformatics. 2007;8:236 17605818 Nucleic Acids Res. 2008 Jan;36(Database issue):D577-81 17982175 Proc Natl Acad Sci U S A. 2008 Mar 4;105(9):3461-6 18305163 PLoS Comput Biol. 2008 Apr;4(4):e1000065 18421374 Science. 2008 Apr 18;320(5874):362-5 18420932 Genome Res. 2008 Jul;18(7):1092-9 18463300 Mol Syst Biol. 2008;4:209 18628749 Nat Methods. 2008 Aug;5(8):711-8 18622397 Bioinformatics. 2008 Oct 15;24(20):2376-83 18718945 Genome Biol. 2008;9(9):R135 18789146 Nucleic Acids Res. 2009 Jan;37(Database issue):D555-9 18948289 Nucleic Acids Res. 2009 Feb;37(3):825-31 19095691 Cell. 2009 Mar 6;136(5):952-63 19269370 PLoS One. 2009;4(4):e5364 19399174 FEBS Lett. 2009 Jun 5;583(11):1656-61 19351535 Mol Biosyst. 2009 Dec;5(12):1473-81 19763324 J Cell Biol. 2010 Jan 11;188(1):69-81 20065090 J Biol. 2007;6(3):8 17897480 Science. 2010 Jan 22;327(5964):425-31 20093466 Bioinformatics. 2010 Jun 15;26(12):i228-36 20529911 Mol Cell. 2010 Jun 25;38(6):916-28 20620961 Nat Methods. 2010 Dec;7(12):1017-24 21076421 Science. 2001 Feb 9;291(5506):1001-4 11232561 Computational Biology Gene Regulatory Networks Genes, Fungal Models, Biological Phenotype Saccharomyces cerevisiae genetics metabolism physiology Saccharomyces cerevisiae Proteins genetics metabolism physiology Signal Transduction PMC3044758 2010 8 1 2011 1 25 2011 2 24 2011 3 11 6 0 2011 3 11 6 0 2011 6 23 6 0 ppublish 10.1371/journal.pcbi.1001092 21390331 PMC3044758 21324131 2011 10 20 2012 02 15 2015 08 04

1474-760X 12 2 2011 Genome Biol. Bringing order to protein disorder through comparative genomics and genetic interactions. R14 10.1186/gb-2011-12-2-r14 Intrinsically disordered regions are widespread, especially in proteomes of higher eukaryotes. Recently, protein disorder has been associated with a wide variety of cellular processes and has been implicated in several human diseases. Despite its apparent functional importance, the sheer range of different roles played by protein disorder often makes its exact contribution difficult to interpret. We attempt to better understand the different roles of disorder using a novel analysis that leverages both comparative genomics and genetic interactions. Strikingly, we find that disorder can be partitioned into three biologically distinct phenomena: regions where disorder is conserved but with quickly evolving amino acid sequences (flexible disorder); regions of conserved disorder with also highly conserved amino acid sequences (constrained disorder); and, lastly, non-conserved disorder. Flexible disorder bears many of the characteristics commonly attributed to disorder and is associated with signaling pathways and multi-functionality. Conversely, constrained disorder has markedly different functional attributes and is involved in RNA binding and protein chaperones. Finally, non-conserved disorder lacks clear functional hallmarks based on our analysis. Our new perspective on protein disorder clarifies a variety of previous results by putting them into a systematic framework. Moreover, the clear and distinct functional association of flexible and constrained disorder will allow for new approaches and more specific algorithms for disorder detection in a functional context. Finally, in flexible disordered regions, we demonstrate clear evolutionary selection of protein disorder with little selection on primary structure, which has important implications for sequence-based studies of protein structure and evolution. Bellay Jeremy J Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, USA. Han Sangjo S Michaut Magali M Kim Taehyung T Costanzo Michael M Andrews Brenda J BJ Boone Charles C Bader Gary D GD Myers Chad L CL Kim Philip M PM eng 1R01HG005084-01A1 HG NHGRI NIH HHS United States P41 GM103504 GM NIGMS NIH HHS United States R01 HG005084 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. 2011 02 16

England Genome Biol 100960660 1474-7596 0 Proteins IM J Mol Biol. 2003 Jul 25;330(5):979-92 12860121 Proc Natl Acad Sci U S A. 2002 Apr 30;99(9):5860-5 11972065 Genome Res. 2003 Nov;13(11):2498-504 14597658 Structure. 2003 Nov;11(11):1453-9 14604535 Nucleic Acids Res. 2004;32(3):1037-49 14960716 J Mol Biol. 2004 Mar 26;337(3):635-45 15019783 Nature. 2004 Jul 1;430(6995):88-93 15190252 FASEB J. 2004 Aug;18(11):1169-75 15284216 BMC Bioinformatics. 2004 Aug 19;5:113 15318951 Cell. 1998 Nov 25;95(5):717-28 9845373 Nucleic Acids Res. 2005;33(2):511-8 15661851 Nat Rev Mol Cell Biol. 2005 Mar;6(3):197-208 15738986 Mol Cell Proteomics. 2005 Mar;4(3):310-27 15665377 FEBS Lett. 2005 Jun 13;579(15):3346-54 15943980 Nature. 2006 Mar 30;440(7084):631-6 16429126 Nature. 2006 Mar 30;440(7084):637-43 16554755 J Proteome Res. 2006 Apr;5(4):879-87 16602695 J Proteome Res. 2006 Apr;5(4):888-98 16602696 Proc Natl Acad Sci U S A. 2006 May 30;103(22):8390-5 16717195 BMC Genomics. 2006;7:187 16869964 Bioinformatics. 2006 Dec 1;22(23):2890-7 17005538 Science. 2006 Dec 22;314(5807):1938-41 17185604 Biophys J. 2007 Mar 1;92(5):1439-56 17158572 Proc Natl Acad Sci U S A. 2007 Feb 13;104(7):2193-8 17287358 J Proteome Res. 2007 Mar;6(3):1190-7 17330950 Mol Biol Evol. 2007 Aug;24(8):1586-91 17483113 J Biol Chem. 2008 Mar 14;283(11):6886-96 18184659 Mol Cell. 2008 Mar 14;29(5):563-76 18342604 Mol Syst Biol. 2008;4:179 18364713 Science. 2008 Apr 18;320(5874):362-5 18420932 Mol Cell Proteomics. 2008 Jul;7(7):1389-96 18407956 BMC Genomics. 2008;9 Suppl 2:S1 18831774 Science. 2008 Oct 3;322(5898):104-10 18719252 Science. 2008 Nov 28;322(5906):1365-8 19039133 PLoS Biol. 2008 Nov 4;6(11):e264 18986213 Curr Opin Struct Biol. 2009 Feb;19(1):31-8 19157855 Nat Struct Mol Biol. 2009 Mar;16(3):287-93 19234467 Bioinformatics. 2009 May 1;25(9):1189-91 19151095 PLoS Comput Biol. 2009 Jun;5(6):e1000413 19521505 Biochem Cell Biol. 2010 Apr;88(2):167-74 20453919 Nat Biotechnol. 2002 Mar;20(3):301-5 11875433 Proteomics. 2010 Mar;10(6):1316-27 20127684 Mol Cell Biol. 2002 Oct;22(19):6750-8 12215532 Nucleic Acids Res. 2003 Jul 1;31(13):3635-41 12824383 Biochem Cell Biol. 2010 Apr;88(2):269-90 20453929 Mol Biol Evol. 2010 Sep;27(9):2027-37 20368267 PLoS Biol. 2009 Jun 16;7(6):e1000134 19547744 Cell. 2009 Jul 10;138(1):198-208 19596244 Proteins. 2009;77 Suppl 9:210-6 19774619 Science. 2010 Jan 22;327(5964):425-31 20093466 Bioinformatics. 2010 Mar 1;26(5):625-31 20081223 Proteins. 2003 Sep 1;52(4):573-84 12910457 Algorithms Amino Acid Sequence Conserved Sequence Databases, Protein Escherichia coli Evolution, Molecular Genomics methods Humans Models, Statistical Molecular Sequence Data Protein Folding Protein Structure, Tertiary genetics Proteins chemistry genetics Transcriptome PMC3188796 2010 9 29 2011 2 1 2011 2 16 2011 2 16 2011 2 18 6 0 2011 2 18 6 0 2012 2 16 6 0 ppublish gb-2011-12-2-r14 10.1186/gb-2011-12-2-r14 21324131 PMC3188796 21307913 2011 02 10 2011 02 28

1476-4687 470 7333 2011 Feb 10 Nature Too many roads not taken. 163-5 10.1038/470163a Edwards Aled M AM University of Toronto, Toronto, Ontario M5G 1L7, Canada. aled.edwards@utoronto.ca Isserlin Ruth R Bader Gary D GD Frye Stephen V SV Willson Timothy M TM Yu Frank H FH eng Journal Article

England Nature 0410462 0028-0836 0 Ion Channels 0 Receptors, Cytoplasmic and Nuclear EC 2.7.- Protein Kinases IM Bibliometrics Biomedical Research instrumentation methods statistics & numerical data trends Human Genome Project Humans Ion Channels Protein Kinases Receptors, Cytoplasmic and Nuclear 2011 2 11 6 0 2011 2 11 6 0 2011 3 1 6 0 ppublish 470163a 10.1038/470163a 21307913 21233089 2011 01 14 2011 05 03 2015 02 09

1758-0463 2011 2011 Database (Oxford) The Biomolecular Interaction Network Database in PSI-MI 2.5. baq037 10.1093/database/baq037 The Biomolecular Interaction Network Database (BIND) is a major source of curated biomolecular interactions, which has been unmaintained for the last few years, a trend which will eventually result in the loss of a significant amount of unique biomolecular interaction information, mostly as database identifiers become out of date. To help reverse this trend, we converted BIND to a standard format, Proteomics Standard Initiative-Molecular Interaction 2.5, starting from the last curated data release (from 2005) available in a custom XML format and made the core components (interactions and complexes) plus additional valuable curated information available for download (http://download.baderlab.org/BINDTranslation/). Major work during the conversion process was required to update out of date molecule identifiers resulting in a more comprehensive conversion of BIND, by measures including number of species and interactor types covered, than what is currently accessible elsewhere. This work also highlights issues of data modeling, controlled vocabulary adoption and data cleaning that can serve as a general case study on the future compatibility of interaction databases. Database URL: http://download.baderlab.org/BINDTranslation/ Isserlin Ruth R The Donnelly Centre, University of Toronto, ON, Canada. El-Badrawi Rashad A RA Bader Gary D GD eng P41 P41HG04118 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2011 01 12

England Database (Oxford) 101517697 1758-0463 IM Nat Genet. 2000 May;25(1):25-9 10802651 Bioinformatics. 2000 May;16(5):465-77 10871269 Nucleic Acids Res. 2001 Jan 1;29(1):242-5 11125103 Methods Biochem Anal. 2001;43:45-63 11449735 Tanpakushitsu Kakusan Koso. 2002 May;47(6):733-6 11995340 Biopolymers. 2001-2002;61(2):111-20 11987160 Nat Biotechnol. 2002 Oct;20(10):991-7 12355115 Nucleic Acids Res. 2003 Jan 1;31(1):248-50 12519993 Genome Res. 2003 Nov;13(11):2498-504 14597658 Nat Biotechnol. 2004 Feb;22(2):177-83 14755292 Bioinformatics. 2004 Oct 12;20(15):2466-70 15117749 Hum Genomics. 2004 Mar;1(3):229-33 15588483 Nucleic Acids Res. 2005 Jan 1;33(Database issue):D418-24 15608229 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D527-34 16381926 Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W298-302 16845013 Nucleic Acids Res. 2007 Jan;35(Database issue):D61-5 17130148 Nucleic Acids Res. 2007 Jan;35(Database issue):D16-20 17148479 Genome Biol. 2007;8(5):R95 17535438 Nat Biotechnol. 2007 Aug;25(8):894-8 17687370 Nat Biotechnol. 2007 Sep;25(9):971 17846617 Nucleic Acids Res. 2008 Jan;36(Database issue):D637-40 18000002 BMC Syst Biol. 2007;1:58 18078503 Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W372-6 18467421 Mol Syst Biol. 2008;4:218 18766178 BMC Bioinformatics. 2008;9:405 18823568 Nucleic Acids Res. 2009 Jan;37(Database issue):D412-6 18940858 Nucleic Acids Res. 2009 Jan;37(Database issue):D642-6 18978014 Nucleic Acids Res. 2009 Jan;37(Database issue):D657-60 18984619 Nucleic Acids Res. 2009 Jan;37(Database issue):D651-6 18988626 Nucleic Acids Res. 2009 Jan;37(Database issue):D767-72 18988627 J Med Chem. 2008 Nov 27;51(22):7021-40 18975926 BMC Genomics. 2009;10 Suppl 1:S16 19594875 Proteomics. 2009 Nov;9(22):5112-9 19834897 Nucleic Acids Res. 2010 Jan;38(Database issue):D142-8 19843607 Nucleic Acids Res. 2010 Jan;38(Database issue):D525-31 19850723 Nat Biotechnol. 2010 Sep;28(9):935-42 20829833 Animals Databases, Protein Humans Internet Protein Binding Proteomics standards Rats Reproducibility of Results Vocabulary, Controlled PMC3021793 2011 2011 1 15 6 0 2011 1 15 6 0 2011 5 4 6 0 epublish baq037 10.1093/database/baq037 21233089 PMC3021793 21127034 2011 02 01 2011 12 20 2014 08 21

1367-4811 27 3 2011 Feb 1 Bioinformatics A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequence. 383-90 10.1093/bioinformatics/btq657 Predicting protein interactions involving peptide recognition domains is essential for understanding the many important biological processes they mediate. It is important to consider the binding strength of these interactions to help us construct more biologically relevant protein interaction networks that consider cellular context and competition between potential binders. We developed a novel regression framework that considers both positive (quantitative) and negative (qualitative) interaction data available for mouse PDZ domains to quantitatively predict interactions between PDZ domains, a large peptide recognition domain family, and their peptide ligands using primary sequence information. First, we show that it is possible to learn from existing quantitative and negative interaction data to infer the relative binding strength of interactions involving previously unseen PDZ domains and/or peptides given their primary sequence. Performance was measured using cross-validated hold out testing and testing with previously unseen PDZ domain-peptide interactions. Second, we find that incorporating negative data improves quantitative interaction prediction. Third, we show that sequence similarity is an important prediction performance determinant, which suggests that experimentally collecting additional quantitative interaction data for underrepresented PDZ domain subfamilies will improve prediction. The Matlab code for our SemiSVR predictor and all data used here are available at http://baderlab.org/Data/PDZAffinity. Shao Xiaojian X Department of Applied Mathematics, College of Science, China Agricultural University, Beijing, 100083, China. Tan Chris S H CS Voss Courtney C Li Shawn S C SS Deng Naiyang N Bader Gary D GD eng MOP-84324 Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't 2010 12 02

England Bioinformatics 9808944 1367-4803 0 Ligands 0 Peptides 0 Proteins IM Nature. 2003 Dec 11;426(6967):676-80 14668868 Sci Signal. 2009;2(58):ra6 19224897 PLoS Biol. 2004 Jan;2(1):E14 14737190 Bioinformatics. 2000 Jan;16(1):16-23 10812473 Nat Biotechnol. 2001 Apr;19(4):348-53 11283593 Science. 2002 Jan 11;295(5553):321-4 11743162 Science. 2003 Apr 18;300(5618):445-52 12702867 Sci STKE. 2003 Apr 22;2003(179):RE7 12709532 Curr Opin Struct Biol. 2004 Apr;14(2):208-16 15093836 FEBS Lett. 2004 Jun 1;567(1):74-9 15165896 Science. 1999 Oct 8;286(5438):295-9 10514373 J Mol Biol. 2004 Nov 26;344(3):865-81 15533451 Proc Natl Acad Sci U S A. 2005 May 3;102(18):6395-400 15851683 Nature. 2006 Jan 12;439(7073):168-74 16273093 J Am Chem Soc. 2006 May 3;128(17):5913-22 16637659 Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W32-7 16845018 BMC Bioinformatics. 2006;7:182 16579851 Bioinformatics. 2006 Oct 1;22(19):2333-9 16870929 IEEE Trans Neural Netw. 2007 Jan;18(1):300-6 17278481 Science. 2007 Jul 20;317(5836):364-9 17641200 Nature. 2007 Oct 18;449(7164):851-61 17943122 Bioinformatics. 2008 Feb 1;24(3):358-66 18083718 PLoS Comput Biol. 2008 Apr;4(4):e1000052 18389064 Mol Cell Proteomics. 2008 Apr;7(4):768-84 17956856 Protein Sci. 2009 Aug;18(8):1609-19 19569188 Nucleic Acids Res. 2009 Aug;37(14):4629-41 19502496 Cell. 2009 Aug 21;138(4):774-86 19703402 Sci Signal. 2009;2(87):ra50 19738200 Trends Biochem Sci. 2009 Oct;34(10):471-82 19744855 PLoS Biol. 2009 Oct;7(10):e1000218 19841731 BMC Bioinformatics. 2010;11:144 20298601 PLoS Comput Biol. 2010 May;6(5):e1000789 20502673 J Mol Biol. 2010 Sep 17;402(2):460-74 20654621 PLoS Comput Biol. 2008;4(7):e1000107 18604266 Nat Biotechnol. 2008 Sep;26(9):1041-5 18711339 Biochemistry. 2008 Sep 23;47(38):10084-98 18754678 PLoS Biol. 2008 Sep 30;6(9):e239 18828675 Nat Biotechnol. 2008 Nov;26(11):1251-9 18953355 Bioinformatics. 2009 Jan 1;25(1):83-9 18996943 Proteomics. 2004 Mar;4(3):643-55 14997488 Animals Computational Biology methods Ligands Mice Models, Molecular Mutation PDZ Domains Peptides chemistry genetics metabolism Protein Binding Protein Structure, Tertiary Proteins chemistry metabolism Regression Analysis Reproducibility of Results PMC3031032 2010 12 2 2010 12 4 6 0 2010 12 4 6 0 2011 12 21 6 0 ppublish btq657 10.1093/bioinformatics/btq657 21127034 PMC3031032 21085593 2010 11 18 2011 04 27 2014 08 21

1932-6203 5 11 2010 PLoS ONE Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. e13984 10.1371/journal.pone.0013984 Gene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal. To overcome gene-set redundancy and help in the interpretation of large gene lists, we developed "Enrichment Map", a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results. Enrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http://baderlab.org/Software/EnrichmentMap/). Merico Daniele D Department of Molecular Genetics, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. Isserlin Ruth R Stueker Oliver O Emili Andrew A Bader Gary D GD eng P41P41HG04118 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2010 11 15

United States PLoS One 101285081 1932-6203 0 Estrogens IM Cancer Epidemiol Biomarkers Prev. 2006 Dec;15(12):2422-6 17164365 BMC Bioinformatics. 2006;7:426 17018143 Bioinformatics. 2007 Feb 15;23(4):401-7 17182697 Clin Cancer Res. 2007 Feb 15;13(4):1107-14 17317818 PLoS One. 2007;2(5):e425 17487280 Neoplasia. 2007 May;9(5):443-54 17534450 PLoS Genet. 2007 Jun;3(6):e87 17542648 Int J Colorectal Dis. 2007 Oct;22(10):1185-94 17483957 Cancer Lett. 2007 Oct 28;256(2):137-65 17629396 Nat Methods. 2007 Oct;4(10):787-97 17901868 Genome Res. 2007 Oct;17(10):1537-45 17785539 Nat Protoc. 2007;2(10):2366-82 17947979 Bioinformatics. 2007 Nov 15;23(22):3024-31 17848398 Genome Biol. 2007;8(7):R131 17615082 Genome Biol. 2007;8(9):R183 17784955 Adv Exp Med Biol. 2007;623:64-84 18380341 Nat Genet. 2000 May;25(1):25-9 10802651 Nat Rev Genet. 2001 Jun;2(6):418-27 11389458 Genomics. 2002 Feb;79(2):266-70 11829497 Bioinformatics. 2002;18 Suppl 1:S233-40 12169552 Genomics. 2003 Feb;81(2):98-104 12620386 Genome Biol. 2003;4(5):P3 12734009 Endocr Relat Cancer. 2003 Jun;10(2):179-86 12790780 Nucleic Acids Res. 2003 Jul 1;31(13):3775-81 12824416 BMC Bioinformatics. 2002 Nov 13;3:35 12431279 Int J Oncol. 2004 May;24(5):1279-88 15067352 Genome Biol. 2004;5(10):R80 15461798 J Biopharm Stat. 2004 Aug;14(3):701-21 15468760 Nucleic Acids Res. 1999 Jan 1;27(1):29-34 9847135 Nat Genet. 1999 Jul;22(3):281-5 10391217 Mutat Res. 2005 Jan 6;569(1-2):101-10 15603755 Brief Bioinform. 2008 May;9(3):189-97 18202032 Nat Genet. 2008 May;40(5):499-507 18443585 Bioinformatics. 2009 Jan 1;25(1):75-82 18990722 Nat Protoc. 2009;4(1):44-57 19131956 Nucleic Acids Res. 2009 Jan;37(1):1-13 19033363 Bioinformatics. 2009 Apr 15;25(8):1091-3 19237447 Nucleic Acids Res. 2009 Jul;37(Web Server issue):W329-34 19406925 Proteomics. 2010 Mar;10(6):1316-27 20127684 Nature. 2010 Jul 15;466(7304):368-72 20531469 Nat Biotechnol. 2010 Sep;28(9):935-42 20829833 Nucleic Acids Res. 2005 Jan 1;33(Database issue):D428-32 15608231 Clin Colorectal Cancer. 2005 Jan;4(5):302-12 15663833 FEBS Lett. 2005 Mar 21;579(8):1815-20 15763557 Proc Natl Acad Sci U S A. 2005 Jun 21;102(25):8961-5 15951424 Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W741-8 15980575 Bioinformatics. 2005 Aug 15;21(16):3448-9 15972284 Bioinformatics. 2005 Sep 15;21(18):3587-95 15994189 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 Nat Rev Genet. 2006 Jan;7(1):55-65 16369572 Bioinformatics. 2006 Jul 1;22(13):1600-7 16606683 Cancer Res. 2006 Sep 15;66(18):8927-30 16982728 Bioinformatics. 2007 Jan 15;23(2):257-8 17098774 Algorithms Breast Neoplasms genetics Cluster Analysis Colonic Neoplasms genetics Computational Biology methods Estrogens pharmacology Female Gene Expression Profiling Gene Expression Regulation, Neoplastic drug effects Gene Regulatory Networks Humans Internet Reproducibility of Results Software PMC2981572 2010 2 2 2010 10 20 2010 11 15 2010 11 19 6 0 2010 11 19 6 0 2011 4 28 6 0 epublish 10.1371/journal.pone.0013984 21085593 PMC2981572 21078182 2010 12 08 2011 02 17 2014 08 21

1471-2105 11 2010 BMC Bioinformatics An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology. 562 10.1186/1471-2105-11-562 Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs). They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO). Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity. We describe an improved algorithm, Topological Clustering Semantic Similarity (TCSS), to compute semantic similarity between GO terms annotated to proteins in interaction datasets. Our algorithm, considers unequal depth of biological knowledge representation in different branches of the GO graph. The central idea is to divide the GO graph into sub-graphs and score PPIs higher if participating proteins belong to the same sub-graph as compared to if they belong to different sub-graphs. The TCSS algorithm performs better than other semantic similarity measurement techniques that we evaluated in terms of their performance on distinguishing true from false protein interactions, and correlation with gene expression and protein families. We show an average improvement of 4.6 times the F1 score over Resnik, the next best method, on our Saccharomyces cerevisiae PPI dataset and 2 times on our Homo sapiens PPI dataset using cellular component, biological process and molecular function GO annotations. Jain Shobhit S Department of Computer Science, University of Toronto, 10 Kings College Road, Toronto, Ontario M5S3G4, Canada. Bader Gary D GD eng MOP-84324 Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't 2010 11 15

England BMC Bioinformatics 100965194 1471-2105 0 Proteins IM Nucleic Acids Res. 2000 Jan 1;28(1):289-91 10592249 Nucleic Acids Res. 2010 Jul;38(Web Server issue):W214-20 20576703 Nat Genet. 2000 May;25(1):25-9 10802651 EMBO J. 2000 Nov 1;19(21):5824-34 11060033 Nature. 2002 May 16;417(6886):304-8 11979277 Mol Cell Proteomics. 2002 May;1(5):349-56 12118076 Bioinformatics. 2003 Mar 22;19(5):635-42 12651722 J Biopharm Stat. 2004 Aug;14(3):687-700 15468759 Mol Cell Biol. 1997 Sep;17(9):5146-55 9271392 Pac Symp Biocomput. 2005;:471-82 15759652 Mol Cell Biol. 2005 May;25(9):3802-13 15831484 BMC Bioinformatics. 2005;6:100 15833142 Nucleic Acids Res. 2005;33(9):2822-37 15901854 Gene. 2005 Jun 6;352:75-81 15927423 Nat Biotechnol. 2005 Aug;23(8):951-9 16082366 Cell. 2005 Sep 23;122(6):957-68 16169070 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D535-9 16381927 Nature. 2006 Mar 30;440(7084):631-6 16429126 Nature. 2006 Mar 30;440(7084):637-43 16554755 BMC Bioinformatics. 2006;7:135 16536867 Bioinformatics. 2006 Apr 15;22(8):967-73 16492685 Methods Mol Biol. 2006;316:67-86 16671401 BMC Bioinformatics. 2006;7:302 16776819 IEEE/ACM Trans Comput Biol Bioinform. 2005 Oct-Dec;2(4):330-8 17044170 BMC Bioinformatics. 2006;7:491 17090318 BMC Bioinformatics. 2006;7:508 17112386 PLoS One. 2007;2(3):e337 17396164 Bioinformatics. 2007 May 15;23(10):1274-81 17344234 Bioinformatics. 2007 Jul 1;23(13):i529-38 17646340 J Biol Chem. 2007 Nov 9;282(45):32630-9 17761675 Nucleic Acids Res. 2008 Jan;36(Database issue):D434-9 17932054 BMC Bioinformatics. 2008;9:50 18221506 BMC Bioinformatics. 2008;9 Suppl 5:S4 18460186 Mol Cell Proteomics. 2008 Jun;7(6):1043-52 18230642 Science. 2008 Oct 3;322(5898):104-10 18719252 BMC Bioinformatics. 2008;9:405 18823568 BMC Bioinformatics. 2008;9:472 18986551 PLoS Comput Biol. 2009 Jul;5(7):e1000443 19649320 Nucleic Acids Res. 2010 Jan;38(Database issue):D142-8 19843607 Nat Biotechnol. 2010 Feb;28(2):128-30 20139945 Bioinformatics. 2010 Apr 1;26(7):976-8 20179076 Nature. 2000 Feb 10;403(6770):623-7 10688190 Algorithms Databases, Protein Humans Molecular Sequence Annotation Protein Interaction Mapping methods Proteins chemistry genetics metabolism Semantics PMC2998529 2010 7 9 2010 11 15 2010 11 15 2010 11 17 6 0 2010 11 17 6 0 2011 2 18 6 0 epublish 1471-2105-11-562 10.1186/1471-2105-11-562 21078182 PMC2998529 21076421 2010 11 30 2010 12 30 2014 09 17

1548-7105 7 12 2010 Dec Nat. Methods Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. 1017-24 10.1038/nmeth.1534 Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles. Baryshnikova Anastasia A Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada. brenda.andrews@utoronto.ca Costanzo Michael M Kim Yungil Y Ding Huiming H Koh Judice J Toufighi Kiana K Youn Ji-Young JY Ou Jiongwen J San Luis Bryan-Joseph BJ Bandyopadhyay Sunayan S Hibbs Matthew M Hess David D Gingras Anne-Claude AC Bader Gary D GD Troyanskaya Olga G OG Brown Grant W GW Andrews Brenda B Boone Charles C Myers Chad L CL eng (MOP-79368 Canadian Institutes of Health Research Canada 1R01HG005084-01A1 HG NHGRI NIH HHS United States GSP-415-67 Canadian Institutes of Health Research Canada R01 HG005084 HG NHGRI NIH HHS United States R01 HG005084-01A1 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. 2010 11 14

United States Nat Methods 101215604 1548-7091 IM Trends Genet. 1992 Sep;8(9):312-6 1365397 Nat Genet. 2005 Jan;37(1):77-83 15592468 Genome Biol. 2005;6(4):R38 15833125 Nat Genet. 2005 May;37(5):471-7 15821735 Eukaryot Cell. 2005 May;4(5):890-9 15879523 Genetics. 2005 Apr;169(4):1915-25 15716499 Mol Cell Biol. 2005 Aug;25(15):6772-88 16024810 Mol Cell Biol. 2005 Nov;25(21):9478-90 16227598 Trends Cell Biol. 2006 Feb;16(2):113-20 16406524 Nature. 2006 Mar 30;440(7084):631-6 16429126 Nature. 2006 Mar 30;440(7084):637-43 16554755 BMC Genomics. 2006;7:187 16869964 Genome Biol. 2006;7(7):R63 16859555 Nat Genet. 2007 Feb;39(2):199-206 17206143 Nat Genet. 2007 Apr;39(4):550-4 17322879 Mol Biol Cell. 2008 Jan;19(1):352-67 17959824 Nucleic Acids Res. 2008 Jan;36(Database issue):D637-40 18000002 PLoS Comput Biol. 2008 Apr;4(4):e1000065 18421374 Proc Natl Acad Sci U S A. 2008 May 20;105(20):7111-2 18474868 Science. 2008 Jun 13;320(5882):1465-70 18467557 Nat Methods. 2008 Aug;5(8):711-8 18622397 Science. 2008 Oct 3;322(5898):104-10 18719252 Science. 2008 Oct 17;322(5900):405-10 18818364 Proc Natl Acad Sci U S A. 2008 Oct 28;105(43):16653-8 18931302 Mol Cell Proteomics. 2009 Jan;8(1):157-71 18782753 Science. 2010 Jan 22;327(5964):425-31 20093466 Mol Biosyst. 2009 Dec;5(12):1439-55 19763343 Nat Genet. 2010 Mar;42(3):272-6 20101242 Nat Rev Genet. 2010 Oct;11(10):733-9 20838408 Methods Enzymol. 2010;470:145-79 20946810 Genome Biol. 2004;5(7):R49 15239834 Cell. 2000 Jul 7;102(1):109-26 10929718 Mol Cell Biol. 2003 Mar;23(5):1750-63 12588993 Proc Natl Acad Sci U S A. 2003 Dec 23;100(26):15724-9 14676322 Science. 2004 Feb 6;303(5659):808-13 14764870 J Biol Chem. 1993 Jan 15;268(2):1349-54 7678255 Algorithms Gene Expression Regulation, Fungal Genetic Fitness Genome, Fungal Genome-Wide Association Study methods Mutagenesis Mutation Oligonucleotide Array Sequence Analysis methods Ultraviolet Rays Yeasts genetics radiation effects NIHMS291951 PMC3117325 2010 7 28 2010 10 14 2010 11 14 2010 11 16 6 0 2010 11 16 6 0 2010 12 31 6 0 ppublish nmeth.1534 10.1038/nmeth.1534 21076421 PMC3117325 NIHMS291951 21071392 2010 12 23 2011 04 28 2014 08 21

1362-4962 39 Database issue 2011 Jan Nucleic Acids Res. Pathway Commons, a web resource for biological pathway data. D685-90 10.1093/nar/gkq1039 Pathway Commons (http://www.pathwaycommons.org) is a collection of publicly available pathway data from multiple organisms. Pathway Commons provides a web-based interface that enables biologists to browse and search a comprehensive collection of pathways from multiple sources represented in a common language, a download site that provides integrated bulk sets of pathway information in standard or convenient formats and a web service that software developers can use to conveniently query and access all data. Database providers can share their pathway data via a common repository. Pathways include biochemical reactions, complex assembly, transport and catalysis events and physical interactions involving proteins, DNA, RNA, small molecules and complexes. Pathway Commons aims to collect and integrate all public pathway data available in standard formats. Pathway Commons currently contains data from nine databases with over 1400 pathways and 687,000 interactions and will be continually expanded and updated. Cerami Ethan G EG Computational Biology Center, Memorial Sloan-Kettering Cancer Center 1275 York Avenue, Box 460, New York, NY 10065, USA. Gross Benjamin E BE Demir Emek E Rodchenkov Igor I Babur Ozgün O Anwar Nadia N Schultz Nikolaus N Bader Gary D GD Sander Chris C eng 1T32 GM083937 GM NIGMS NIH HHS United States 2R01GM070743-06 GM NIGMS NIH HHS United States P41HG004118 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2010 11 10

England Nucleic Acids Res 0411011 0305-1048 IM Nat Genet. 2000 May;25(1):25-9 10802651 Proc Natl Acad Sci U S A. 2003 Apr 15;100(8):4372-6 12676999 Genome Res. 2003 Nov;13(11):2498-504 14597658 Nat Biotechnol. 2004 Jan;22(1):78-85 14704708 Genome Biol. 2005;6(1):R2 15642094 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D187-91 16381842 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6 16381921 BMC Bioinformatics. 2006;7:497 17101041 Mol Syst Biol. 2007;3:140 17940530 BMC Biol. 2007;5:44 17925023 Nucleic Acids Res. 2008 Jan;36(Database issue):D637-40 18000002 Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9 18832364 Nucleic Acids Res. 2009 Jan;37(Database issue):D5-15 18940862 Nucleic Acids Res. 2009 Jan;37(Database issue):D619-22 18981052 Nucleic Acids Res. 2009 Jan;37(Database issue):D767-72 18988627 Nucleic Acids Res. 2009 Jan;37(1):1-13 19033363 Nat Biotechnol. 2009 Aug;27(8):735-41 19668183 BMC Bioinformatics. 2009;10:376 19917102 Nucleic Acids Res. 2010 Jan;38(Database issue):D525-31 19850723 Nucleic Acids Res. 2010 Jan;38(Database issue):D532-9 19897547 Bioinformatics. 2010 Feb 1;26(3):429-31 20007251 PLoS One. 2010;5(2):e8918 20169195 Bioinformatics. 2010 Sep 15;26(18):2347-8 20656902 Databases, Factual Databases, Genetic Databases, Protein Disease classification Genomics Internet Models, Biological Systems Integration User-Computer Interface PMC3013659 2010 11 10 2010 11 13 6 0 2010 11 13 6 0 2011 4 29 6 0 ppublish gkq1039 10.1093/nar/gkq1039 21071392 PMC3013659 20939902 2010 11 02 2011 03 24 2014 08 21

1471-2105 11 2010 BMC Bioinformatics Proteome scanning to predict PDZ domain interactions using support vector machines. 507 10.1186/1471-2105-11-507 PDZ domains mediate protein-protein interactions involved in important biological processes through the recognition of short linear motifs in their target proteins. Two recent independent studies have used protein microarray or phage display technology to detect PDZ domain interactions with peptide ligands on a large scale. Several computational predictors of PDZ domain interactions have been developed, however they are trained using only protein microarray data and focus on limited subsets of PDZ domains. An accurate predictor of genomic PDZ domain interactions would allow the proteomes of organisms to be scanned for potential binders. Such an application would require an accurate and precise predictor to avoid generating too many false positive hits given the large amount of possible interactors in a given proteome. Once validated these predictions will help to increase the coverage of current PDZ domain interaction networks and further our understanding of the roles that PDZ domains play in a variety of biological processes. We developed a PDZ domain interaction predictor using a support vector machine (SVM) trained with both protein microarray and phage display data. In order to use the phage display data for training, which only contains positive interactions, we developed a method to generate artificial negative interactions. Using cross-validation and a series of independent tests, we showed that our SVM successfully predicts interactions in different organisms. We then used the SVM to scan the proteomes of human, worm and fly to predict binders for several PDZ domains. Predictions were validated using known genomic interactions and published protein microarray experiments. Based on our results, new protein interactions potentially associated with Usher and Bardet-Biedl syndromes were predicted. A comparison of performance measures (F1 measure and FPR) for the SVM and published predictors demonstrated our SVM's improved accuracy and precision at proteome scanning. We built an SVM using mouse and human experimental training data to predict PDZ domain interactions. We showed that it correctly predicts known interactions from proteomes of different organisms and is more accurate and precise at proteome scanning compared with published state-of-the-art predictors. Hui Shirley S Donnelly Center for Cellular and Biomolecular Research, Banting and Best Department of Medical Research, University of Toronto, Toronto ON, Canada. Bader Gary D GD eng MOP-84324 Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't 2010 10 12

England BMC Bioinformatics 100965194 1471-2105 0 Proteins 0 Proteome IM J Clin Invest. 1999 Nov;104(10):1353-61 10562297 BMC Bioinformatics. 2010;11:144 20298601 Proc Natl Acad Sci U S A. 2003 Jan 7;100(1):74-9 12502784 Science. 2003 Apr 18;300(5618):445-52 12702867 Science. 2003 Oct 17;302(5644):449-53 14564010 BMC Bioinformatics. 2002 Sep 11;3:25 12225620 J Mol Biol. 2004 Jan 23;335(4):1105-15 14698303 J Mol Biol. 2004 Oct 22;343(3):703-18 15465056 Science. 1997 Jan 3;275(5296):73-7 8974395 Protein Sci. 1997 Feb;6(2):464-8 9041651 Nat Rev Drug Discov. 2004 Dec;3(12):1047-56 15573103 Bioinformatics. 2005 Mar15;21(6):827-8 15513994 Proteomics. 2005 Mar;5(4):876-84 15717327 Proc Natl Acad Sci U S A. 2005 May 3;102(18):6395-400 15851683 Curr Opin Genet Dev. 2005 Jun;15(3):308-14 15917207 Bioinformatics. 2005 Aug 15;21(16):3448-9 15972284 Bioinformatics. 2006 Mar 1;22(5):532-40 16397010 Clin Sci (Lond). 2006 May;110(5):525-41 16597322 BMC Bioinformatics. 2006;7 Suppl 1:S2 16723005 J Am Chem Soc. 2006 Oct 4;128(39):12766-77 17002371 Science. 2007 Jul 20;317(5836):364-9 17641200 Mol Cell Proteomics. 2008 Jun;7(6):1043-52 18230642 Nat Biotechnol. 2008 Sep;26(9):1041-5 18711339 PLoS Biol. 2008 Sep 30;6(9):e239 18828675 BMC Bioinformatics. 2008;9:405 18823568 Nucleic Acids Res. 2009 Jan;37(Database issue):D690-7 19033362 Mol Cells. 2009 Jun 30;27(6):629-34 19533032 Nucleic Acids Res. 2009 Aug;37(14):4629-41 19502496 Nat Biotechnol. 2001 Apr;19(4):348-53 11283593 Animals Artificial Intelligence Binding Sites Humans Mice PDZ Domains Protein Array Analysis methods Protein Interaction Mapping methods Proteins chemistry metabolism Proteome chemistry metabolism PMC2967561 2010 5 12 2010 10 12 2010 10 12 2010 10 14 6 0 2010 10 14 6 0 2011 3 25 6 0 epublish 1471-2105-11-507 10.1186/1471-2105-11-507 20939902 PMC2967561 20926419 2010 11 04 2011 02 16 2014 08 21

1367-4811 26 22 2010 Nov 15 Bioinformatics GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop. 2927-8 10.1093/bioinformatics/btq562 The GeneMANIA Cytoscape plugin brings fast gene function prediction capabilities to the desktop. GeneMANIA identifies the most related genes to a query gene set using a guilt-by-association approach. The plugin uses over 800 networks from six organisms and each related gene is traceable to the source network used to make the prediction. Users may add their own interaction networks and expression profile data to complement or override the default data. The GeneMANIA Cytoscape plugin is implemented in Java and is freely available at http://www.genemania.org/plugin/. Montojo J J Banting and Best Department of Medical Research, The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON, M5S 3E1, Canada. Zuberi K K Rodriguez H H Kazi F F Wright G G Donaldson S L SL Morris Q Q Bader G D GD eng Journal Article Research Support, Non-U.S. Gov't 2010 10 05

England Bioinformatics 9808944 1367-4803 IM Genome Res. 2003 Nov;13(11):2498-504 14597658 Bioinformatics. 2005 May 1;21(9):2076-82 15657099 Nucleic Acids Res. 2008 Jan;36(Database issue):D637-40 18000002 Genome Biol. 2008;9 Suppl 1:S2 18613946 Bioinformatics. 2010 Jul 15;26(14):1759-65 20507895 Nucleic Acids Res. 2009 Jan;37(Database issue):D885-90 18940857 PLoS Med. 2009 Apr 7;6(4):e1000046 19360088 Nucleic Acids Res. 2010 Jul;38(Web Server issue):W214-20 20576703 Genome Biol. 2008;9 Suppl 1:S4 18613948 Algorithms Computational Biology methods Databases, Factual Gene Regulatory Networks Genes Software PMC2971582 2010 10 5 2010 10 8 6 0 2010 10 12 6 0 2011 2 17 6 0 ppublish btq562 10.1093/bioinformatics/btq562 20926419 PMC2971582 20924352 2010 10 06 2011 01 19 2014 08 21

1744-4292 6 2010 Oct 5 Mol. Syst. Biol. Dynamic interaction networks in a hierarchically organized tissue. 417 10.1038/msb.2010.71 Intercellular (between cell) communication networks maintain homeostasis and coordinate regenerative and developmental cues in multicellular organisms. Despite the importance of intercellular networks in stem cell biology, their rules, structure and molecular components are poorly understood. Herein, we describe the structure and dynamics of intercellular and intracellular networks in a stem cell derived, hierarchically organized tissue using experimental and theoretical analyses of cultured human umbilical cord blood progenitors. By integrating high-throughput molecular profiling, database and literature mining, mechanistic modeling, and cell culture experiments, we show that secreted factor-mediated intercellular communication networks regulate blood stem cell fate decisions. In particular, self-renewal is modulated by a coupled positive-negative intercellular feedback circuit composed of megakaryocyte-derived stimulatory growth factors (VEGF, PDGF, EGF, and serotonin) versus monocyte-derived inhibitory factors (CCL3, CCL4, CXCL10, TGFB2, and TNFSF9). We reconstruct a stem cell intracellular network, and identify PI3K, Raf, Akt, and PLC as functionally distinct signal integration nodes, linking extracellular, and intracellular signaling. This represents the first systematic characterization of how stem cell fate decisions are regulated non-autonomously through lineage-specific interactions with differentiated progeny. Kirouac Daniel C DC Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada. Ito Caryn C Csaszar Elizabeth E Roch Aline A Yu Mei M Sykes Edward A EA Bader Gary D GD Zandstra Peter W PW eng Journal Article Research Support, Non-U.S. Gov't

England Mol Syst Biol 101235389 1744-4292 0 Intercellular Signaling Peptides and Proteins IM Bone Marrow Transplant. 2001 May;27(10):1075-80 11438824 J Med Chem. 2008 Dec 25;51(24):7898-914 19035792 J Exp Med. 2001 Oct 1;194(7):941-52 11581316 Br J Haematol. 2002 Jun;117(3):735-46 12028051 Nature. 2002 Jun 27;417(6892):954-8 12087404 Nat Med. 2002 Aug;8(8):841-9 12091880 Genome Biol. 2003;4(1):R7 12540299 Blood. 1990 Jan 1;75(1):96-101 2403823 Int J Hematol. 1996 Feb;63(2):137-42 8867723 Proc Natl Acad Sci U S A. 1997 Jan 21;94(2):663-8 9012841 Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8 9843981 Cell. 1999 Jun 11;97(6):727-41 10380925 Exp Hematol. 1999 Jul;27(7):1113-23 10390186 Bioinformatics. 2005 May 1;21(9):2076-82 15657099 Oncogene. 2005 Aug 29;24(37):5676-92 16123801 Exp Hematol. 2005 Oct;33(10):1229-39 16219546 Cell. 2006 Mar 24;124(6):1225-39 16564013 Nat Cell Biol. 2006 Jun;8(6):571-80 16699502 Curr Opin Biotechnol. 2006 Oct;17(5):538-47 16899360 Bone. 2006 Nov;39(5):978-84 16860008 Biol Blood Marrow Transplant. 2006 Oct;12(10):1020-30 17084368 Nat Methods. 2009 Jan;6(1):39-46 19116613 Cell Stem Cell. 2009 Jan 9;4(1):62-72 19128793 PLoS Biol. 2009 Jan 20;7(1):e15 19166268 Cell. 2009 Apr 17;137(2):369-79 19379700 Mol Syst Biol. 2009;5:293 19638974 Nat Rev Mol Cell Biol. 2009 Oct;10(10):672-81 19738627 Blood. 2010 Jan 14;115(2):257-60 19897585 Nature. 2010 Jan 28;463(7280):495-500 20110993 Nat Med. 2010 Feb;16(2):232-6 20081862 Blood. 2010 May 20;115(20):4030-8 20354168 Bone Marrow Transplant. 2010 Jun;45(6):1000-7 19838220 BMC Genomics. 2007;8:264 17683550 Anal Chem. 2007 Nov 15;79(22):8557-63 17953452 Nat Rev Immunol. 2008 Apr;8(4):290-301 18323850 Cell Stem Cell. 2008 Nov 6;3(5):484-92 18983964 PLoS Comput Biol. 2008 Nov;4(11):e1000217 18989396 Blood. 2001 Sep 15;98(6):1782-91 11535512 Analysis of Variance Cell Communication physiology Cell Differentiation physiology Cells, Cultured Cluster Analysis Computational Biology methods Computer Simulation Data Mining Fetal Blood cytology Gene Expression Profiling Gene Regulatory Networks Hematopoietic Stem Cells cytology physiology Humans Intercellular Signaling Peptides and Proteins physiology Linear Models Models, Biological Signal Transduction PMC2990637 2010 3 29 2010 7 27 2010 10 7 6 0 2010 10 7 6 0 2011 1 20 6 0 ppublish msb201071 10.1038/msb.2010.71 20924352 PMC2990637 20868367 2010 11 26 2011 01 06 2013 04 05

1470-8728 432 3 2010 Dec 15 Biochem. J. Functional complexes between YAP2 and ZO-2 are PDZ domain-dependent, and regulate YAP2 nuclear localization and signalling. 461-72 10.1042/BJ20100870 The Hippo pathway regulates the size of organs by controlling two opposing processes: proliferation and apoptosis. YAP2 (Yes kinase-associated protein 2), one of the three isoforms of YAP, is a WW domain-containing transcriptional co-activator that acts as the effector of the Hippo pathway in mammalian cells. In addition to WW domains, YAP2 has a PDZ-binding motif at its C-terminus. We reported previously that this motif was necessary for YAP2 localization in the nucleus and for promoting cell detachment and apoptosis. In the present study, we show that the tight junction protein ZO (zonula occludens)-2 uses its first PDZ domain to form a complex with YAP2. The endogenous ZO-2 and YAP2 proteins co-localize in the nucleus. We also found that ZO-2 facilitates the nuclear localization and pro-apoptotic function of YAP2, and that this activity of ZO-2 is PDZ-domain-dependent. The present paper is the first report on a PDZ-based nuclear translocation mechanism. Moreover, since the Hippo pathway acts as a tumour suppressor pathway, the YAP2-ZO-2 complex could represent a target for cancer therapy. Oka Tsutomu T Weis Center for Research, 100 North Academy Avenue, Danville, PA 17822, USA. Remue Eline E Meerschaert Kris K Vanloo Berlinda B Boucherie Ciska C Gfeller David D Bader Gary D GD Sidhu Sachdev S SS Vandekerckhove Joël J Gettemans Jan J Sudol Marius M eng MOP-84324 Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't

England Biochem J 2984726R 0264-6021 0 Adaptor Proteins, Signal Transducing 0 Membrane Proteins 0 Mutant Proteins 0 Phosphoproteins 0 Recombinant Fusion Proteins 0 TJP1 protein, human 0 TJP2 protein, human 0 YAP1 (Yes-associated) protein, human 0 Zonula Occludens-1 Protein 0 Zonula Occludens-2 Protein IM Adaptor Proteins, Signal Transducing chemistry genetics metabolism Animals Cell Adhesion Cell Line Cell Nucleus metabolism Cell Proliferation Dogs Genes, Reporter HEK293 Cells Humans Immunoprecipitation Membrane Proteins chemistry genetics metabolism Mutant Proteins chemistry metabolism PDZ Domains Phosphoproteins chemistry genetics metabolism Protein Transport RNA Interference Recombinant Fusion Proteins chemistry metabolism Signal Transduction Transfection Zonula Occludens-1 Protein Zonula Occludens-2 Protein 2010 9 28 6 0 2010 9 28 6 0 2011 1 7 6 0 ppublish BJ20100870 10.1042/BJ20100870 20868367 20829833 2010 09 10 2010 12 22 2015 04 06

1546-1696 28 9 2010 Sep Nat. Biotechnol. The BioPAX community standard for pathway data sharing. 935-42 10.1038/nbt.1666 Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery. Demir Emek E Computational Biology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. Cary Michael P MP Paley Suzanne S Fukuda Ken K Lemer Christian C Vastrik Imre I Wu Guanming G D'Eustachio Peter P Schaefer Carl C Luciano Joanne J Schacherer Frank F Martinez-Flores Irma I Hu Zhenjun Z Jimenez-Jacinto Veronica V Joshi-Tope Geeta G Kandasamy Kumaran K Lopez-Fuentes Alejandra C AC Mi Huaiyu H Pichler Elgar E Rodchenkov Igor I Splendiani Andrea A Tkachev Sasha S Zucker Jeremy J Gopinath Gopal G Rajasimha Harsha H Ramakrishnan Ranjani R Shah Imran I Syed Mustafa M Anwar Nadia N Babur Ozgün O Blinov Michael M Brauner Erik E Corwin Dan D Donaldson Sylva S Gibbons Frank F Goldberg Robert R Hornbeck Peter P Luna Augustin A Murray-Rust Peter P Neumann Eric E Ruebenacker Oliver O Reubenacker Oliver O Samwald Matthias M van Iersel Martijn M Wimalaratne Sarala S Allen Keith K Braun Burk B Whirl-Carrillo Michelle M Cheung Kei-Hoi KH Dahlquist Kam K Finney Andrew A Gillespie Marc M Glass Elizabeth E Gong Li L Haw Robin R Honig Michael M Hubaut Olivier O Kane David D Krupa Shiva S Kutmon Martina M Leonard Julie J Marks Debbie D Merberg David D Petri Victoria V Pico Alex A Ravenscroft Dean D Ren Liya L Shah Nigam N Sunshine Margot M Tang Rebecca R Whaley Ryan R Letovksy Stan S Buetow Kenneth H KH Rzhetsky Andrey A Schachter Vincent V Sobral Bruno S BS Dogrusoz Ugur U McWeeney Shannon S Aladjem Mirit M Birney Ewan E Collado-Vides Julio J Goto Susumu S Hucka Michael M Le Novère Nicolas N Maltsev Natalia N Pandey Akhilesh A Thomas Paul P Wingender Edgar E Karp Peter D PD Sander Chris C Bader Gary D GD eng 1R13GM076939 GM NIGMS NIH HHS United States BB/F010516/1 Biotechnology and Biological Sciences Research Council United Kingdom P30 CA069533 CA NCI NIH HHS United States P41 HG004118 HG NHGRI NIH HHS United States P41 HG004118-01A1 HG NHGRI NIH HHS United States P41HG004118 HG NHGRI NIH HHS United States R01 GM070923 GM NIGMS NIH HHS United States R01 GM071962 GM NIGMS NIH HHS United States R01 RR022971 RR NCRR NIH HHS United States R01GM071962-07 GM NIGMS NIH HHS United States R13 GM076939 GM NIGMS NIH HHS United States R13 GM076939-01 GM NIGMS NIH HHS United States U24 GM077678 GM NIGMS NIH HHS United States U24 GM077678-18 GM NIGMS NIH HHS United States U54 AI081680 AI NIAID NIH HHS United States U54 HG004028 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. 2010 09 09

United States Nat Biotechnol 9604648 1087-0156 IM Nucleic Acids Res. 2007 Jan;35(Database issue):D247-52 17130144 Nucleic Acids Res. 2007 Jan;35(Database issue):D572-4 17135203 Nucleic Acids Res. 2007 Jan;35(Database issue):D561-5 17145710 Nucleic Acids Res. 2007 Jan;35(Database issue):D5-12 17170002 Nucleic Acids Res. 2007 Jul;35(Web Server issue):W625-32 17586824 Mol Syst Biol. 2007;3:140 17940530 Nat Protoc. 2007;2(10):2366-82 17947979 BMC Biol. 2007;5:44 17925023 Nucleic Acids Res. 2008 Jan;36(Database issue):D120-4 18158297 BMC Syst Biol. 2007;1:58 18078503 Bioinformatics. 2008 Mar 15;24(6):876-7 18024474 PLoS Biol. 2008 Jul 22;6(7):e184 18651794 PLoS One. 2008;3(8):e2901 18682855 Nature. 2008 Oct 23;455(7216):1061-8 18772890 IET Syst Biol. 2008 Sep;2(5):352-62 19045830 Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9 18832364 Nucleic Acids Res. 2009 Jan;37(Database issue):D464-70 18974181 Nucleic Acids Res. 2009 Jan;37(Database issue):D619-22 18981052 Nucleic Acids Res. 2009 Jan;37(1):1-13 19033363 Nat Biotechnol. 2009 Aug;27(8):735-41 19668183 Bioinformatics. 2009 Dec 15;25(24):3327-9 19837718 Nucleic Acids Res. 2010 Jan;38(Database issue):D473-9 19850718 Science. 2010 Jan 22;327(5964):425-31 20093466 Nat Genet. 2000 May;25(1):25-9 10802651 Bioinformatics. 2000 Mar;16(3):269-85 10869020 Bioinformatics. 2000 May;16(5):465-77 10871269 Nature. 2001 Apr 26;410(6832):1023-4 11323639 IUBMB Life. 2000 Dec;50(6):341-4 11327304 Bioinformatics. 2001 Sep;17(9):829-37 11590099 Nucleic Acids Res. 2002 Jan 1;30(1):303-5 11752321 Science. 2002 Mar 1;295(5560):1669-78 11872831 FEBS Lett. 2002 Feb 20;513(1):135-40 11911893 Nat Rev Cancer. 2002 Jul;2(7):489-501 12094235 Bioinformatics. 2002 Jul;18(7):996-1003 12117798 Bioinformatics. 2003 Mar 1;19(4):524-31 12611808 Genome Biol. 2003;4(3):R23 12620108 Nucleic Acids Res. 2003 Jul 1;31(13):3784-8 12824418 Genome Res. 2003 Oct;13(10):2363-71 14525934 Genome Res. 2003 Nov;13(11):2498-504 14597658 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D277-80 14681412 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D449-51 14681454 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D452-5 14681455 OMICS. 2003 Winter;7(4):355-72 14683609 Nat Biotechnol. 2004 Feb;22(2):177-83 14755292 Bioinformatics. 2004 Feb 12;20(3):349-56 14960461 BMC Bioinformatics. 2004 Feb 19;5:17 15028117 Prog Biophys Mol Biol. 2004 Jun-Jul;85(2-3):433-50 15142756 Bioinformatics. 2004 Aug 4;20 Suppl 1:i257-64 15262807 Mol Biol Cell. 1999 Aug;10(8):2703-34 10436023 Nucleic Acids Res. 2005 Jan 1;33(Database issue):D284-8 15608197 Nucleic Acids Res. 2005 Jan 1;33(Database issue):D428-32 15608231 Genome Biol. 2005;6(1):R2 15642094 Nucleic Acids Res. 2005;33(4):1399-409 15745999 FEBS Lett. 2005 Mar 21;579(8):1815-20 15763557 Bioinformatics. 2005 May 1;21(9):2076-82 15657099 Sci STKE. 2005 May 10;2005(283):pe21 15886388 Genome Biol. 2005;6(5):R44 15892872 Nat Biotechnol. 2005 Aug;23(8):961-6 16082367 Nucleic Acids Res. 2005;33(19):6083-9 16246909 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D108-10 16381825 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D436-41 16381906 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6 16381921 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D535-9 16381927 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D546-51 16381929 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D689-91 16381960 Bioinformatics. 2006 Jan 15;22(2):233-41 16278238 Sci STKE. 2006 Jul 17;2006(344):re6 16849649 BMC Bioinformatics. 2006;7:196 16603083 Bioinformatics. 2006 Jul 15;22(14):e271-80 16873482 BMC Bioinformatics. 2006;7:497 17101041 Brief Bioinform. 2010 Jan;11(1):40-79 19955237 Bioinformatics. 2010 Feb 1;26(3):429-31 20007251 Proteomics. 2010 Mar;10(6):1316-27 20127684 Genome Biol. 2010;11(1):R3 20067622 Genome Biol. 2010;11(5):R53 20482850 Nature. 2010 Jul 15;466(7304):368-72 20531469 Bioinformatics. 2002;18 Suppl 1:S233-40 12169552 Nucleic Acids Res. 2003 Jan 1;31(1):248-50 12519993 Nat Biotechnol. 2010 Dec;28(12):1308 Nat Biotechnol. 2012 Apr;30(4):365 Reubenacker, Oliver [corrected to Ruebenacker, Oliver] Computational Biology methods standards Databases as Topic Information Dissemination Metabolic Networks and Pathways Programming Languages Signal Transduction Software NIHMS216985 PMC3001121 2010 9 09 2010 9 11 6 0 2010 9 11 6 0 2010 12 24 6 0 ppublish nbt.1666 10.1038/nbt.1666 20829833 PMC3001121 NIHMS216985 20714644 2010 09 08 2011 01 13 2011 05 16

1742-2051 6 10 2010 Oct Mol Biosyst Coevolution of PDZ domain-ligand interactions analyzed by high-throughput phage display and deep sequencing. 1782-90 10.1039/c0mb00061b The determinants of binding specificities of peptide recognition domains and their evolution remain important problems in molecular systems biology. Here, we present a new methodology to analyze the coevolution between a domain and its ligands by combining high-throughput phage display with deep sequencing. First, from a library of PDZ domains with diversity introduced at ten positions in the binding site, we evolved domains for binding to 15 distinct peptide ligands. Interestingly, for a given peptide many different functional domains emerged, which exhibited only limited sequence homology, showing that many different binding sites can recognize a given peptide. Subsequently, we used peptide-phage libraries and deep sequencing to map the specificity profiles of these evolved domains at high resolution, and we found that the domains recognize their cognate peptides with high affinity but low specificity. Our analysis reveals two aspects of evolution of new binding specificities. First, we were able to identify some common features amongst domains raised against a common peptide. Second, our analysis suggests that cooperative interactions between multiple binding site residues lead to a diversity of binding profiles with considerable plasticity. The details of intramolecular cooperativity remain to be elucidated, but nonetheless, we have established a general methodology that can be used to explore protein evolution in a systematic yet rapid manner. Ernst Andreas A Banting and Best Department of Medical Research, and the Terrence Donnelly Center for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario, Canada. Gfeller David D Kan Zhengyan Z Seshagiri Somasekar S Kim Philip M PM Bader Gary D GD Sidhu Sachdev S SS eng MOP-84324 Canadian Institutes of Health Research Canada MOP-93684 Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't 2010 08 11

England Mol Biosyst 101251620 1742-2051 0 Ligands 0 Peptide Library 0 Peptides IM Amino Acid Sequence Bacteriophages genetics Evolution, Molecular Ligands PDZ Domains Peptide Library Peptides metabolism Protein Binding 2010 8 11 2010 10 1 2010 8 18 6 0 2010 8 18 6 0 2011 1 14 6 0 ppublish 10.1039/c0mb00061b 20714644 20656902 2010 09 08 2010 10 26 2014 08 24

1367-4811 26 18 2010 Sep 15 Bioinformatics Cytoscape Web: an interactive web-based network browser. 2347-8 10.1093/bioinformatics/btq430 Cytoscape Web is a web-based network visualization tool-modeled after Cytoscape-which is open source, interactive, customizable and easily integrated into web sites. Multiple file exchange formats can be used to load data into Cytoscape Web, including GraphML, XGMML and SIF. Cytoscape Web is implemented in Flex/ActionScript with a JavaScript API and is freely available at http://cytoscapeweb.cytoscape.org/. Lopes Christian T CT Banting and Best Department of Medical Research, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada. Franz Max M Kazi Farzana F Donaldson Sylva L SL Morris Quaid Q Bader Gary D GD eng 2R01GM070743-06 GM NIGMS NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2010 07 23

England Bioinformatics 9808944 1367-4803 IM Genome Res. 2003 Nov;13(11):2498-504 14597658 Bioinformatics. 2005 Dec 15;21(24):4432-3 16188923 Bioinformatics. 2008 Jun 15;24(12):1467-8 18445606 BMC Bioinformatics. 2008;9:405 18823568 Nucleic Acids Res. 2009 Jan;37(Database issue):D412-6 18940858 Nucleic Acids Res. 2010 Jul;38(Web Server issue):W214-20 20576703 Internet Software PMC2935447 2010 7 23 2010 7 27 6 0 2010 7 27 6 0 2010 10 27 6 0 ppublish btq430 10.1093/bioinformatics/btq430 20656902 PMC2935447 20576703 2010 06 25 2010 09 27 2014 08 24

1362-4962 38 Web Server issue 2010 Jul Nucleic Acids Res. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. W214-20 10.1093/nar/gkq537 GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist. Warde-Farley David D Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. Donaldson Sylva L SL Comes Ovi O Zuberi Khalid K Badrawi Rashad R Chao Pauline P Franz Max M Grouios Chris C Kazi Farzana F Lopes Christian Tannus CT Maitland Anson A Mostafavi Sara S Montojo Jason J Shao Quentin Q Wright George G Bader Gary D GD Morris Quaid Q eng Journal Article Research Support, Non-U.S. Gov't

England Nucleic Acids Res 0411011 0305-1048 IM Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9 18832364 PLoS Comput Biol. 2008;4(9):e1000165 18818725 Nucleic Acids Res. 2009 Jan;37(Database issue):D412-6 18940858 Nucleic Acids Res. 2009 Jan;37(Database issue):D767-72 18988627 Genome Res. 2009 Jun;19(6):1107-16 19246318 Nucleic Acids Res. 2010 Jan;38(Database issue):D525-31 19850723 Nucleic Acids Res. 2010 Jan;38(Database issue):D532-9 19897547 Bioinformatics. 2010 Jul 15;26(14):1759-65 20507895 Genome Biol. 2005;6(1):R2 15642094 Bioinformatics. 2005 May 1;21(9):2076-82 15657099 Plant J. 2005 Jul;43(1):153-63 15960624 Genome Biol. 2005;6(13):R114 16420673 Genome Biol. 2007;8(3):R39 17367534 Nucleic Acids Res. 2008 Jan;36(Database issue):D637-40 18000002 Genome Biol. 2008;9 Suppl 1:S2 18613946 Genome Biol. 2008;9 Suppl 1:S4 18613948 Curr Protoc Bioinformatics. 2008 Sep;Chapter 9:Unit 9.11 18819079 Nucleic Acids Res. 2009 Jan;37(Database issue):D885-90 18940857 Algorithms Animals Gene Regulatory Networks Genes physiology Genomics Humans Internet Mice Software PMC2896186 2010 6 26 6 0 2010 7 2 6 0 2010 9 29 6 0 ppublish gkq537 10.1093/nar/gkq537 20576703 PMC2896186 20531469 2010 07 15 2010 08 30 2015 07 08

1476-4687 466 7304 2010 Jul 15 Nature Functional impact of global rare copy number variation in autism spectrum disorders. 368-72 10.1038/nature09146 The autism spectrum disorders (ASDs) are a group of conditions characterized by impairments in reciprocal social interaction and communication, and the presence of restricted and repetitive behaviours. Individuals with an ASD vary greatly in cognitive development, which can range from above average to intellectual disability. Although ASDs are known to be highly heritable ( approximately 90%), the underlying genetic determinants are still largely unknown. Here we analysed the genome-wide characteristics of rare (<1% frequency) copy number variation in ASD using dense genotyping arrays. When comparing 996 ASD individuals of European ancestry to 1,287 matched controls, cases were found to carry a higher global burden of rare, genic copy number variants (CNVs) (1.19 fold, P = 0.012), especially so for loci previously implicated in either ASD and/or intellectual disability (1.69 fold, P = 3.4 x 10(-4)). Among the CNVs there were numerous de novo and inherited events, sometimes in combination in a given family, implicating many novel ASD genes such as SHANK2, SYNGAP1, DLGAP2 and the X-linked DDX53-PTCHD1 locus. We also discovered an enrichment of CNVs disrupting functional gene sets involved in cellular proliferation, projection and motility, and GTPase/Ras signalling. Our results reveal many new genetic and functional targets in ASD that may lead to final connected pathways. Pinto Dalila D The Centre for Applied Genomics and Program in Genetics and Genomic Biology, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada. Pagnamenta Alistair T AT Klei Lambertus L Anney Richard R Merico Daniele D Regan Regina R Conroy Judith J Magalhaes Tiago R TR Correia Catarina C Abrahams Brett S BS Almeida Joana J Bacchelli Elena E Bader Gary D GD Bailey Anthony J AJ Baird Gillian G Battaglia Agatino A Berney Tom T Bolshakova Nadia N Bölte Sven S Bolton Patrick F PF Bourgeron Thomas T Brennan Sean S Brian Jessica J Bryson Susan E SE Carson Andrew R AR Casallo Guillermo G Casey Jillian J Chung Brian H Y BH Cochrane Lynne L Corsello Christina C Crawford Emily L EL Crossett Andrew A Cytrynbaum Cheryl C Dawson Geraldine G de Jonge Maretha M Delorme Richard R Drmic Irene I Duketis Eftichia E Duque Frederico F Estes Annette A Farrar Penny P Fernandez Bridget A BA Folstein Susan E SE Fombonne Eric E Freitag Christine M CM Gilbert John J Gillberg Christopher C Glessner Joseph T JT Goldberg Jeremy J Green Andrew A Green Jonathan J Guter Stephen J SJ Hakonarson Hakon H Heron Elizabeth A EA Hill Matthew M Holt Richard R Howe Jennifer L JL Hughes Gillian G Hus Vanessa V Igliozzi Roberta R Kim Cecilia C Klauck Sabine M SM Kolevzon Alexander A Korvatska Olena O Kustanovich Vlad V Lajonchere Clara M CM Lamb Janine A JA Laskawiec Magdalena M Leboyer Marion M Le Couteur Ann A Leventhal Bennett L BL Lionel Anath C AC Liu Xiao-Qing XQ Lord Catherine C Lotspeich Linda L Lund Sabata C SC Maestrini Elena E Mahoney William W Mantoulan Carine C Marshall Christian R CR McConachie Helen H McDougle Christopher J CJ McGrath Jane J McMahon William M WM Merikangas Alison A Migita Ohsuke O Minshew Nancy J NJ Mirza Ghazala K GK Munson Jeff J Nelson Stanley F SF Noakes Carolyn C Noor Abdul A Nygren Gudrun G Oliveira Guiomar G Papanikolaou Katerina K Parr Jeremy R JR Parrini Barbara B Paton Tara T Pickles Andrew A Pilorge Marion M Piven Joseph J Ponting Chris P CP Posey David J DJ Poustka Annemarie A Poustka Fritz F Prasad Aparna A Ragoussis Jiannis J Renshaw Katy K Rickaby Jessica J Roberts Wendy W Roeder Kathryn K Roge Bernadette B Rutter Michael L ML Bierut Laura J LJ Rice John P JP Salt Jeff J Sansom Katherine K Sato Daisuke D Segurado Ricardo R Sequeira Ana F AF Senman Lili L Shah Naisha N Sheffield Val C VC Soorya Latha L Sousa Inês I Stein Olaf O Sykes Nuala N Stoppioni Vera V Strawbridge Christina C Tancredi Raffaella R Tansey Katherine K Thiruvahindrapduram Bhooma B Thompson Ann P AP Thomson Susanne S Tryfon Ana A Tsiantis John J Van Engeland Herman H Vincent John B JB Volkmar Fred F Wallace Simon S Wang Kai K Wang Zhouzhi Z Wassink Thomas H TH Webber Caleb C Weksberg Rosanna R Wing Kirsty K Wittemeyer Kerstin K Wood Shawn S Wu Jing J Yaspan Brian L BL Zurawiecki Danielle D Zwaigenbaum Lonnie L Buxbaum Joseph D JD Cantor Rita M RM Cook Edwin H EH Coon Hilary H Cuccaro Michael L ML Devlin Bernie B Ennis Sean S Gallagher Louise L Geschwind Daniel H DH Gill Michael M Haines Jonathan L JL Hallmayer Joachim J Miller Judith J Monaco Anthony P AP Nurnberger John I JI Jr Paterson Andrew D AD Pericak-Vance Margaret A MA Schellenberg Gerard D GD Szatmari Peter P Vicente Astrid M AM Vieland Veronica J VJ Wijsman Ellen M EM Scherer Stephen W SW Sutcliffe James S JS Betancur Catalina C eng 075491/Z/04 Wellcome Trust United Kingdom AS2077 Autism Speaks United States AS7462 Autism Speaks United States G0601030 Medical Research Council United Kingdom HD055751 HD NICHD NIH HHS United States HD055782 HD NICHD NIH HHS United States HD055784 HD NICHD NIH HHS United States HD35465 HD NICHD NIH HHS United States MC_U137761446 Medical Research Council United Kingdom MH061009 MH NIMH NIH HHS United States MH06359 MH NIMH NIH HHS United States MH066673 MH NIMH NIH HHS United States MH080647 MH NIMH NIH HHS United States MH081754 MH NIMH NIH HHS United States MH52708 MH NIMH NIH HHS United States MH55284 MH NIMH NIH HHS United States MH57881 MH NIMH NIH HHS United States MH66766 MH NIMH NIH HHS United States NS026630 NS NINDS NIH HHS United States NS042165 NS NINDS NIH HHS United States NS049261 NS NINDS NIH HHS United States P01 CA089392 CA NCI NIH HHS United States P01 CA089392-08 CA NCI NIH HHS United States P01 HD035465-01S1 HD NICHD NIH HHS United States P01 NS026630 NS NINDS NIH HHS United States P01 NS026630-15 NS NINDS NIH HHS United States P50 HD055748 HD NICHD NIH HHS United States P50 HD055748-01 HD NICHD NIH HHS United States P50 HD055748-02 HD NICHD NIH HHS United States P50 HD055748-03 HD NICHD NIH HHS United States P50 HD055751 HD NICHD NIH HHS United States P50 HD055751-01 HD NICHD NIH HHS United States P50 HD055782 HD NICHD NIH HHS United States P50 HD055782-04 HD NICHD NIH HHS United States R01 DA013423 DA NIDA NIH HHS United States R01 DA013423-05 DA NIDA NIH HHS United States R01 DA019963 DA NIDA NIH HHS United States R01 DA019963-01A2 DA NIDA NIH HHS United States R01 DA019963-02 DA NIDA NIH HHS United States R01 DA019963-03 DA NIDA NIH HHS United States R01 MH052708-05 MH NIMH NIH HHS United States R01 MH055284 MH NIMH NIH HHS United States R01 MH055284-04 MH NIMH NIH HHS United States R01 MH057881 MH NIMH NIH HHS United States R01 MH057881-02 MH NIMH NIH HHS United States R01 MH061009 MH NIMH NIH HHS United States R01 MH061009-05 MH NIMH NIH HHS United States R01 MH080647 MH NIMH NIH HHS United States R01 MH080647-11 MH NIMH NIH HHS United States R01 MH081754 MH NIMH NIH HHS United States R01 MH081754-01 MH NIMH NIH HHS United States R01 NS042165 NS NINDS NIH HHS United States R01 NS042165-05 NS NINDS NIH HHS United States R01 NS049261 NS NINDS NIH HHS United States R01 NS049261-02 NS NINDS NIH HHS United States U01 HG004422 HG NHGRI NIH HHS United States U01 HG004422-02 HG NHGRI NIH HHS United States U10 MH066766-05 MH NIMH NIH HHS United States U19 HD035469 HD NICHD NIH HHS United States U19 HD035469-06 HD NICHD NIH HHS United States U19 HD035469-07 HD NICHD NIH HHS United States U19 HD035469-08 HD NICHD NIH HHS United States U19 HD035469-09 HD NICHD NIH HHS United States U19 HD035469-10 HD NICHD NIH HHS United States U54 MH066673 MH NIMH NIH HHS United States U54 MH066673-05 MH NIMH NIH HHS United States UL1 TR000448 TR NCATS NIH HHS United States Canadian Institutes of Health Research Canada Medical Research Council United Kingdom Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2010 06 09

England Nature 0410462 0028-0836 IM Nat Genet. 2003 May;34(1):27-9 12669065 Annu Rev Genomics Hum Genet. 2004;5:379-405 15485354 J Neurosci. 2004 Oct 20;24(42):9391-404 15496675 Psychol Med. 1995 Jan;25(1):63-77 7792363 Am J Psychiatry. 2005 Jun;162(6):1133-41 15930062 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 Nat Genet. 2007 Jan;39(1):25-7 17173049 Nat Genet. 2007 Mar;39(3):319-28 17322880 Science. 2007 Apr 20;316(5823):445-9 17363630 Nucleic Acids Res. 2007;35(6):2013-25 17341461 Trends Genet. 2007 Aug;23(8):387-95 17630015 Am J Hum Genet. 2007 Sep;81(3):559-75 17701901 Nat Protoc. 2007;2(10):2366-82 17947979 Genome Res. 2007 Nov;17(11):1665-74 17921354 Hum Mol Genet. 2008 Feb 15;17(4):628-38 18156158 Am J Hum Genet. 2008 Feb;82(2):477-88 18252227 N Engl J Med. 2008 Feb 14;358(7):667-75 18184952 Science. 2008 Jul 11;321(5886):218-23 18621663 Nature. 2008 Sep 11;455(7210):237-41 18668038 Nature. 2008 Oct 16;455(7215):919-23 18923514 N Engl J Med. 2009 Feb 5;360(6):599-605 19196676 Nature. 2009 May 28;459(7246):528-33 19404256 Nature. 2009 May 28;459(7246):569-73 19404257 Proc Natl Acad Sci U S A. 2009 Sep 22;106(38):16434-45 19805316 Nature. 2009 Oct 8;461(7265):802-8 19812673 Genet Epidemiol. 2010 Jan;34(1):51-9 19455578 Proc Natl Acad Sci U S A. 2010 Mar 16;107(11):5082-7 20202923 Nature. 2010 Apr 1;464(7289):704-12 19812545 Acta Neuropathol. 2010 Jun;119(6):755-70 20198484 Mol Psychiatry. 2011 Mar;16(3):286-92 20157312 Case-Control Studies Cell Movement Child Child Development Disorders, Pervasive genetics pathology physiopathology Cytoprotection DNA Copy Number Variations genetics Europe ethnology Gene Dosage genetics Genetic Predisposition to Disease genetics Genome-Wide Association Study Humans Signal Transduction Social Behavior NIHMS237855 PMC3021798 2009 12 03 2010 5 07 2010 6 09 2010 6 10 6 0 2010 6 10 6 0 2010 8 31 6 0 ppublish nature09146 10.1038/nature09146 20531469 PMC3021798 NIHMS237855 20512140 2010 06 18 2010 07 16

1552-4469 6 7 2010 Jul Nat. Chem. Biol. A predictive model for drug bioaccumulation and bioactivity in Caenorhabditis elegans. 549-57 10.1038/nchembio.380 The resistance of Caenorhabditis elegans to pharmacological perturbation limits its use as a screening tool for novel small bioactive molecules. One strategy to improve the hit rate of small-molecule screens is to preselect molecules that have an increased likelihood of reaching their target in the worm. To learn which structures evade the worm's defenses, we performed the first survey of the accumulation and metabolism of over 1,000 commercially available drug-like small molecules in the worm. We discovered that fewer than 10% of these molecules accumulate to concentrations greater than 50% of that present in the worm's environment. Using our dataset, we developed a structure-based accumulation model that identifies compounds with an increased likelihood of bioavailability and bioactivity, and we describe structural features that facilitate small-molecule accumulation in the worm. Preselecting molecules that are more likely to reach a target by first applying our model to the tens of millions of commercially available compounds will undoubtedly increase the success of future small-molecule screens with C. elegans. Burns Andrew R AR Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. Wallace Iain M IM Wildenhain Jan J Tyers Mike M Giaever Guri G Bader Gary D GD Nislow Corey C Cutler Sean R SR Roy Peter J PJ eng PubChem-Substance 92763262 92763263 92763264 92763265 92763266 92763267 92763268 92763269 92763270 92763271 92763272 92763273 68813 Canadian Institutes of Health Research Canada MOP-81340 Canadian Institutes of Health Research Canada Journal Article Research Support, Non-U.S. Gov't 2010 05 30

United States Nat Chem Biol 101231976 1552-4450 0 Pharmaceutical Preparations IM Nat Chem Biol. 2010 Jul;6(7):482-3 20559312 Animals Caenorhabditis elegans metabolism Chromatography, High Pressure Liquid methods Drug Evaluation, Preclinical methods Models, Biological Molecular Structure Pharmaceutical Preparations chemistry metabolism Structure-Activity Relationship 2009 10 05 2010 4 26 2010 5 30 2010 6 1 6 0 2010 6 1 6 0 2010 7 17 6 0 ppublish nchembio.380 10.1038/nchembio.380 20512140 20393554 2010 04 15 2010 06 02 2014 09 08

1476-4687 464 7291 2010 Apr 15 Nature International network of cancer genome projects. 993-8 10.1038/nature08987 The International Cancer Genome Consortium (ICGC) was launched to coordinate large-scale cancer genome studies in tumours from 50 different cancer types and/or subtypes that are of clinical and societal importance across the globe. Systematic studies of more than 25,000 cancer genomes at the genomic, epigenomic and transcriptomic levels will reveal the repertoire of oncogenic mutations, uncover traces of the mutagenic influences, define clinically relevant subtypes for prognosis and therapeutic management, and enable the development of new cancer therapies. International Cancer Genome Consortium Hudson Thomas J TJ Anderson Warwick W Artez Axel A Barker Anna D AD Bell Cindy C Bernabé Rosa R RR Bhan M K MK Calvo Fabien F Eerola Iiro I Gerhard Daniela S DS Guttmacher Alan A Guyer Mark M Hemsley Fiona M FM Jennings Jennifer L JL Kerr David D Klatt Peter P Kolar Patrik P Kusada Jun J Lane David P DP Laplace Frank F Youyong Lu L Nettekoven Gerd G Ozenberger Brad B Peterson Jane J Rao T S TS Remacle Jacques J Schafer Alan J AJ Shibata Tatsuhiro T Stratton Michael R MR Vockley Joseph G JG Watanabe Koichi K Yang Huanming H Yuen Matthew M F MM Knoppers Bartha M BM Bobrow Martin M Cambon-Thomsen Anne A Dressler Lynn G LG Dyke Stephanie O M SO Joly Yann Y Kato Kazuto K Kennedy Karen L KL Nicolás Pilar P Parker Michael J MJ Rial-Sebbag Emmanuelle E Romeo-Casabona Carlos M CM Shaw Kenna M KM Wallace Susan S Wiesner Georgia L GL Zeps Nikolajs N Lichter Peter P Biankin Andrew V AV Chabannon Christian C Chin Lynda L Clément Bruno B de Alava Enrique E Degos Françoise F Ferguson Martin L ML Geary Peter P Hayes D Neil DN Hudson Thomas J TJ Johns Amber L AL Kasprzyk Arek A Nakagawa Hidewaki H Penny Robert R Piris Miguel A MA Sarin Rajiv R Scarpa Aldo A Shibata Tatsuhiro T van de Vijver Marc M Futreal P Andrew PA Aburatani Hiroyuki H Bayés Mónica M Botwell David D L DD Campbell Peter J PJ Estivill Xavier X Gerhard Daniela S DS Grimmond Sean M SM Gut Ivo I Hirst Martin M López-Otín Carlos C Majumder Partha P Marra Marco M McPherson John D JD Nakagawa Hidewaki H Ning Zemin Z Puente Xose S XS Ruan Yijun Y Shibata Tatsuhiro T Stratton Michael R MR Stunnenberg Hendrik G HG Swerdlow Harold H Velculescu Victor E VE Wilson Richard K RK Xue Hong H HH Yang Liu L Spellman Paul T PT Bader Gary D GD Boutros Paul C PC Campbell Peter J PJ Flicek Paul P Getz Gad G Guigó Roderic R Guo Guangwu G Haussler David D Heath Simon S Hubbard Tim J TJ Jiang Tao T Jones Steven M SM Li Qibin Q López-Bigas Nuria N Luo Ruibang R Muthuswamy Lakshmi L Ouellette B F Francis BF Pearson John V JV Puente Xose S XS Quesada Victor V Raphael Benjamin J BJ Sander Chris C Shibata Tatsuhiro T Speed Terence P TP Stein Lincoln D LD Stuart Joshua M JM Teague Jon W JW Totoki Yasushi Y Tsunoda Tatsuhiko T Valencia Alfonso A Wheeler David A DA Wu Honglong H Zhao Shancen S Zhou Guangyu G Stein Lincoln D LD Guigó Roderic R Hubbard Tim J TJ Joly Yann Y Jones Steven M SM Kasprzyk Arek A Lathrop Mark M López-Bigas Nuria N Ouellette B F Francis BF Spellman Paul T PT Teague Jon W JW Thomas Gilles G Valencia Alfonso A Yoshida Teruhiko T Kennedy Karen L KL Axton Myles M Dyke Stephanie O M SO Futreal P Andrew PA Gerhard Daniela S DS Gunter Chris C Guyer Mark M Hudson Thomas J TJ McPherson John D JD Miller Linda J LJ Ozenberger Brad B Shaw Kenna M KM Kasprzyk Arek A Stein Lincoln D LD Zhang Junjun J Haider Syed A SA Wang Jianxin J Yung Christina K CK Cros Anthony A Cross Anthony A Liang Yong Y Gnaneshan Saravanamuttu S Guberman Jonathan J Hsu Jack J Bobrow Martin M Chalmers Don R C DR Hasel Karl W KW Joly Yann Y Kaan Terry S H TS Kennedy Karen L KL Knoppers Bartha M BM Lowrance William W WW Masui Tohru T Nicolás Pilar P Rial-Sebbag Emmanuelle E Rodriguez Laura Lyman LL Vergely Catherine C Yoshida Teruhiko T Grimmond Sean M SM Biankin Andrew V AV Bowtell David D L DD Cloonan Nicole N deFazio Anna A Eshleman James R JR Etemadmoghadam Dariush D Gardiner Brooke B BB Gardiner Brooke A BA Kench James G JG Scarpa Aldo A Sutherland Robert L RL Tempero Margaret A MA Waddell Nicola J NJ Wilson Peter J PJ McPherson John D JD Gallinger Steve S Tsao Ming-Sound MS Shaw Patricia A PA Petersen Gloria M GM Mukhopadhyay Debabrata D Chin Lynda L DePinho Ronald A RA Thayer Sarah S Muthuswamy Lakshmi L Shazand Kamran K Beck Timothy T Sam Michelle M Timms Lee L Ballin Vanessa V Lu Youyong Y Ji Jiafu J Zhang Xiuqing X Chen Feng F Hu Xueda X Zhou Guangyu G Yang Qi Q Tian Geng G Zhang Lianhai L Xing Xiaofang X Li Xianghong X Zhu Zhenggang Z Yu Yingyan Y Yu Jun J Yang Huanming H Lathrop Mark M Tost Jörg J Brennan Paul P Holcatova Ivana I Zaridze David D Brazma Alvis A Egevard Lars L Prokhortchouk Egor E Banks Rosamonde Elizabeth RE Uhlén Mathias M Cambon-Thomsen Anne A Viksna Juris J Ponten Fredrik F Skryabin Konstantin K Stratton Michael R MR Futreal P Andrew PA Birney Ewan E Borg Ake A Børresen-Dale Anne-Lise AL Caldas Carlos C Foekens John A JA Martin Sancha S Reis-Filho Jorge S JS Richardson Andrea L AL Sotiriou Christos C Stunnenberg Hendrik G HG Thoms Giles G van de Vijver Marc M van't Veer Laura L Calvo Fabien F Birnbaum Daniel D Blanche Hélène H Boucher Pascal P Boyault Sandrine S Chabannon Christian C Gut Ivo I Masson-Jacquemier Jocelyne D JD Lathrop Mark M Pauporté Iris I Pivot Xavier X Vincent-Salomon Anne A Tabone Eric E Theillet Charles C Thomas Gilles G Tost Jörg J Treilleux Isabelle I Calvo Fabien F Bioulac-Sage Paulette P Clément Bruno B Decaens Thomas T Degos Françoise F Franco Dominique D Gut Ivo I Gut Marta M Heath Simon S Lathrop Mark M Samuel Didier D Thomas Gilles G Zucman-Rossi Jessica J Lichter Peter P Eils Roland R Brors Benedikt B Korbel Jan O JO Korshunov Andrey A Landgraf Pablo P Lehrach Hans H Pfister Stefan S Radlwimmer Bernhard B Reifenberger Guido G Taylor Michael D MD von Kalle Christof C Majumder Partha P PP Sarin Rajiv R Rao T S TS Bhan M K MK Scarpa Aldo A Pederzoli Paolo P Lawlor Rita A RA Delledonne Massimo M Bardelli Alberto A Biankin Andrew V AV Grimmond Sean M SM Gress Thomas T Klimstra David D Zamboni Giuseppe G Shibata Tatsuhiro T Nakamura Yusuke Y Nakagawa Hidewaki H Kusada Jun J Tsunoda Tatsuhiko T Miyano Satoru S Aburatani Hiroyuki H Kato Kazuto K Fujimoto Akihiro A Yoshida Teruhiko T Campo Elias E López-Otín Carlos C Estivill Xavier X Guigó Roderic R de Sanjosé Silvia S Piris Miguel A MA Montserrat Emili E González-Díaz Marcos M Puente Xose S XS Jares Pedro P Valencia Alfonso A Himmelbauer Heinz H Himmelbaue Heinz H Quesada Victor V Bea Silvia S Stratton Michael R MR Futreal P Andrew PA Campbell Peter J PJ Vincent-Salomon Anne A Richardson Andrea L AL Reis-Filho Jorge S JS van de Vijver Marc M Thomas Gilles G Masson-Jacquemier Jocelyne D JD Aparicio Samuel S Borg Ake A Børresen-Dale Anne-Lise AL Caldas Carlos C Foekens John A JA Stunnenberg Hendrik G HG van't Veer Laura L Easton Douglas F DF Spellman Paul T PT Martin Sancha S Barker Anna D AD Chin Lynda L Collins Francis S FS Compton Carolyn C CC Ferguson Martin L ML Gerhard Daniela S DS Getz Gad G Gunter Chris C Guttmacher Alan A Guyer Mark M Hayes D Neil DN Lander Eric S ES Ozenberger Brad B Penny Robert R Peterson Jane J Sander Chris C Shaw Kenna M KM Speed Terence P TP Spellman Paul T PT Vockley Joseph G JG Wheeler David A DA Wilson Richard K RK Hudson Thomas J TJ Chin Lynda L Knoppers Bartha M BM Lander Eric S ES Lichter Peter P Stein Lincoln D LD Stratton Michael R MR Anderson Warwick W Barker Anna D AD Bell Cindy C Bobrow Martin M Burke Wylie W Collins Francis S FS Compton Carolyn C CC DePinho Ronald A RA Easton Douglas F DF Futreal P Andrew PA Gerhard Daniela S DS Green Anthony R AR Guyer Mark M Hamilton Stanley R SR Hubbard Tim J TJ Kallioniemi Olli P OP Kennedy Karen L KL Ley Timothy J TJ Liu Edison T ET Lu Youyong Y Majumder Partha P Marra Marco M Ozenberger Brad B Peterson Jane J Schafer Alan J AJ Spellman Paul T PT Stunnenberg Hendrik G HG Wainwright Brandon J BJ Wilson Richard K RK Yang Huanming H eng 077198 Wellcome Trust United Kingdom 088340 Wellcome Trust United Kingdom 093867 Wellcome Trust United Kingdom 6613 Cancer Research UK United Kingdom K08 DK071329 DK NIDDK NIH HHS United States K08 DK071329-04 DK NIDDK NIH HHS United States K08 DK071329-05 DK NIDDK NIH HHS United States P01 CA117969 CA NCI NIH HHS United States P01 CA117969-04S1 CA NCI NIH HHS United States P01 CA117969-05 CA NCI NIH HHS United States P50 CA102701 CA NCI NIH HHS United States P50 CA102701-08 CA NCI NIH HHS United States P50 CA127003 CA NCI NIH HHS United States P50 CA127003-04 CA NCI NIH HHS United States P50 CA127003-05 CA NCI NIH HHS United States R01 HG001806-02 HG NHGRI NIH HHS United States Journal Article

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1615-9861 10 6 2010 Mar Proteomics Pathway analysis of dilated cardiomyopathy using global proteomic profiling and enrichment maps. 1316-27 10.1002/pmic.200900412 Global protein expression profiling can potentially uncover perturbations associated with common forms of heart disease. We have used shotgun MS/MS to monitor the state of biological systems in cardiac tissue correlating with disease onset, cardiac insufficiency and progression to heart failure in a time-course mouse model of dilated cardiomyopathy. However, interpreting the functional significance of the hundreds of differentially expressed proteins has been challenging. Here, we utilize improved enrichment statistical methods and an extensive collection of functionally related gene sets, gaining a more comprehensive understanding of the progressive alterations associated with functional decline in dilated cardiomyopathy. We visualize the enrichment results as an Enrichment Map, where significant gene sets are grouped based on annotation similarity. This approach vastly simplifies the interpretation of the large number of enriched gene sets found. For pathways of specific interest, such as Apoptosis and the MAPK (mitogen-activated protein kinase) cascade, we performed a more detailed analysis of the underlying signaling network, including experimental validation of expression patterns. Isserlin Ruth R Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada. Merico Daniele D Alikhani-Koupaei Rasoul R Gramolini Anthony A Bader Gary D GD Emili Andrew A eng 106538-1 Canadian Institutes of Health Research Canada 87143-1 Canadian Institutes of Health Research Canada P41 HG004118 HG NHGRI NIH HHS United States P41 HG004118-01A1 HG NHGRI NIH HHS United States P41 HG004118-02 HG NHGRI NIH HHS United States P41 P41HG04118 HG NHGRI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Germany Proteomics 101092707 1615-9853 0 Gelsolin 9Y8NXQ24VQ Propranolol EC 3.4.22.- Caspase 3 IM J Am Coll Cardiol. 2000 Mar 1;35(3):569-82 10716457 J Proteome Res. 2009 Apr;8(4):1887-901 19714876 Circulation. 2000 Dec 19;102(25):3046-52 11120693 Nat Rev Genet. 2001 Jun;2(6):418-27 11389458 Proc Natl Acad Sci U S A. 2001 Oct 9;98(21):12283-8 11593045 Science. 2003 Feb 28;299(5611):1410-3 12610310 Genome Biol. 2003;4(5):P3 12734009 J Clin Invest. 2003 May;111(10):1475-86 12750397 EMBO J. 2003 Oct 1;22(19):5079-89 14517246 BMC Bioinformatics. 2002 Nov 13;3:35 12431279 Genome Res. 2003 Nov;13(11):2498-504 14597658 Circulation. 2004 Jan 20;109(2):150-8 14734503 Bioinformatics. 2004 Mar 1;20(4):578-80 14990455 Anal Chem. 2004 Jul 15;76(14):4193-201 15253663 Science. 1997 Oct 10;278(5336):294-8 9323209 J Clin Invest. 2005 Mar;115(3):547-55 15765136 J Clin Invest. 2005 Mar;115(3):565-71 15765138 J Am Soc Mass Spectrom. 2005 Aug;16(8):1207-20 15979338 Bioinformatics. 2005 Aug 15;21(16):3448-9 15972284 BMC Bioinformatics. 2005;6:168 15998470 Annu Rev Genomics Hum Genet. 2005;6:185-216 16124859 Bioinformatics. 2005 Sep 15;21(18):3587-95 15994189 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 BMC Bioinformatics. 2005;6:269 16280084 Cell. 2006 Apr 7;125(1):173-86 16615898 Cardiovasc Res. 2006 Jun 1;70(3):422-33 16466704 J Am Coll Cardiol. 2006 Nov 7;48(9):1733-41 17084242 Methods Mol Biol. 2007;357:15-31 17172675 N Engl J Med. 2006 Dec 21;355(25):2631-9 17182988 Nat Biotechnol. 2007 Jan;25(1):117-24 17187058 J Chromatogr B Analyt Technol Biomed Life Sci. 2007 Feb 15;847(1):3-11 17023222 N Engl J Med. 2007 Mar 15;356(11):1140-51 17360992 Circulation. 2007 Mar 20;115(11):1456-63 17372187 Science. 2007 Apr 27;316(5824):575-9 17379774 Nat Med. 2007 May;13(5):613-8 17468766 Am J Pathol. 2007 Jun;170(6):1831-40 17525252 Can J Cardiol. 2007 Aug;23 Suppl A:28A-33A 17668085 Nucleic Acids Res. 2008 Jan;36(Database issue):D646-50 17965090 Nucleic Acids Res. 2008 Jan;36(Database issue):D623-31 17965431 Can J Cardiol. 2008 Jan;24(1):21-40 18209766 Circulation. 2008 Jan 29;117(4):e25-146 18086926 Nature. 2008 Feb 21;451(7181):919-28 18288181 Mol Cell Proteomics. 2008 Mar;7(3):519-33 18056057 Curr Opin Mol Ther. 2008 Jun;10(3):231-42 18535930 Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9 18832364 Nucleic Acids Res. 2009 Jan;37(Database issue):D619-22 18981052 Cardiovasc Res. 2009 Feb 15;81(3):465-73 18779231 Artif Intell Med. 2009 Feb-Mar;45(2-3):97-107 18789659 Circ Res. 2009 Apr 10;104(7):896-904 19246681 Proc Natl Acad Sci U S A. 2009 Apr 28;106(17):7143-8 19357309 Science. 2009 May 15;324(5929):895-9 19443776 Science. 2009 May 22;324(5930):1029-33 19460998 Nucleic Acids Res. 2009 Jul;37(Web Server issue):W23-7 19420058 Mayo Clin Proc. 2009 Aug;84(8):718-29 19648389 Nat Genet. 2000 May;25(1):25-9 10802651 Animals Apoptosis physiology Cardiomyopathy, Dilated drug therapy genetics physiopathology Caspase 3 physiology Databases, Protein Gelsolin physiology Gene Expression Profiling MAP Kinase Signaling System physiology Metabolomics Mice Propranolol therapeutic use Proteomics methods Systems Biology Tandem Mass Spectrometry NIHMS201980 PMC2879143 2010 2 4 6 0 2010 2 4 6 0 2010 6 19 6 0 ppublish 10.1002/pmic.200900412 20127684 PMC2879143 NIHMS201980 20093466 2010 01 22 2010 02 02 2014 12 05

1095-9203 327 5964 2010 Jan 22 Science The genetic landscape of a cell. 425-31 10.1126/science.1180823 A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification. Costanzo Michael M Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada. Baryshnikova Anastasia A Bellay Jeremy J Kim Yungil Y Spear Eric D ED Sevier Carolyn S CS Ding Huiming H Koh Judice L Y JL Toufighi Kiana K Mostafavi Sara S Prinz Jeany J St Onge Robert P RP VanderSluis Benjamin B Makhnevych Taras T Vizeacoumar Franco J FJ Alizadeh Solmaz S Bahr Sondra S Brost Renee L RL Chen Yiqun Y Cokol Murat M Deshpande Raamesh R Li Zhijian Z Lin Zhen-Yuan ZY Liang Wendy W Marback Michaela M Paw Jadine J San Luis Bryan-Joseph BJ Shuteriqi Ermira E Tong Amy Hin Yan AH van Dyk Nydia N Wallace Iain M IM Whitney Joseph A JA Weirauch Matthew T MT Zhong Guoqing G Zhu Hongwei H Houry Walid A WA Brudno Michael M Ragibizadeh Sasan S Papp Balázs B Pál Csaba C Roth Frederick P FP Giaever Guri G Nislow Corey C Troyanskaya Olga G OG Bussey Howard H Bader Gary D GD Gingras Anne-Claude AC Morris Quaid D QD Kim Philip M PM Kaiser Chris A CA Myers Chad L CL Andrews Brenda J BJ Boone Charles C eng 084314 Wellcome Trust United Kingdom GSP-41567 Canadian Institutes of Health Research Canada R01 HG003224 HG NHGRI NIH HHS United States Journal Article Research Support, Non-U.S. Gov't

United States Science 0404511 0036-8075 0 Saccharomyces cerevisiae Proteins IM Nat Rev Genet. 2010 Mar;11(3):172 21485431 Computational Biology Gene Duplication Gene Expression Regulation, Fungal Gene Regulatory Networks Genes, Fungal Genetic Fitness Genome, Fungal Metabolic Networks and Pathways Mutation Protein Interaction Mapping Saccharomyces cerevisiae genetics metabolism physiology Saccharomyces cerevisiae Proteins genetics metabolism 2010 1 23 6 0 2010 1 23 6 0 2010 2 3 6 0 ppublish 327/5964/425 10.1126/science.1180823 20093466 20067622 2010 03 31 2010 07 28 2015 08 04

1474-760X 11 1 2010 Genome Biol. NetPath: a public resource of curated signal transduction pathways. R3 10.1186/gb-2010-11-1-r3 We have developed NetPath as a resource of curated human signaling pathways. As an initial step, NetPath provides detailed maps of a number of immune signaling pathways, which include approximately 1,600 reactions annotated from the literature and more than 2,800 instances of transcriptionally regulated genes - all linked to over 5,500 published articles. We anticipate NetPath to become a consolidated resource for human signaling pathways that should enable systems biology approaches. Kandasamy Kumaran K Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. kumaran@ibioinformatics.org Mohan S Sujatha SS Raju Rajesh R Keerthikumar Shivakumar S Kumar Ghantasala S Sameer GS Venugopal Abhilash K AK Telikicherla Deepthi D Navarro J Daniel JD Mathivanan Suresh S Pecquet Christian C Gollapudi Sashi Kanth SK Tattikota Sudhir Gopal SG Mohan Shyam S Padhukasahasram Hariprasad H Subbannayya Yashwanth Y Goel Renu R Jacob Harrys K C HK Zhong Jun J Sekhar Raja R Nanjappa Vishalakshi V Balakrishnan Lavanya L Subbaiah Roopashree R Ramachandra Y L YL Rahiman B Abdul BA Prasad T S Keshava TS Lin Jian-Xin JX Houtman Jon C D JC Desiderio Stephen S Renauld Jean-Christophe JC Constantinescu Stefan N SN Ohara Osamu O Hirano Toshio T Kubo Masato M Singh Sujay S Khatri Purvesh P Draghici Sorin S Bader Gary D GD Sander Chris C Leonard Warren J WJ Pandey Akhilesh A eng CA 88843 CA NCI NIH HHS United States R01 DK089167 DK NIDDK NIH HHS United States U54 RR020839 RR NCRR NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S. 2010 01 12

England Genome Biol 100960660 1474-7596 0 Interleukin-2 IM Intensive Care Med. 2007 Oct;33(10):1829-39 17581740 FEBS Lett. 2005 Mar 21;579(8):1815-20 15763557 PLoS Biol. 2008 Jul 22;6(7):e184 18651794 BMC Bioinformatics. 2008;9:399 18817533 Nucleic Acids Res. 2009 Jan;37(Database issue):D773-81 18948298 Nucleic Acids Res. 2009 Jan;37(Database issue):D767-72 18988627 Bioinformatics. 2009 Nov 1;25(21):2860-2 19628504 Bioinformatics. 2002 Jul;18(7):996-1003 12117798 Bioinformatics. 2003 Mar 1;19(4):524-31 12611808 Immunol Rev. 2003 Jun;193:70-81 12752672 Genome Res. 2003 Sep;13(9):2129-41 12952881 FEBS Lett. 2005 Mar 21;579(8):1821-7 15763558 J Immunol. 2005 Aug 15;175(4):2570-8 16081831 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6 16381921 Nucleic Acids Res. 2000 Jan 1;28(1):27-30 10592173 Nature. 2001 May 17;411(6835):380-4 11357146 Bioinformatics. 2001 Sep;17(9):829-37 11590099 Cytokine Growth Factor Rev. 2002 Feb;13(1):27-40 11750878 AIDS Rev. 2002 Jan-Mar;4(1):36-40 11998783 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D527-34 16381926 Cytokine Growth Factor Rev. 2006 Oct;17(5):349-66 16911870 Nucleic Acids Res. 2007 Jan;35(Database issue):D760-5 17099226 Genome Res. 2003 Nov;13(11):2498-504 14597658 Nat Biotechnol. 2004 Feb;22(2):177-83 14755292 Ann N Y Acad Sci. 2004 May;1020:77-91 15208185 Nat Genet. 2004 Jul;36(7):664 15226743 Adv Exp Med Biol. 2004;547:21-30 15230090 Nat Rev Immunol. 2004 Sep;4(9):665-74 15343366 Bioinformatics. 2004 Sep 22;20(14):2236-41 15059817 Science. 1976 Sep 10;193(4257):1007-8 181845 J Exp Med. 1982 Jun 1;155(6):1823-41 6176669 Nature. 1984 Dec 13-19;312(5995):641-3 6438535 Annu Rev Immunol. 1986;4:69-95 3011033 J Immunol. 1986 Dec 15;137(12):3845-54 3491151 Cell. 1993 Apr 9;73(1):147-57 8462096 Semin Immunol. 1993 Oct;5(5):309-17 8260647 Proc Natl Acad Sci U S A. 1997 Apr 15;94(8):3627-32 9108028 Cytokine Growth Factor Rev. 1997 Dec;8(4):313-32 9620644 Nucleic Acids Res. 2005 Jan 1;33(Database issue):D428-32 15608231 BMC Genomics. 2004;5(1):93 15588328 Genome Res. 2007 Oct;17(10):1537-45 17785539 Access to Information Animals Apoptosis Biochemistry methods Cell Movement Computational Biology methods Databases, Factual Humans Immune System Interleukin-2 metabolism Models, Biological Models, Genetic Protein Interaction Mapping Signal Transduction Software Transcription, Genetic PMC2847715 2009 4 21 2009 11 2 2010 1 12 2010 1 12 2010 1 14 6 0 2010 1 14 6 0 2010 7 29 6 0 epublish gb-2010-11-1-r3 10.1186/gb-2010-11-1-r3 20067622 PMC2847715 19880385 2009 12 22 2010 02 01 2014 08 27

1362-4962 38 Database issue 2010 Jan Nucleic Acids Res. DRYGIN: a database of quantitative genetic interaction networks in yeast. D502-7 10.1093/nar/gkp820 Genetic interactions are highly informative for deciphering the underlying functional principles that govern how genes control cell processes. Recent developments in Synthetic Genetic Array (SGA) analysis enable the mapping of quantitative genetic interactions on a genome-wide scale. To facilitate access to this resource, which will ultimately represent a complete genetic interaction network for a eukaryotic cell, we developed DRYGIN (Data Repository of Yeast Genetic Interactions)-a web database system that aims at providing a central platform for yeast genetic network analysis and visualization. In addition to providing an interface for searching the SGA genetic interactions, DRYGIN also integrates other data sources, in order to associate the genetic interactions with pathway information, protein complexes, other binary genetic and physical interactions, and Gene Ontology functional annotation. DRYGIN version 1.0 currently holds more than 5.4 million measurements of genetic interacting pairs involving approximately 4500 genes, and is available at http://drygin.ccbr.utoronto.ca. Koh Judice L Y JL Banting and Best Department of Medical Research, The Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, ON, Canada. judice.koh@utoronto.ca Ding Huiming H Costanzo Michael M Baryshnikova Anastasia A Toufighi Kiana K Bader Gary D GD Myers Chad L CL Andrews Brenda J BJ Boone Charles C eng Journal Article Research Support, Non-U.S. Gov't 2009 10 30

England Nucleic Acids Res 0411011 0305-1048 0 Fungal Proteins IM Genome Res. 2003 Nov;13(11):2498-504 14597658 Science. 2001 Dec 14;294(5550):2364-8 11743205 Science. 2004 Feb 6;303(5659):808-13 14764870 Bioinformatics. 2004 Nov 22;20(17):3246-8 15180930 Nat Genet. 2005 Jan;37(1):77-83 15592468 Bioinformatics. 2005 Apr 15;21(8):1741-2 15613388 Cell. 2005 Nov 4;123(3):507-19 16269340 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D442-5 16381907 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D535-9 16381927 Nat Rev Genet. 2007 Jun;8(6):437-49 17510664 Nat Rev Genet. 2007 Sep;8(9):699-710 17703239 Nucleic Acids Res. 2008 Jan;36(Database issue):D480-4 18077471 Nucleic Acids Res. 2008 Jan;36(Database issue):D196-201 18158298 PLoS Comput Biol. 2008 Apr;4(4):e1000065 18421374 Proc Natl Acad Sci U S A. 2008 Oct 28;105(43):16653-8 18931302 Nucleic Acids Res. 2009 Jan;37(Database issue):D619-22 18981052 Nucleic Acids Res. 2009 Feb;37(3):825-31 19095691 Nat Biotechnol. 2004 Feb;22(2):177-83 14755292 Computational Biology methods trends Databases, Genetic Databases, Nucleic Acid Databases, Protein Fungal Proteins genetics Genes, Fungal Genome, Fungal Information Storage and Retrieval methods Internet Models, Genetic Protein Interaction Mapping Protein Structure, Tertiary Software PMC2808960 2009 10 30 2009 11 3 6 0 2009 11 3 6 0 2010 2 2 6 0 ppublish gkp820 10.1093/nar/gkp820 19880385 PMC2808960 19841731 2009 10 20 2010 02 17 2014 08 27

1545-7885 7 10 2009 Oct PLoS Biol. Bayesian modeling of the yeast SH3 domain interactome predicts spatiotemporal dynamics of endocytosis proteins. e1000218 10.1371/journal.pbio.1000218 SH3 domains are peptide recognition modules that mediate the assembly of diverse biological complexes. We scanned billions of phage-displayed peptides to map the binding specificities of the SH3 domain family in the budding yeast, Saccharomyces cerevisiae. Although most of the SH3 domains fall into the canonical classes I and II, each domain utilizes distinct features of its cognate ligands to achieve binding selectivity. Furthermore, we uncovered several SH3 domains with specificity profiles that clearly deviate from the two canonical classes. In conjunction with phage display, we used yeast two-hybrid and peptide array screening to independently identify SH3 domain binding partners. The results from the three complementary techniques were integrated using a Bayesian algorithm to generate a high-confidence yeast SH3 domain interaction map. The interaction map was enriched for proteins involved in endocytosis, revealing a set of SH3-mediated interactions that underlie formation of protein complexes essential to this biological pathway. We used the SH3 domain interaction network to predict the dynamic localization of several previously uncharacterized endocytic proteins, and our analysis suggests a novel role for the SH3 domains of Lsb3p and Lsb4p as hubs that recruit and assemble several endocytic complexes. Tonikian Raffi R Terrence Donnelly Center for Cellular and Biomolecular Research, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada. Xin Xiaofeng X Toret Christopher P CP Gfeller David D Landgraf Christiane C Panni Simona S Paoluzi Serena S Castagnoli Luisa L Currell Bridget B Seshagiri Somasekar S Yu Haiyuan H Winsor Barbara B Vidal Marc M Gerstein Mark B MB Bader Gary D GD Volkmer Rudolf R Cesareni Gianni G Drubin David G DG Kim Philip M PM Sidhu Sachdev S SS Boone Charles C eng GM R01 50399 GM NIGMS NIH HHS United States MOP-84324 Canadian Institutes of Health Research Canada Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2009 10 20

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1546-1696 27 10 2009 Oct Nat. Biotechnol. How to visually interpret biological data using networks. 921-4 10.1038/nbt.1567 Networks in biology can appear complex and difficult to decipher. We illustrate how to interpret biological networks with the help of frequently used visualization and analysis patterns. Merico Daniele D Terrence Donnelly Centre for Cellular and Biomolecular Research and Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada. Gfeller David D Bader Gary D GD eng R01 GM070743 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't

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1937-9145 2 87 2009 Sci Signal Rapid evolution of functional complexity in a domain family. ra50 10.1126/scisignal.2000416 Multicellular organisms rely on complex, fine-tuned protein networks to respond to environmental changes. We used in vitro evolution to explore the role of domain mutation and expansion in the evolution of network complexity. Using random mutagenesis to facilitate family expansion, we asked how versatile and robust the binding site must be to produce the rich functional diversity of the natural PDZ domain family. From a combinatorial protein library, we analyzed several hundred structured domain variants and found that one-quarter were functional for carboxyl-terminal ligand recognition and that our variant repertoire was as specific and diverse as the natural family. Our results show that ligand binding is hardwired in the PDZ fold and suggest that this flexibility may facilitate the rapid evolution of complex protein interaction networks. Ernst Andreas A Department of Protein Engineering, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA. Sazinsky Stephen L SL Hui Shirley S Currell Bridget B Dharsee Moyez M Seshagiri Somasekar S Bader Gary D GD Sidhu Sachdev S SS eng Journal Article 2009 09 08

United States Sci Signal 101465400 0 Adaptor Proteins, Signal Transducing 0 ERBB2IP protein, human 0 Ligands IM Sci Signal. 2010;3(139):pe31 20841566 Adaptor Proteins, Signal Transducing chemistry genetics Animals Binding Sites genetics Directed Molecular Evolution Humans Ligands Mutagenesis Protein Structure, Tertiary 2009 9 10 6 0 2009 9 10 6 0 2009 12 16 6 0 epublish 2/87/ra50 10.1126/scisignal.2000416 19738200 19638616 2009 07 29 2009 10 26 2012 06 08

1937-9145 2 81 2009 Sci Signal Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases. ra39 10.1126/scisignal.2000316 Protein kinases enable cellular information processing. Although numerous human phosphorylation sites and their dynamics have been characterized, the evolutionary history and physiological importance of many signaling events remain unknown. Using target phosphoproteomes determined with a similar experimental and computational pipeline, we investigated the conservation of human phosphorylation events in distantly related model organisms (fly, worm, and yeast). With a sequence-alignment approach, we identified 479 phosphorylation events in 344 human proteins that appear to be positionally conserved over approximately 600 million years of evolution and hence are likely to be involved in fundamental cellular processes. This sequence-alignment analysis suggested that many phosphorylation sites evolve rapidly and therefore do not display strong evolutionary conservation in terms of sequence position in distantly related organisms. Thus, we devised a network-alignment approach to reconstruct conserved kinase-substrate networks, which identified 778 phosphorylation events in 698 human proteins. Both methods identified proteins tightly regulated by phosphorylation as well as signal integration hubs, and both types of phosphoproteins were enriched in proteins encoded by disease-associated genes. We analyzed the cellular functions and structural relationships for these conserved signaling events, noting the incomplete nature of current phosphoproteomes. Assessing phosphorylation conservation at both site and network levels proved useful for exploring both fast-evolving and ancient signaling events. We reveal that multiple complex diseases seem to converge within the conserved networks, suggesting that disease development might rely on common molecular networks. Tan Chris Soon Heng CS Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada M5G 1X5. Bodenmiller Bernd B Pasculescu Adrian A Jovanovic Marko M Hengartner Michael O MO Jørgensen Claus C Bader Gary D GD Aebersold Ruedi R Pawson Tony T Linding Rune R eng Comparative Study Journal Article 2009 07 28

United States Sci Signal 101465400 0 Phosphoproteins 0 Proteins EC 2.7.11.22 Cyclin-Dependent Kinases EC 2.7.11.22 cyclin-dependent kinase-activating kinase IM Amino Acid Sequence Animals Binding Sites Caenorhabditis elegans genetics metabolism Cluster Analysis Cyclin-Dependent Kinases chemistry genetics metabolism Disease Susceptibility metabolism physiopathology Drosophila melanogaster genetics metabolism Evolution, Molecular Humans Molecular Sequence Data Molecular Structure Phosphoproteins classification metabolism Phosphorylation Phylogeny Protein Structure, Tertiary Proteins classification genetics metabolism Proteomics methods Saccharomyces cerevisiae genetics metabolism Sequence Homology, Amino Acid Signal Transduction physiology 2009 7 30 9 0 2009 7 30 9 0 2009 10 27 6 0 epublish 2/81/ra39 10.1126/scisignal.2000316 19638616 19589966 2009 09 25 2009 10 09 2015 06 12

1095-9203 325 5948 2009 Sep 25 Science Positive selection of tyrosine loss in metazoan evolution. 1686-8 10.1126/science.1174301 John Nash showed that within a complex system, individuals are best off if they make the best decision that they can, taking into account the decisions of the other individuals. Here, we investigate whether similar principles influence the evolution of signaling networks in multicellular animals. Specifically, by analyzing a set of metazoan species we observed a striking negative correlation of genomically encoded tyrosine content with biological complexity (as measured by the number of cell types in each organism). We discuss how this observed tyrosine loss correlates with the expansion of tyrosine kinases in the evolution of the metazoan lineage and how it may relate to the optimization of signaling systems in multicellular animals. We propose that this phenomenon illustrates genome-wide adaptive evolution to accommodate beneficial genetic perturbation. Tan Chris Soon Heng CS Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto M5G 1X5, Canada. Pasculescu Adrian A Lim Wendell A WA Pawson Tony T Bader Gary D GD Linding Rune R eng R01 GM055040 GM NIGMS NIH HHS United States R01 GM055040-11 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't 2009 07 09

United States Science 0404511 0036-8075 0 Fungal Proteins 0 Proteins 21820-51-9 Phosphotyrosine 42HK56048U Tyrosine EC 2.7.10.1 Protein-Tyrosine Kinases IM Nature. 2003 Dec 11;426(6967):676-80 14668868 Trends Biochem Sci. 1995 Nov;20(11):470-5 8578591 J Mol Evol. 2008 Dec;67(6):621-30 18937004 Sci Signal. 2008;1(35):ra2 18765831 Proc Natl Acad Sci U S A. 2008 Jul 15;105(28):9674-9 18621719 Proc Natl Acad Sci U S A. 2008 Jul 15;105(28):9680-4 18599463 Brief Funct Genomic Proteomic. 2008 Jan;7(1):17-26 18270216 Nature. 2008 Feb 14;451(7180):783-8 18273011 Cell. 2007 Jun 29;129(7):1415-26 17570479 Nat Rev Mol Cell Biol. 2006 Jul;7(7):473-83 16829979 PLoS Comput Biol. 2006 May;2(5):e48 16733546 Science. 2001 May 18;292(5520):1315-6 11360989 Science. 2009 Sep 25;325(5948):1635-6 19779182 Science. 2011 May 20;332(6032):917; author reply 917 21596977 Adaptation, Physiological Animals Biological Evolution Evolution, Molecular Fungal Proteins chemistry metabolism Glycosylation Humans Methylation Mutation Phosphorylation Phosphotyrosine metabolism Protein Structure, Tertiary Protein-Tyrosine Kinases metabolism Proteins chemistry metabolism Selection, Genetic Signal Transduction Substrate Specificity Tyrosine metabolism NIHMS279874 PMC3066034 2009 7 9 2009 7 9 2009 7 11 9 0 2009 7 11 9 0 2009 10 10 6 0 ppublish 1174301 10.1126/science.1174301 19589966 PMC3066034 NIHMS279874 18828675 2008 10 02 2008 11 12 2014 09 03

1545-7885 6 9 2008 Sep 30 PLoS Biol. A specificity map for the PDZ domain family. e239 10.1371/journal.pbio.0060239 PDZ domains are protein-protein interaction modules that recognize specific C-terminal sequences to assemble protein complexes in multicellular organisms. By scanning billions of random peptides, we accurately map binding specificity for approximately half of the over 330 PDZ domains in the human and Caenorhabditis elegans proteomes. The domains recognize features of the last seven ligand positions, and we find 16 distinct specificity classes conserved from worm to human, significantly extending the canonical two-class system based on position -2. Thus, most PDZ domains are not promiscuous, but rather are fine-tuned for specific interactions. Specificity profiling of 91 point mutants of a model PDZ domain reveals that the binding site is highly robust, as all mutants were able to recognize C-terminal peptides. However, many mutations altered specificity for ligand positions both close and far from the mutated position, suggesting that binding specificity can evolve rapidly under mutational pressure. Our specificity map enables the prediction and prioritization of natural protein interactions, which can be used to guide PDZ domain cell biology experiments. Using this approach, we predicted and validated several viral ligands for the PDZ domains of the SCRIB polarity protein. These findings indicate that many viruses produce PDZ ligands that disrupt host protein complexes for their own benefit, and that highly pathogenic strains target PDZ domains involved in cell polarity and growth. Tonikian Raffi R Terrence Donnelly Center for Cellular and Biomolecular Research, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada. Zhang Yingnan Y Sazinsky Stephen L SL Currell Bridget B Yeh Jung-Hua JH Reva Boris B Held Heike A HA Appleton Brent A BA Evangelista Marie M Wu Yan Y Xin Xiaofeng X Chan Andrew C AC Seshagiri Somasekar S Lasky Laurence A LA Sander Chris C Boone Charles C Bader Gary D GD Sidhu Sachdev S SS eng Journal Article Research Support, Non-U.S. Gov't

United States PLoS Biol 101183755 1544-9173 0 Caenorhabditis elegans Proteins 0 Membrane Proteins 0 Peptides 0 Proteome 0 SCRIB protein, human 0 Tumor Suppressor Proteins 0 Viral Proteins IM J Cell Sci. 2001 Sep;114(Pt 18):3219-31 11591811 Bioinformatics. 2001;17 Suppl 1:S22-9 11472989 J Biol Chem. 2002 Apr 5;277(14):12275-9 11801603 J Biol Chem. 2002 Apr 12;277(15):12906-14 11821434 J Biol Chem. 2002 Jun 14;277(24):21666-74 11929862 Mol Cell. 2002 Jun;9(6):1215-25 12086619 Science. 2002 Oct 25;298(5594):846-50 12399596 Oncogene. 2003 Feb 6;22(5):710-21 12569363 J Biol Chem. 2003 Feb 28;278(9):7645-54 12446668 Science. 2003 Apr 18;300(5618):445-52 12702867 Sci STKE. 2003 Apr 22;2003(179):RE7 12709532 Bioessays. 2003 Jun;25(6):542-53 12766944 Cancer Res. 2003 Aug 1;63(15):4547-51 12907630 Science. 2003 Sep 26;301(5641):1904-8 14512628 Genome Res. 2003 Nov;13(11):2498-504 14597658 BMC Bioinformatics. 2004 Aug 19;5:113 15318951 Nat Rev Neurosci. 2004 Oct;5(10):771-81 15378037 J Mol Biol. 1990 Oct 5;215(3):403-10 2231712 Nucleic Acids Res. 1990 Oct 25;18(20):6097-100 2172928 Cell. 1996 Apr 19;85(2):195-204 8612272 Cell. 1996 Jun 28;85(7):1067-76 8674113 Science. 1997 Jan 3;275(5296):73-7 8974395 Nat Biotechnol. 1997 Apr;15(4):336-42 9094134 EMBO J. 1998 Apr 15;17(8):2285-97 9545241 Proc Biol Sci. 1999 Jan 22;266(1415):163-71 10097391 Science. 1999 Apr 30;284(5415):812-5 10221915 Science. 1999 Oct 8;286(5438):295-9 10514373 Nat Struct Mol Biol. 2004 Nov;11(11):1122-7 15475968 Nat Rev Drug Discov. 2004 Dec;3(12):1047-56 15573103 Annu Rev Biochem. 2005;74:219-45 15952887 Immunity. 2005 Jun;22(6):737-48 15963788 Bioinformatics. 2005 Aug 15;21(16):3448-9 15972284 Oncogene. 2005 Sep 5;24(39):5965-75 16155603 Nature. 2005 Sep 22;437(7058):579-83 16177795 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D247-51 16381856 Cell. 2006 Jan 13;124(1):133-45 16413487 Science. 2006 Mar 17;311(5767):1576-80 16439620 Clin Sci (Lond). 2006 May;110(5):525-41 16597322 PLoS Comput Biol. 2006 May;2(5):e48 16733546 J Biol Chem. 2006 Aug 4;281(31):22299-311 16737968 J Biol Chem. 2006 Aug 4;281(31):22312-20 16737969 Brain Res Rev. 2006 Sep;52(2):305-15 16764936 ACS Chem Biol. 2006 Sep 19;1(8):525-33 17168540 Nucleic Acids Res. 2007 Jan;35(Database issue):D610-7 17148474 Nucleic Acids Res. 2007 Jan;35(Database issue):D590-4 17158159 Nat Protoc. 2007;2(6):1368-86 17545975 Science. 2007 Jul 20;317(5836):364-9 17641200 Protein Sci. 2007 Aug;16(8):1738-50 17656586 Protein Sci. 2007 Nov;16(11):2454-71 17962403 Mol Cell. 2007 Dec 14;28(5):886-98 18082612 Cell. 2008 Mar 7;132(5):846-59 18329370 Nucleic Acids Res. 2000 Jan 1;28(1):231-4 10592234 J Biol Chem. 2000 Jul 14;275(28):21486-91 10887205 Annu Rev Neurosci. 2001;24:1-29 11283303 J Biol Chem. 2001 Nov 9;276(45):42122-30 11509564 Amino Acid Sequence Animals Binding Sites genetics Caenorhabditis elegans Proteins analysis chemistry classification genetics Humans Membrane Proteins chemistry genetics metabolism Models, Molecular Molecular Sequence Data Mutation PDZ Domains Peptides analysis genetics Phylogeny Protein Structure, Secondary Proteome analysis Tumor Suppressor Proteins chemistry genetics metabolism Viral Proteins genetics metabolism PMC2553845 2007 12 14 2008 8 19 2008 10 3 9 0 2008 11 13 9 0 2008 10 3 9 0 ppublish 07-PLBI-RA-4212 10.1371/journal.pbio.0060239 18828675 PMC2553845 18819078 2008 09 26 2008 11 04

1934-340X Chapter 8 2008 Sep Curr Protoc Bioinformatics Exploring biological networks with Cytoscape software. Unit 8.13 10.1002/0471250953.bi0813s23 Cytoscape is a free software package for visualizing, modeling, and analyzing molecular and genetic interaction networks. As a key feature, Cytoscape enables biologists to determine and analyze the interconnectivity of a list of genes or proteins. This unit explains how to use Cytoscape to load and navigate biological network information and view mRNA expression profiles and other functional genomics and proteomics data in the context of the network obtained for genes of interest. Additional analyses that can be performed with Cytoscape are also discussed. (c) 2008 by John Wiley & Sons, Inc. Yeung Natalie N University of Toronto, Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada. Cline Melissa S MS Kuchinsky Allan A Smoot Michael E ME Bader Gary D GD eng GM070743-01 GM NIGMS NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

United States Curr Protoc Bioinformatics 101157830 1934-3396 IM Animals Computational Biology Computer Simulation Database Management Systems utilization Gene Expression Profiling utilization Gene Regulatory Networks Genomics methods Humans Proteomics methods Quantitative Structure-Activity Relationship Software 2008 9 27 9 0 2008 11 5 9 0 2008 9 27 9 0 ppublish 10.1002/0471250953.bi0813s23 18819078 18445605 2008 06 16 2008 07 10 2014 09 03

1367-4811 24 12 2008 Jun 15 Bioinformatics Cytoscape ESP: simple search of complex biological networks. 1465-6 10.1093/bioinformatics/btn208 Cytoscape enhanced search plugin (ESP) enables searching complex biological networks on multiple attribute fields using logical operators and wildcards. Queries use an intuitive syntax and simple search line interface. ESP is implemented as a Cytoscape plugin and complements existing search functions in the Cytoscape network visualization and analysis software, allowing users to easily identify nodes, edges and subgraphs of interest, even for very large networks. Availabiity: http://chianti.ucsd.edu/cyto_web/plugins/ ashkenaz@agri.huji.ac.il. Ashkenazi Maital M Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Hebrew University of Jerusalem, Rehovot, Israel. ashkenaz@agri.huji.ac.il Bader Gary D GD Kuchinsky Allan A Moshelion Menachem M States David J DJ eng DA021519 DA NIDA NIH HHS United States GM070743-01 GM NIGMS NIH HHS United States LM008106 LM NLM NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2008 04 28

England Bioinformatics 9808944 1367-4803 IM Nucleic Acids Res. 2002 Jan 1;30(1):303-5 11752321 Genome Res. 2003 Nov;13(11):2498-504 14597658 Planta. 2003 Nov;218(1):1-14 14513379 Plant J. 2007 Apr;50(2):347-63 17376166 Plant Physiol. 2005 Oct;139(2):790-805 16183846 Plant Physiol. 2005 Nov;139(3):1313-22 16244138 Nucleic Acids Res. 2007 Jan;35(Database issue):D566-71 17130145 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D452-5 14681455 Algorithms Computer Graphics Computer Simulation Information Storage and Retrieval methods Models, Biological Signal Transduction physiology Software User-Computer Interface PMC2427162 2008 4 28 2008 5 1 9 0 2008 7 11 9 0 2008 5 1 9 0 ppublish btn208 10.1093/bioinformatics/btn208 18445605 PMC2427162 18095187 2007 12 20 2008 02 11

1073-6085 38 1 2008 Jan Mol. Biotechnol. Computational prediction of protein-protein interactions. 1-17 Recently a number of computational approaches have been developed for the prediction of protein-protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis. Skrabanek Lucy L Department of Physiology and Biophysics and Institute for Computational Biomedicine, Weill Medical College of Cornell University, 1300 York Avenue, New York, NY 10021, USA. Saini Harpreet K HK Bader Gary D GD Enright Anton J AJ eng Journal Article Review 2007 08 14

United States Mol Biotechnol 9423533 1073-6085 0 Multiprotein Complexes IM Biotechnology Computer Simulation Databases, Genetic Gene Fusion Genomics Models, Molecular Multiprotein Complexes Phylogeny Protein Array Analysis Protein Interaction Mapping statistics & numerical data Proteomics Software Two-Hybrid System Techniques 103 2007 7 4 2007 7 16 2007 8 14 2007 12 21 9 0 2008 2 12 9 0 2007 12 21 9 0 ppublish 10.1007/s12033-007-0069-2 18095187 17947979 2007 10 19 2008 01 22 2014 09 16

1750-2799 2 10 2007 Nat Protoc Integration of biological networks and gene expression data using Cytoscape. 2366-82 Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape. Cline Melissa S MS Institut Pasteur, 25-28 rue du Docteur Roux, 75724 Paris cedex 15, France. Smoot Michael M Cerami Ethan E Kuchinsky Allan A Landys Nerius N Workman Chris C Christmas Rowan R Avila-Campilo Iliana I Creech Michael M Gross Benjamin B Hanspers Kristina K Isserlin Ruth R Kelley Ryan R Killcoyne Sarah S Lotia Samad S Maere Steven S Morris John J Ono Keiichiro K Pavlovic Vuk V Pico Alexander R AR Vailaya Aditya A Wang Peng-Liang PL Adler Annette A Conklin Bruce R BR Hood Leroy L Kuiper Martin M Sander Chris C Schmulevich Ilya I Schwikowski Benno B Warner Guy J GJ Ideker Trey T Bader Gary D GD eng GM070743-01 GM NIGMS NIH HHS United States R01 GM070743 GM NIGMS NIH HHS United States R01 GM070743-01 GM NIGMS NIH HHS United States R01 GM070743-02 GM NIGMS NIH HHS United States R01 GM070743-03 GM NIGMS NIH HHS United States R01 GM070743-03S1 GM NIGMS NIH HHS United States R01 GM070743-04 GM NIGMS NIH HHS United States R01 GM070743-05 GM NIGMS NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

England Nat Protoc 101284307 1750-2799 0 RNA, Messenger IM Science. 2005 Feb 4;307(5710):724-7 15692050 Proc Natl Acad Sci U S A. 2005 Feb 8;102(6):1974-9 15687504 Bioinformatics. 2005 Feb 15;21(4):430-8 15608051 Genome Biol. 2005;6(4):R38 15833125 Nat Biotechnol. 2005 May;23(5):561-6 15877074 Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W352-7 15980487 Genome Biol. 2005;6(7):R62 15998451 Genome Biol. 2005;6(7):224 15998455 Appl Bioinformatics. 2005;4(1):71-4 16000016 Bioinformatics. 2005 Aug 15;21(16):3448-9 15972284 Nat Biotechnol. 2005 Aug;23(8):951-9 16082366 Nature. 2005 Aug 11;436(7052):861-5 16094371 Physiol Genomics. 2005 Sep 21;23(1):103-18 15942018 Bioinformatics. 2005 Sep 1;21 Suppl 2:ii220-1 16204107 Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 16199517 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D319-21 16381876 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D411-4 16381900 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6 16381921 Proc IEEE Comput Syst Bioinform Conf. 2004;:216-23 16448015 BMC Evol Biol. 2006;6:8 16441898 Cell. 2006 Mar 10;124(5):1069-81 16487579 Nature. 2006 Mar 30;440(7084):637-43 16554755 Bioinformatics. 2006 Apr 15;22(8):1015-7 16510498 Cell. 2006 Apr 7;125(1):173-86 16615898 Annu Rev Plant Biol. 2006;57:451-75 16669770 Nature. 2006 May 11;441(7090):173-8 16688168 Bioinformatics. 2006 Aug 15;22(16):2044-6 16777906 Bioinformatics. 2006 Sep 1;22(17):2178-9 16921162 Methods Enzymol. 2006;411:352-69 16939800 Mol Syst Biol. 2006;2:63 17102808 Bioinformatics. 2006 Dec 1;22(23):2968-70 17021160 BMC Bioinformatics. 2006;7:497 17101041 Brief Bioinform. 2006 Dec;7(4):331-8 17132622 Genome Biol. 2006;7(9):R83 16973001 Nucleic Acids Res. 2007 Jan;35(Database issue):D566-71 17130145 Nucleic Acids Res. 2007 Jan;35(Database issue):D747-50 17132828 Bioinformatics. 2007 Jan 15;23(2):207-14 17092991 Bioinformatics. 2007 Feb 1;23(3):394-6 17127678 Bioinformatics. 2007 Feb 1;23(3):392-3 17138585 Genome Biol. 2007;8(1):R7 17217541 Nat Protoc. 2006;1(2):662-71 17406294 Nat Protoc. 2007;2(3):685-94 17406631 Bioinformatics. 2007 Apr 1;23(7):910-2 17277332 Bioinformatics. 2007 Apr 15;23(8):1040-2 17309895 PLoS Comput Biol. 2007 Apr 20;3(4):e59 17447836 Comput Biol Chem. 2007 Jun;31(3):222-5 17500038 Science. 2007 Jun 8;316(5830):1497-502 17540862 Bioinformatics. 2007 Aug 15;23(16):2193-5 17553858 Nat Genet. 2000 May;25(1):25-9 10802651 Yeast. 2000 Apr;17(1):48-55 10928937 Science. 2001 May 4;292(5518):929-34 11340206 FEBS Lett. 2002 Feb 20;513(1):135-40 11911893 Nat Genet. 2002 May;31(1):19-20 11984561 Bioinformatics. 2002 Jul;18(7):996-1003 12117798 Bioinformatics. 2002;18 Suppl 1:S233-40 12169552 Genome Biol. 2003;4(1):R7 12540299 Nucleic Acids Res. 2003 Feb 15;31(4):e15 12582260 Genome Biol. 2003;4(3):R22 12620107 J Mol Biol. 2003 Apr 11;327(5):919-23 12662919 Genome Biol. 2003;4(4):R28 12702209 Trends Cell Biol. 2003 Jul;13(7):344-56 12837605 Nature. 2003 Oct 16;425(6959):737-41 14562106 BMC Bioinformatics. 2003 Jan 13;4:2 12525261 Genome Res. 2003 Nov;13(11):2498-504 14597658 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D452-5 14681455 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D497-501 14681466 Nat Biotechnol. 2004 Feb;22(2):177-83 14755292 Science. 2004 Feb 6;303(5659):808-13 14764870 Nat Biotechnol. 2004 Oct;22(10):1253-9 15470465 Nucleic Acids Res. 2005 Jan 1;33(Database issue):D428-32 15608231 Bioinformatics. 2005 Jan 15;21(2):272-4 15347570 Computational Biology methods Gene Expression Profiling methods Gene Regulatory Networks Genomics methods Proteomics methods RNA, Messenger metabolism Software NIHMS170904 PMC3685583 2007 10 20 9 0 2008 1 23 9 0 2007 10 20 9 0 ppublish nprot.2007.324 10.1038/nprot.2007.324 17947979 PMC3685583 NIHMS170904 17925023 2008 01 08 2008 01 18 2014 09 04

1741-7007 5 2007 BMC Biol. Broadening the horizon--level 2.5 of the HUPO-PSI format for molecular interactions. 44 Molecular interaction Information is a key resource in modern biomedical research. Publicly available data have previously been provided in a broad array of diverse formats, making access to this very difficult. The publication and wide implementation of the Human Proteome Organisation Proteomics Standards Initiative Molecular Interactions (HUPO PSI-MI) format in 2004 was a major step towards the establishment of a single, unified format by which molecular interactions should be presented, but focused purely on protein-protein interactions. The HUPO-PSI has further developed the PSI-MI XML schema to enable the description of interactions between a wider range of molecular types, for example nucleic acids, chemical entities, and molecular complexes. Extensive details about each supported molecular interaction can now be captured, including the biological role of each molecule within that interaction, detailed description of interacting domains, and the kinetic parameters of the interaction. The format is supported by data management and analysis tools and has been adopted by major interaction data providers. Additionally, a simpler, tab-delimited format MITAB2.5 has been developed for the benefit of users who require only minimal information in an easy to access configuration. The PSI-MI XML2.5 and MITAB2.5 formats have been jointly developed by interaction data producers and providers from both the academic and commercial sector, and are already widely implemented and well supported by an active development community. PSI-MI XML2.5 enables the description of highly detailed molecular interaction data and facilitates data exchange between databases and users without loss of information. MITAB2.5 is a simpler format appropriate for fast Perl parsing or loading into Microsoft Excel. Kerrien Samuel S European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. skerrien@ebi.ac.uk Orchard Sandra S Montecchi-Palazzi Luisa L Aranda Bruno B Quinn Antony F AF Vinod Nisha N Bader Gary D GD Xenarios Ioannis I Wojcik Jérôme J Sherman David D Tyers Mike M Salama John J JJ Moore Susan S Ceol Arnaud A Chatr-Aryamontri Andrew A Oesterheld Matthias M Stümpflen Volker V Salwinski Lukasz L Nerothin Jason J Cerami Ethan E Cusick Michael E ME Vidal Marc M Gilson Michael M Armstrong John J Woollard Peter P Hogue Christopher C Eisenberg David D Cesareni Gianni G Apweiler Rolf R Hermjakob Henning H eng 1 R01 GM071909 GM NIGMS NIH HHS United States GM070064 GM NIGMS NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. 2007 10 09

England BMC Biol 101190720 1741-7007 IM EMBO J. 2001 Mar 1;20(5):1153-63 11230138 Nat Biotechnol. 2007 Aug;25(8):894-8 17687370 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D449-51 14681454 Nat Biotechnol. 2004 Feb;22(2):177-83 14755292 Nucleic Acids Res. 2005 Jan 1;33(Database issue):D418-24 15608229 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D436-41 16381906 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D535-9 16381927 Proteomics. 2006 Jan;6(1):4-8 16400714 BMC Bioinformatics. 2006;7:97 16507094 Mol Cell Proteomics. 2006 May;5(5):787-8 16670253 OMICS. 2006 Summer;10(2):179-84 16901224 Nucleic Acids Res. 2007 Jan;35(Database issue):D572-4 17135203 Nucleic Acids Res. 2007 Jan;35(Database issue):D193-7 17142230 Nucleic Acids Res. 2007 Jan;35(Database issue):D198-201 17145705 Nucleic Acids Res. 2007 Jan;35(Database issue):D561-5 17145710 Nucleic Acids Res. 2007 Jan;35(Database issue):D5-12 17170002 Genome Res. 2003 Nov;13(11):2498-504 14597658 Computational Biology Computer Graphics Database Management Systems Databases, Protein standards Natural Language Processing Protein Interaction Mapping methods Proteomics methods standards User-Computer Interface PMC2189715 2007 2 19 2007 10 09 2007 10 09 2007 10 11 9 0 2008 1 19 9 0 2007 10 11 9 0 epublish 1741-7007-5-44 10.1186/1741-7007-5-44 17925023 PMC2189715 17687370 2007 08 09 2007 11 13 2007 11 15

1087-0156 25 8 2007 Aug Nat. Biotechnol. The minimum information required for reporting a molecular interaction experiment (MIMIx). 894-8 A wealth of molecular interaction data is available in the literature, ranging from large-scale datasets to a single interaction confirmed by several different techniques. These data are all too often reported either as free text or in tables of variable format, and are often missing key pieces of information essential for a full understanding of the experiment. Here we propose MIMIx, the minimum information required for reporting a molecular interaction experiment. Adherence to these reporting guidelines will result in publications of increased clarity and usefulness to the scientific community and will support the rapid, systematic capture of molecular interaction data in public databases, thereby improving access to valuable interaction data. Orchard Sandra S European Molecular Biology Laboratory (EMBL) - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. orchard@ebi.ac.uk Salwinski Lukasz L Kerrien Samuel S Montecchi-Palazzi Luisa L Oesterheld Matthias M Stümpflen Volker V Ceol Arnaud A Chatr-aryamontri Andrew A Armstrong John J Woollard Peter P Salama John J JJ Moore Susan S Wojcik Jérôme J Bader Gary D GD Vidal Marc M Cusick Michael E ME Gerstein Mark M Gavin Anne-Claude AC Superti-Furga Giulio G Greenblatt Jack J Bader Joel J Uetz Peter P Tyers Mike M Legrain Pierre P Fields Stan S Mulder Nicola N Gilson Michael M Niepmann Michael M Burgoon Lyle L De Las Rivas Javier J Prieto Carlos C Perreau Victoria M VM Hogue Chris C Mewes Hans-Werner HW Apweiler Rolf R Xenarios Ioannis I Eisenberg David D Cesareni Gianni G Hermjakob Henning H eng Journal Article Review

United States Nat Biotechnol 9604648 1087-0156 IM Databases, Protein standards Guidelines as Topic Humans Information Storage and Retrieval standards Internationality Protein Interaction Mapping standards Proteomics standards Research standards 21 2007 8 10 9 0 2007 11 14 9 0 2007 8 10 9 0 ppublish nbt1324 10.1038/nbt1324 17687370 17638019 2007 10 18 2008 04 02 2008 11 21

0340-6717 122 3-4 2007 Nov Hum. Genet. Germ-line DNA copy number variation frequencies in a large North American population. 345-53 Genomic copy number variation (CNV) is a recently identified form of global genetic variation in the human genome. The Affymetrix GeneChip 100 and 500 K SNP genotyping platforms were used to perform a large-scale population-based study of CNV frequency. We constructed a genomic map of 578 CNV regions, covering approximately 220 Mb (7.3%) of the human genome, identifying 183 previously unknown intervals. Copy number changes were observed to occur infrequently (<1%) in the majority (>93%) of these genomic regions, but encompass hundreds of genes and disease loci. This North American population-based map will be a useful resource for future genetic studies. Zogopoulos George G Sam Minuk Cancer Genetics and Biomarker Laboratories, Fred Litwin Centre for Cancer Genetics, Samuel Lunenfeld Research Institute, Toronto, Canada. Ha Kevin C H KC Naqib Faisal F Moore Sara S Kim Hyeja H Montpetit Alexandre A Robidoux Frederick F Laflamme Philippe P Cotterchio Michelle M Greenwood Celia C Scherer Stephen W SW Zanke Brent B Hudson Thomas J TJ Bader Gary D GD Gallinger Steven S eng CA-96-011 CA NCI NIH HHS United States Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2007 07 19

Germany Hum Genet 7613873 0340-6717 9007-49-2 DNA IM Aged Chromosome Mapping DNA genetics Female Gene Dosage Gene Frequency Genetic Variation Genetics, Population Genome, Human Germ Cells metabolism Humans Male Middle Aged Oligonucleotide Array Sequence Analysis Ontario Polymerase Chain Reaction Polymorphism, Single Nucleotide Registries 2007 5 25 2007 7 9 2007 7 19 2007 7 20 9 0 2008 4 3 9 0 2007 7 20 9 0 ppublish 10.1007/s00439-007-0404-5 17638019 17319736 2007 02 26 2007 03 19 2014 09 07

1553-7358 3 2 2007 Feb 23 PLoS Comput. Biol. From bytes to bedside: data integration and computational biology for translational cancer research. e12 Mathew Jomol P JP Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America. Jomol_Mathew@dfci.harvard.edu Taylor Barry S BS Bader Gary D GD Pyarajan Saiju S Antoniotti Marco M Chinnaiyan Arul M AM Sander Chris C Burakoff Steven J SJ Mishra Bud B eng Journal Article Review

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1367-4811 23 7 2007 Apr 1 Bioinformatics NetMatch: a Cytoscape plugin for searching biological networks. 910-2 NetMatch is a Cytoscape plugin which allows searching biological networks for subcomponents matching a given query. Queries may be approximate in the sense that certain parts of the subgraph-query may be left unspecified. To make the query creation process easy, a drawing tool is provided. Cytoscape is a bioinformatics software platform for the visualization and analysis of biological networks. The full package, a tutorial and associated examples are available at the following web sites: http://alpha.dmi.unict.it/~ctnyu/netmatch.html, http://baderlab.org/Software/NetMatch. Ferro A A Dipartimento di Matematica e Informatica, Università di Catania, Viale A. Doria 6, I-95125 Catania, Italy. ferro@dmi.unict.it Giugno R R Pigola G G Pulvirenti A A Skripin D D Bader G D GD Shasha D D eng Journal Article 2007 02 03

England Bioinformatics 9808944 1367-4803 IM Algorithms Computer Graphics Computer Simulation Database Management Systems Information Storage and Retrieval methods Models, Biological Signal Transduction physiology Software User-Computer Interface 2007 2 3 2007 2 6 9 0 2007 5 16 9 0 2007 2 6 9 0 ppublish btm032 10.1093/bioinformatics/btm032 17277332 17101041 2006 11 23 2007 01 03 2014 09 07

1471-2105 7 2006 BMC Bioinformatics cPath: open source software for collecting, storing, and querying biological pathways. 497 Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required. We have developed cPath, an open source database and web application for collecting, storing, and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists via a customizable web interface, and export pathway data via a web service to third-party software, such as Cytoscape, for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identifier mapping service for linking identical interactors and linking to external resources; built-in support for PSI-MI and BioPAX standard pathway exchange formats; a web service interface for searching and retrieving pathway data sets; and thorough documentation. The cPath software is freely available under the LGPL open source license for academic and commercial use. cPath is a robust, scalable, modular, professional-grade software platform for collecting, storing, and querying biological pathways. It can serve as the core data handling component in information systems for pathway visualization, analysis and modeling. Cerami Ethan G EG Computational Biology Center, Memorial Sloan-Kettering Cancer Center 1275 York Avenue, Box 460, New York, NY 10021, USA. cpath-bmc@cbio.mskcc.org Bader Gary D GD Gross Benjamin E BE Sander Chris C eng Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2006 11 13

England BMC Bioinformatics 100965194 1471-2105 IM Bioinformatics. 2006 Apr 15;22(8):1015-7 16510498 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D689-91 16381960 Bioinformatics. 2006 Jun 1;22(11):1383-90 16533819 Nucleic Acids Res. 2000 Jan 1;28(1):289-91 10592249 Cell. 2000 Jan 7;100(1):57-70 10647931 Science. 2000 Mar 17;287(5460):1977-8 10755952 Science. 2001 May 4;292(5518):929-34 11340206 Annu Rev Genomics Hum Genet. 2001;2:343-72 11701654 Bioinformatics. 2002 Feb;18(2):368-73 11847095 Science. 2002 Mar 1;295(5560):1662-4 11872829 FEBS Lett. 2002 Feb 20;513(1):135-40 11911893 Nature. 2002 May 9;417(6885):119-20 12000935 Nat Rev Cancer. 2002 May;2(5):331-41 12044009 Bioinformatics. 2003 Mar 1;19(4):524-31 12611808 Nat Rev Genet. 2003 May;4(5):337-45 12728276 Nature. 2003 Aug 21;424(6951):883 12931164 Genome Res. 2003 Oct;13(10):2363-71 14525934 Genome Res. 2003 Nov;13(11):2498-504 14597658 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D449-51 14681454 Nucleic Acids Res. 2004 Jan 1;32(Database issue):D452-5 14681455 Nat Biotechnol. 2004 Feb;22(2):177-83 14755292 Prog Biophys Mol Biol. 2004 Jun-Jul;85(2-3):433-50 15142756 Ann N Y Acad Sci. 2004 May;1020:77-91 15208185 Proteomics. 2004 Jul;4(7):1985-8 15221759 Nat Biotechnol. 2004 Sep;22(9):1071-3 15340462 Sci STKE. 2004 Aug 31;2004(248):pl11 15340175 Bioinformatics. 2004 Sep 22;20(14):2331-2 15059813 Nucleic Acids Res. 1996 Jan 1;24(1):32-9 8594595 Trends Biotechnol. 1996 Aug;14(8):273-9 8987457 Nucleic Acids Res. 1999 Jan 1;27(1):29-34 9847135 Nucleic Acids Res. 2005 Jan 1;33(Database issue):D428-32 15608231 FEBS Lett. 2005 Mar 21;579(8):1815-20 15763557 BMC Bioinformatics. 2005;6:34 15723693 Science. 2005 May 6;308(5723):821-4 15879210 Nat Biotechnol. 2005 Dec;23(12):1509-15 16333295 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D354-7 16381885 Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6 16381921 BMC Bioinformatics. 2006;7:70 16480510 Computational Biology methods Computer Graphics Databases, Factual Humans Information Storage and Retrieval Internet Programming Languages Signal Transduction Software Software Design Systems Integration User-Computer Interface PMC1660554 2006 6 16 2006 11 13 2006 11 13 2006 11 15 9 0 2007 1 4 9 0 2006 11 15 9 0 epublish 1471-2105-7-497 10.1186/1471-2105-7-497 17101041 PMC1660554 16381921 2005 12 29 2006 02 28 2014 09 09

1362-4962 34 Database issue 2006 Jan 1 Nucleic Acids Res. Pathguide: a pathway resource list. D504-6 Pathguide: the Pathway Resource List (http://pathguide.org) is a meta-database that provides an overview of more than 190 web-accessible biological pathway and network databases. These include databases on metabolic pathways, signaling pathways, transcription factor targets, gene regulatory networks, genetic interactions, protein-compound interactions, and protein-protein interactions. The listed databases are maintained by diverse groups in different locations and the information in them is derived either from the scientific literature or from systematic experiments. Pathguide is useful as a starting point for biological pathway analysis and for content aggregation in integrated biological information systems. Bader Gary D GD Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, New York, NY 10021, USA. Cary Michael P MP Sander Chris C eng Journal Article Research Support, Non-U.S. Gov't

England Nucleic Acids Res 0411011 0305-1048 0 Proteins 0 Transcription Factors IM Bioinformatics. 2003 Mar 1;19(4):524-31 12611808 Nat Biotechnol. 2004 Feb;22(2):177-83 14755292 Prog Biophys Mol Biol. 2004 Jun-Jul;85(2-3):433-50 15142756 Nucleic Acids Res. 2005 Jan 1;33(Database issue):D5-24 15608247 FEBS Lett. 2005 Mar 21;579(8):1815-20 15763557 Databases, Genetic standards Gene Expression Regulation Internet Metabolism Proteins chemistry metabolism Signal Transduction Systems Integration Transcription Factors metabolism User-Computer Interface PMC1347488 2005 12 31 9 0 2006 3 1 9 0 2005 12 31 9 0 ppublish 34/suppl_1/D504 10.1093/nar/gkj126 16381921 PMC1347488 15763557 2005 03 14 2005 04 05 2005 11 16

0014-5793 579 8 2005 Mar 21 FEBS Lett. Pathway information for systems biology. 1815-20 Pathway information is vital for successful quantitative modeling of biological systems. The almost 170 online pathway databases vary widely in coverage and representation of biological processes, making their use extremely difficult. Future pathway information systems for querying, visualization and analysis must support standard exchange formats to successfully integrate data on a large scale. Such integrated systems will greatly facilitate the constructive cycle of computational model building and experimental verification that lies at the heart of systems biology. Cary Michael P MP Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, New York, NY 10021, USA. Bader Gary D GD Sander Chris C eng Journal Article Review

Netherlands FEBS Lett 0155157 0014-5793 IM Animals Computational Biology Databases, Factual Humans Models, Biological Protein Binding Signal Transduction 51 2005 1 31 2005 2 1 2005 2 1 2005 3 15 9 0 2005 4 6 9 0 2005 3 15 9 0 ppublish S0014-5793(05)00170-5 10.1016/j.febslet.2005.02.005 15763557 14764870 2004 02 06 2004 03 04 2007 11 14

1095-9203 303 5659 2004 Feb 6 Science Global mapping of the yeast genetic interaction network. 808-13 A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms. Tong Amy Hin Yan AH Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada M5G 1L6. Lesage Guillaume G Bader Gary D GD Ding Huiming H Xu Hong H Xin Xiaofeng X Young James J Berriz Gabriel F GF Brost Renee L RL Chang Michael M Chen YiQun Y Cheng Xin X Chua Gordon G Friesen Helena H Goldberg Debra S DS Haynes Jennifer J Humphries Christine C He Grace G Hussein Shamiza S Ke Lizhu L Krogan Nevan N Li Zhijian Z Levinson Joshua N JN Lu Hong H Ménard Patrice P Munyana Christella C Parsons Ainslie B AB Ryan Owen O Tonikian Raffi R Roberts Tania T Sdicu Anne-Marie AM Shapiro Jesse J Sheikh Bilal B Suter Bernhard B Wong Sharyl L SL Zhang Lan V LV Zhu Hongwei H Burd Christopher G CG Munro Sean S Sander Chris C Rine Jasper J Greenblatt Jack J Peter Matthias M Bretscher Anthony A Bell Graham G Roth Frederick P FP Brown Grant W GW Andrews Brenda B Bussey Howard H Boone Charles C eng GM39066 GM NIGMS NIH HHS United States GM61221 GM NIGMS NIH HHS United States Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.

United States Science 0404511 0036-8075 0 Saccharomyces cerevisiae Proteins IM Science. 2004 Feb 6;303(5659):774-5 14764857 Amino Acid Sequence Computational Biology Cystic Fibrosis genetics Gene Deletion Genes, Essential Genes, Fungal Genetic Diseases, Inborn genetics Genotype Humans Molecular Sequence Data Multifactorial Inheritance Mutation Phenotype Polymorphism, Genetic Retinitis Pigmentosa genetics Saccharomyces cerevisiae genetics metabolism Saccharomyces cerevisiae Proteins chemistry genetics metabolism 2004 2 7 5 0 2004 3 5 5 0 2004 2 7 5 0 ppublish 14764870 10.1126/science.1091317 303/5659/808 14595360 2003 11 03 2004 06 24

1087-0156 21 11 2003 Nov Nat. Biotechnol. Playing tag with the yeast proteome. 1297-9 Andrews Brenda J BJ Bader Gary D GD Boone Charles C eng News

United States Nat Biotechnol 9604648 1087-0156 0 Epitopes 0 Proteome 0 RNA, Fungal 0 Recombinant Fusion Proteins 0 Saccharomyces cerevisiae Proteins IM Epitopes analysis genetics Expressed Sequence Tags Gene Expression Profiling methods Genome, Fungal Open Reading Frames genetics Proteome genetics metabolism Proteomics methods RNA, Fungal genetics metabolism Recombinant Fusion Proteins chemistry genetics metabolism Saccharomyces cerevisiae genetics growth & development metabolism Saccharomyces cerevisiae Proteins genetics metabolism 2003 11 5 5 0 2004 6 25 5 0 2003 11 5 5 0 ppublish 14595360 10.1038/nbt1103-1297 nbt1103-1297 12837605 2003 07 02 2003 09 05 2004 11 17

0962-8924 13 7 2003 Jul Trends Cell Biol. Functional genomics and proteomics: charting a multidimensional map of the yeast cell. 344-56 The challenge of large-scale functional genomics projects is to build a comprehensive map of the cell including genome sequence and gene expression data, information on protein localization, structure, function and expression, post-translational modifications, molecular and genetic interactions and phenotypic descriptions. Some of this broad set of functional genomics data has been already assembled for the budding yeast. Even though molecular cartography of the yeast cell is still far from comprehensive, functional genomics has begun to forge connections between disparate cellular events and to foster numerous hypotheses. Here we review several different genomics and proteomics technologies and describe bioinformatics methods for exploring these data to make new discoveries. Bader Gary D GD Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, 10021, New York, NY, USA. Heilbut Adrian A Andrews Brenda B Tyers Mike M Hughes Timothy T Boone Charles C eng Journal Article Review

England Trends Cell Biol 9200566 0962-8924 0 Nuclear Proteins 0 Saccharomyces cerevisiae Proteins IM Chromosome Mapping Computational Biology Gene Expression Regulation, Fungal genetics Genomics Nuclear Proteins genetics metabolism Proteomics Saccharomyces cerevisiae genetics metabolism ultrastructure Saccharomyces cerevisiae Proteins genetics metabolism Signal Transduction genetics 138 2003 7 3 5 0 2003 9 6 5 0 2003 7 3 5 0 ppublish 12837605 S0962892403001272 12689350 2003 10 29 2003 12 04 2014 06 11

1471-2105 4 2003 Mar 27 BMC Bioinformatics PreBIND and Textomy--mining the biomedical literature for protein-protein interactions using a support vector machine. 11 The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND. Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days. Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information. Donaldson Ian I Samuel Lunenfeld Research Institute, Toronto, M5G 1X5, Canada. ian.donaldson@utoronto.ca Martin Joel J de Bruijn Berry B Wolting Cheryl C Lay Vicki V Tuekam Brigitte B Zhang Shudong S Baskin Berivan B Bader Gary D GD Michalickova Katerina K Pawson Tony T Hogue Christopher W V CW eng Comparative Study Evaluation Studies Journal Article Validation Studies 2003 03 27

England BMC Bioinformatics 100965194 1471-2105 0 Saccharomyces cerevisiae Proteins IM Bioinformatics. 2000 May;16(5):465-77 10871269 Nat Biotechnol. 2002 Oct;20(10):991-7 12355115 Pac Symp Biocomput. 2000;:517-28 10902199 Pac Symp Biocomput. 2000;:529-40 10902200 Pac Symp Biocomput. 2000;:541-52 10902201 Proc Int Conf Intell Syst Mol Biol. 2000;8:279-85 10977089 Nucleic Acids Res. 2001 Jan 1;29(1):137-40 11125071 Nucleic Acids Res. 2003 Jan 1;31(1):365-70 12520024 BMC Bioinformatics. 2002 Oct 25;3:32 12401134 Pac Symp Biocomput. 1998;:707-18 9697224 Nucleic Acids Res. 2001 Jan 1;29(1):242-5 11125103 Bioinformatics. 2001 Feb;17(2):155-61 11238071 Pac Symp Biocomput. 2001;:520-31 11262970 Bioinformatics. 2001 Apr;17(4):359-63 11301305 Nat Genet. 2001 May;28(1):21-8 11326270 Methods Biochem Anal. 2001;43:19-43 11449725 Bioinformatics. 2001;17 Suppl 1:S74-82 11472995 Nucleic Acids Res. 2002 Jan 1;30(1):13-6 11752242 Nucleic Acids Res. 2002 Jan 1;30(1):31-4 11752246 Nucleic Acids Res. 2002 Jan 1;30(1):69-72 11752257 Genome Inform. 2001;12:123-34 11791231 Nature. 2002 Jan 10;415(6868):180-3 11805837 Pac Symp Biocomput. 2000;:505-16 10902198 Algorithms Artificial Intelligence Computational Biology methods statistics & numerical data Databases, Factual trends Databases, Protein trends Genome, Fungal Information Storage and Retrieval trends Protein Interaction Mapping classification methods statistics & numerical data PubMed classification Saccharomyces cerevisiae genetics Saccharomyces cerevisiae Proteins chemistry PMC153503 2003 4 12 5 0 2003 12 5 5 0 2002 Dec 28 2003 Mar 27 2003 Mar 27 2003 4 12 5 0 ppublish 12689350 PMC153503 12547433 2003 01 27 2003 11 06 2009 08 25

1367-5931 7 1 2003 Feb Curr Opin Chem Biol Functional genomics of intracellular peptide recognition domains with combinatorial biology methods. 97-102 Phage-displayed peptide libraries have been used to identify specific ligands for peptide-binding domains that mediate intracellular protein-protein interactions. These studies have provided significant insights into the specificities of particular domains. For PDZ domains that recognize C-terminal sequences, the information has proven useful in identifying natural binding partners from genomic databases. For SH3 domains that recognize internal proline-rich motifs, the results of database searches with phage-derived ligands have been compared with the results of yeast-two-hybrid experiments to produce overlap networks that reliably predict natural protein-protein interactions. In addition, libraries of phage-displayed PDZ and SH3 domains have been used to identify the residues responsible for ligand recognition, and also to engineer domains with altered specificities. Sidhu Sachdev S SS Department of Protein Engineering, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA. sidhu@gene.com Bader Gary D GD Boone Charles C eng Journal Article Review

England Curr Opin Chem Biol 9811312 1367-5931 0 Peptide Library 0 Peptides IM Databases, Protein Genomics Peptide Library Peptides chemistry metabolism Protein Binding Protein Structure, Tertiary Structural Homology, Protein Two-Hybrid System Techniques 38 2003 1 28 4 0 2003 11 7 5 0 2003 1 28 4 0 ppublish 12547433 S136759310200011X 12525261 2003 10 29 2003 12 04 2014 06 11

1471-2105 4 2003 Jan 13 BMC Bioinformatics An automated method for finding molecular complexes in large protein interaction networks. 2 Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE. Bader Gary D GD Samuel Lunenfeld Research Institute, Mt, Sinai Hospital, Toronto ON Canada M5G 1X5, Dept, of Biochemistry, University of Toronto, Toronto ON Canada M5S 1A8. gary.bader@utoronto.ca Hogue Christopher W V CW eng Evaluation Studies Journal Article Research Support, Non-U.S. Gov't 2003 01 13

England BMC Bioinformatics 100965194 1471-2105 0 Macromolecular Substances 0 Saccharomyces cerevisiae Proteins IM Bioinformatics. 2000 May;16(5):412-24 10871264 Nat Genet. 2000 May;25(1):25-9 10802651 Nat Struct Biol. 2000 Oct;7(10):903-9 11017201 Nature. 2000 Oct 5;407(6804):651-4 11034217 Nat Biotechnol. 2000 Nov;18(11):1121-2 11062388 Nucleic Acids Res. 2001 Jan 1;29(1):75-9 11125054 Science. 2001 Feb 16;291(5507):1221-4 11233445 Proc Natl Acad Sci U S A. 2001 Apr 10;98(8):4569-74 11283351 Nature. 2001 May 3;411(6833):41-2 11333967 J Cell Biol. 2001 Aug 6;154(3):549-71 11489916 Proc Biol Sci. 2001 Sep 7;268(1478):1803-10 11522199 Science. 2001 Sep 14;293(5537):2087-92 11557892 Science. 2001 Nov 23;294(5547):1679-84 11721045 Nucleic Acids Res. 2002 Jan 1;30(1):303-5 11752321 Science. 2002 Jan 11;295(5553):321-4 11743162 Nature. 2002 Jan 10;415(6868):141-7 11805826 Nature. 2002 Jan 10;415(6868):180-3 11805837 Science. 2002 Apr 19;296(5567):548-50 11964484 Science. 2002 May 3;296(5569):910-3 11988575 Nature. 2002 May 23;417(6887):399-403 12000970 Nat Biotechnol. 2002 Oct;20(10):991-7 12355115 Nature. 1998 Jun 4;393(6684):440-2 9623998 Nucleic Acids Res. 1999 Jan 1;27(1):74-8 9847146 J Comput Biol. 1998 Winter;5(4):747-54 10072089 Annu Rev Biophys Biomol Struct. 1999;28:295-317 10410804 EMBO J. 1999 Aug 2;18(15):4321-31 10428970 Science. 1999 Aug 6;285(5429):901-6 10436161 Nucleic Acids Res. 2001 Jan 1;29(1):137-40 11125071 Nucleic Acids Res. 2001 Jan 1;29(1):242-5 11125103 Nucleic Acids Res. 2000 Jan 1;28(1):37-40 10592176 Nucleic Acids Res. 2000 Jan 1;28(1):56-9 10592180 Nucleic Acids Res. 2000 Jan 1;28(1):123-5 10592199 Nucleic Acids Res. 2000 Jan 1;28(1):316-9 10592259 Nature. 2000 Feb 10;403(6770):623-7 10688190 Yeast. 2000 Jun 30;17(2):95-110 10900456 Algorithms Cluster Analysis Computational Biology methods Computer Graphics Macromolecular Substances Predictive Value of Tests Protein Interaction Mapping methods Proteomics methods Saccharomyces cerevisiae Proteins chemistry metabolism Software Validation PMC149346 2003 1 15 4 0 2003 12 5 5 0 2002 Sep 4 2003 Jan 13 2003 Jan 13 2003 1 15 4 0 ppublish 12525261 PMC149346 12519993 2003 01 09 2003 03 14 2014 06 11

1362-4962 31 1 2003 Jan 1 Nucleic Acids Res. BIND: the Biomolecular Interaction Network Database. 248-50 The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD. Bader Gary D GD Department of Biochemistry, Samuel Lunenfeld Research Institute, University of Toronto, Toronto M5G 1X5, Canada. Betel Doron D Hogue Christopher W V CW eng Journal Article Research Support, Non-U.S. Gov't

England Nucleic Acids Res 0411011 0305-1048 0 Macromolecular Substances 0 Proteins IM Science. 2002 Jan 11;295(5553):321-4 11743162 Nucleic Acids Res. 2002 Jan 1;30(1):281-3 11752315 Nature. 2002 Jan 10;415(6868):180-3 11805837 FEBS Lett. 2002 Feb 20;513(1):135-40 11911893 Biopolymers. 2001-2002;61(2):111-20 11987160 Genome Res. 2002 Oct;12(10):1619-23 12368255 BMC Bioinformatics. 2002 Oct 25;3:32 12401134 Nature. 1995 Feb 16;373(6515):573-80 7531822 Methods Enzymol. 1996;266:141-62 8743683 Nucleic Acids Res. 2002 Jan 1;30(1):303-5 11752321 Bioinformatics. 2000 May;16(5):465-77 10871269 Nucleic Acids Res. 2001 Jan 1;29(1):242-5 11125103 Science. 2001 Feb 16;291(5507):1221-4 11233445 Science. 2001 Dec 14;294(5550):2364-8 11743205 Nucleic Acids Res. 2002 Jan 1;30(1):245-8 11752306 Nucleic Acids Res. 2002 Jan 1;30(1):249-52 11752307 Nature. 2002 Jan 10;415(6868):141-7 11805826 Amino Acid Sequence Animals Computer Graphics Databases, Protein Macromolecular Substances Protein Interaction Mapping Protein Structure, Tertiary Proteins chemistry metabolism physiology Sequence Alignment methods PMC165503 2003 1 10 4 0 2003 3 15 4 0 2003 1 10 4 0 ppublish 12519993 PMC165503 12401134 2003 10 29 2003 11 18 2014 06 11

1471-2105 3 2002 Oct 25 BMC Bioinformatics SeqHound: biological sequence and structure database as a platform for bioinformatics research. 32 SeqHound has been developed as an integrated biological sequence, taxonomy, annotation and 3-D structure database system. It provides a high-performance server platform for bioinformatics research in a locally-hosted environment. SeqHound is based on the National Center for Biotechnology Information data model and programming tools. It offers daily updated contents of all Entrez sequence databases in addition to 3-D structural data and information about sequence redundancies, sequence neighbours, taxonomy, complete genomes, functional annotation including Gene Ontology terms and literature links to PubMed. SeqHound is accessible via a web server through a Perl, C or C++ remote API or an optimized local API. It provides functionality necessary to retrieve specialized subsets of sequences, structures and structural domains. Sequences may be retrieved in FASTA, GenBank, ASN.1 and XML formats. Structures are available in ASN.1, XML and PDB formats. Emphasis has been placed on complete genomes, taxonomy, domain and functional annotation as well as 3-D structural functionality in the API, while fielded text indexing functionality remains under development. SeqHound also offers a streamlined WWW interface for simple web-user queries. The system has proven useful in several published bioinformatics projects such as the BIND database and offers a cost-effective infrastructure for research. SeqHound will continue to develop and be provided as a service of the Blueprint Initiative at the Samuel Lunenfeld Research Institute. The source code and examples are available under the terms of the GNU public license at the Sourceforge site http://sourceforge.net/projects/slritools/ in the SLRI Toolkit. Michalickova Katerina K Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada M5S 1A8. katerina@mshri.on.ca Bader Gary D GD Dumontier Michel M Lieu Hao H Betel Doron D Isserlin Ruth R Hogue Christopher W V CW eng Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. 2002 10 25

England BMC Bioinformatics 100965194 1471-2105 IM Nucleic Acids Res. 2000 Jan 1;28(1):45-8 10592178 Trends Biotechnol. 1999 Sep;17(9):351-5 10461180 Bioinformatics. 2000 May;16(5):465-77 10871269 Nucleic Acids Res. 2001 Jan 1;29(1):137-40 11125071 Nucleic Acids Res. 2001 Jan 1;29(1):242-5 11125103 Genome Res. 2001 Aug;11(8):1425-33 11483584 Nucleic Acids Res. 2002 Jan 1;30(1):17-20 11752243 Nucleic Acids Res. 2002 Jan 1;30(1):21-6 11752244 Nucleic Acids Res. 2002 Jan 1;30(1):35-7 11752247 Nucleic Acids Res. 2002 Jan 1;30(1):242-4 11752305 Nucleic Acids Res. 2002 Jan 1;30(1):249-52 11752307 Nucleic Acids Res. 2002 Jan 1;30(1):276-80 11752314 Nucleic Acids Res. 2002 Jan 1;30(1):281-3 11752315 BMC Bioinformatics. 2002 May 8;3:13 12019022 BMC Bioinformatics. 2002 Jul 31;3:20 12150718 Gene. 1988 Dec 15;73(1):237-44 3243435 J Mol Biol. 1990 Oct 5;215(3):403-10 2231712 Nat Genet. 1993 Aug;4(4):332-3 8401577 Nucleic Acids Res. 1994 Nov 11;22(22):4673-80 7984417 Methods Enzymol. 1996;266:141-62 8743683 Methods Biochem Anal. 1998;39:121-44 9707929 Nucleic Acids Res. 2000 Jan 1;28(1):235-42 10592235 Amino Acid Sequence Base Sequence Computational Biology methods Databases, Genetic classification Information Storage and Retrieval methods Internet Models, Genetic Models, Molecular Molecular Sequence Data Software Structure-Activity Relationship PMC138791 2002 10 29 4 0 2003 12 3 5 0 2002 Aug 1 2002 Oct 25 2002 Oct 25 2002 10 29 4 0 ppublish 12401134 PMC138791 12355115 2002 09 30 2003 03 12 2006 11 15

1087-0156 20 10 2002 Oct Nat. Biotechnol. Analyzing yeast protein-protein interaction data obtained from different sources. 991-7 High-throughput methods for detecting protein interactions, such as mass spectrometry and yeast two-hybrid assays, continue to produce vast amounts of data that may be exploited to infer protein function and regulation. As this article went to press, the pool of all published interaction information on Saccharomyces cerevisiae was 15,143 interactions among 4,825 proteins, and power-law scaling supports an estimate of 20,000 specific protein interactions. To investigate the biases, overlaps, and complementarities among these data, we have carried out an analysis of two high-throughput mass spectrometry (HMS)-based protein interaction data sets from budding yeast, comparing them to each other and to other interaction data sets. Our analysis reveals 198 interactions among 222 proteins common to both data sets, many of which reflect large multiprotein complexes. It also indicates that a "spoke" model that directly pairs bait proteins with associated proteins is roughly threefold more accurate than a "matrix" model that connects all proteins. In addition, we identify a large, previously unsuspected nucleolar complex of 148 proteins, including 39 proteins of unknown function. Our results indicate that existing large-scale protein interaction data sets are nonsaturating and that integrating many different experimental data sets yields a clearer biological view than any single method alone. Bader Gary D GD Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON, Canada M5G 1X5. Hogue Christopher W V CW eng Comparative Study Journal Article Research Support, Non-U.S. Gov't

United States Nat Biotechnol 9604648 1087-0156 0 Macromolecular Substances 0 Multiprotein Complexes 0 Proteome 0 Saccharomyces cerevisiae Proteins IM Chromatography, Liquid methods Database Management Systems Databases, Protein Genome, Fungal Macromolecular Substances Mass Spectrometry methods Multiprotein Complexes Protein Interaction Mapping methods Proteome Reproducibility of Results Saccharomyces cerevisiae metabolism Saccharomyces cerevisiae Proteins chemistry metabolism Sensitivity and Specificity Sequence Alignment methods Sequence Analysis, Protein Species Specificity 2002 10 2 4 0 2003 3 13 4 0 2002 Feb 20 2002 Aug 18 2002 10 2 4 0 ppublish 12355115 10.1038/nbt1002-991 nbt1002-991 11805837 2002 01 23 2002 02 14 2009 11 19

0028-0836 415 6868 2002 Jan 10 Nature Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. 180-3 The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks. Ho Yuen Y MDS Proteomics, 251 Attwell Drive, Toronto, Canada M9W 7H4, and Staermosegaardsvej 6, DK-5230 Odense M, Denmark. Gruhler Albrecht A Heilbut Adrian A Bader Gary D GD Moore Lynda L Adams Sally-Lin SL Millar Anna A Taylor Paul P Bennett Keiryn K Boutilier Kelly K Yang Lingyun L Wolting Cheryl C Donaldson Ian I Schandorff Søren S Shewnarane Juanita J Vo Mai M Taggart Joanne J Goudreault Marilyn M Muskat Brenda B Alfarano Cris C Dewar Danielle D Lin Zhen Z Michalickova Katerina K Willems Andrew R AR Sassi Holly H Nielsen Peter A PA Rasmussen Karina J KJ Andersen Jens R JR Johansen Lene E LE Hansen Lykke H LH Jespersen Hans H Podtelejnikov Alexandre A Nielsen Eva E Crawford Janne J Poulsen Vibeke V Sørensen Birgitte D BD Matthiesen Jesper J Hendrickson Ronald C RC Gleeson Frank F Pawson Tony T Moran Michael F MF Durocher Daniel D Mann Matthias M Hogue Christopher W V CW Figeys Daniel D Tyers Mike M eng Journal Article Research Support, Non-U.S. Gov't

England Nature 0410462 0028-0836 0 Cell Cycle Proteins 0 DNA, Fungal 0 Macromolecular Substances 0 Proteome 0 Saccharomyces cerevisiae Proteins EC 2.7.- Protein Kinases EC 2.7.1.- DUN1 protein, S cerevisiae EC 2.7.11.1 Protein-Serine-Threonine Kinases EC 3.1.3.- Phosphoric Monoester Hydrolases IM Nature. 2002 Jan 10;415(6868):123-4 11805813 Amino Acid Sequence Cell Cycle Proteins Cloning, Molecular DNA Damage DNA Repair DNA, Fungal Humans Macromolecular Substances Mass Spectrometry Molecular Sequence Data Phosphoric Monoester Hydrolases metabolism Protein Binding Protein Kinases chemistry metabolism Protein-Serine-Threonine Kinases Proteome Saccharomyces cerevisiae chemistry Saccharomyces cerevisiae Proteins chemistry isolation & purification Sequence Alignment Signal Transduction 2002 1 24 10 0 2002 2 15 10 1 2002 1 24 10 0 ppublish 11805837 10.1038/415180a 415180a 11743205 2001 12 14 2002 01 14 2013 11 21

0036-8075 294 5550 2001 Dec 14 Science Systematic genetic analysis with ordered arrays of yeast deletion mutants. 2364-8 In Saccharomyces cerevisiae, more than 80% of the approximately 6200 predicted genes are nonessential, implying that the genome is buffered from the phenotypic consequences of genetic perturbation. To evaluate function, we developed a method for systematic construction of double mutants, termed synthetic genetic array (SGA) analysis, in which a query mutation is crossed to an array of approximately 4700 deletion mutants. Inviable double-mutant meiotic progeny identify functional relationships between genes. SGA analysis of genes with roles in cytoskeletal organization (BNI1, ARP2, ARC40, BIM1), DNA synthesis and repair (SGS1, RAD27), or uncharacterized functions (BBC1, NBP2) generated a network of 291 interactions among 204 genes. Systematic application of this approach should produce a global map of gene function. Tong A H AH Banting and Best Department of Medical Research, University of Toronto, Toronto ON, Canada M5G 1L6. Evangelista M M Parsons A B AB Xu H H Bader G D GD Pagé N N Robinson M M Raghibizadeh S S Hogue C W CW Bussey H H Andrews B B Tyers M M Boone C C eng Journal Article Research Support, Non-U.S. Gov't

United States Science 0404511 0036-8075 0 BIM1 protein, S cerevisiae 0 BNR1 protein, S cerevisiae 0 Bni1 protein, S cerevisiae 0 Carrier Proteins 0 Cell Cycle Proteins 0 Cytoskeletal Proteins 0 DNA, Fungal 0 Fungal Proteins 0 Genetic Markers 0 Microfilament Proteins 0 Microtubule Proteins 0 Saccharomyces cerevisiae Proteins EC 3.1.- Endodeoxyribonucleases EC 3.1.- Flap Endonucleases EC 3.1.11.5 RAD27 protein, S cerevisiae EC 3.6.1.- SGS1 protein, S cerevisiae EC 3.6.4.- DNA Helicases EC 3.6.4.12 RecQ Helicases IM Carrier Proteins genetics physiology Cell Cycle Proteins genetics physiology Cell Polarity Computational Biology Crosses, Genetic Cytoskeletal Proteins Cytoskeleton physiology DNA Helicases genetics physiology DNA Repair DNA, Fungal biosynthesis Databases, Genetic Endodeoxyribonucleases genetics physiology Flap Endonucleases Fungal Proteins genetics physiology Gene Deletion Genes, Essential Genes, Fungal physiology Genetic Markers Genetic Techniques Genome, Fungal Microfilament Proteins Microtubule Proteins genetics physiology Mitosis RecQ Helicases Recombination, Genetic Robotics Saccharomyces cerevisiae genetics growth & development physiology Saccharomyces cerevisiae Proteins genetics physiology 2001 12 18 10 0 2002 1 15 10 1 2001 12 18 10 0 ppublish 11743205 10.1126/science.1065810 294/5550/2364 11743162 2002 01 11 2002 02 04 2007 11 14

1095-9203 295 5553 2002 Jan 11 Science A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules. 321-4 Peptide recognition modules mediate many protein-protein interactions critical for the assembly of macromolecular complexes. Complete genome sequences have revealed thousands of these domains, requiring improved methods for identifying their physiologically relevant binding partners. We have developed a strategy combining computational prediction of interactions from phage-display ligand consensus sequences with large-scale two-hybrid physical interaction tests. Application to yeast SH3 domains generated a phage-display network containing 394 interactions among 206 proteins and a two-hybrid network containing 233 interactions among 145 proteins. Graph theoretic analysis identified 59 highly likely interactions common to both networks. Las17 (Bee1), a member of the Wiskott-Aldrich Syndrome protein (WASP) family of actin-assembly proteins, showed multiple SH3 interactions, many of which were confirmed in vivo by coimmunoprecipitation. Tong Amy Hin Yan AH Banting and Best Department of Medical Research and Department of Molecular and Medical Genetics, University of Toronto, Toronto, Ontario, Canada M5G 1L6. Drees Becky B Nardelli Giuliano G Bader Gary D GD Brannetti Barbara B Castagnoli Luisa L Evangelista Marie M Ferracuti Silvia S Nelson Bryce B Paoluzi Serena S Quondam Michele M Zucconi Adriana A Hogue Christopher W V CW Fields Stanley S Boone Charles C Cesareni Gianni G eng P41 RR11823 RR NCRR NIH HHS United States Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. 2001 12 13

United States Science 0404511 0036-8075 0 Cytoskeletal Proteins 0 Fungal Proteins 0 LAS17 protein, S cerevisiae 0 Ligands 0 Peptide Library 0 Peptides 0 Proteins 0 Proteome 0 Saccharomyces cerevisiae Proteins 0 Wiskott-Aldrich Syndrome Protein IM Science. 2002 Jan 11;295(5553):284-7 11786630 Algorithms Amino Acid Motifs Amino Acid Sequence Binding Sites Computational Biology Consensus Sequence Cytoskeletal Proteins Databases, Genetic Databases, Protein Fungal Proteins chemistry metabolism Ligands Molecular Sequence Data Peptide Library Peptides chemistry metabolism Protein Binding Protein Structure, Tertiary Proteins chemistry metabolism Proteome Saccharomyces cerevisiae chemistry genetics Saccharomyces cerevisiae Proteins chemistry genetics metabolism Software Two-Hybrid System Techniques Wiskott-Aldrich Syndrome Protein src Homology Domains 2001 12 18 10 0 2002 2 5 10 1 2001 Dec 13 2001 12 18 10 0 ppublish 11743162 10.1126/science.1064987 1064987 10871269 2000 09 29 2000 09 29 2004 11 17

1367-4803 16 5 2000 May Bioinformatics BIND--a data specification for storing and describing biomolecular interactions, molecular complexes and pathways. 465-77 Proteomics is gearing up towards high-throughput methods for identifying and characterizing all of the proteins, protein domains and protein interactions in a cell and will eventually create more recorded biological information than the Human Genome Project. Each protein expressed in a cell can interact with various other proteins and molecules in the course of its function. A standard data specification is required that can describe and store this information in all its detail and allow efficient cross-platform transfer of data. A complete specification must be the basis for any database or tool for managing and analysing this information. We have defined a complete data specification in ASN.1 that can describe information about biomolecular interactions, complexes and pathways. Our group is using this data specification in our database, the Biomolecular Interaction Network Database (BIND). An interaction record is based on the interaction between two objects. An object can be a protein, DNA, RNA, ligand, molecular complex or an interaction. Interaction description encompasses cellular location, experimental conditions used to observe the interaction, conserved sequence, molecular location, chemical action, kinetics, thermodynamics, and chemical state. Molecular complexes are defined as collections of more than two interactions that form a complex, with extra descriptive information such as complex topology. Pathways are defined as collections of more than two interactions that form a pathway, with additional descriptive information such as cell cycle stage. A request for proposal of a human readable flat-file format that mirrors the BIND data specification is also tendered for interested parties. The ASN.1 data specification for biomolecular interaction, molecular complex and pathway data is available at ftp://bioinfo.mshri.on.ca/pub/BIND/Spec/bind.asn. An interactive browser for this document is available through our homepage at http://bioinfo.mshri.on.ca/BIND/asn-browser/. Bader G D GD Department of Biochemistry, University of Toronto/Samuel Lunenfeld Research Institute, Toronto, M5G 1X5, Canada Samuel Lunenfeld Research Institute, Toronto, M5G 1X5, Canada. Hogue C W CW eng Journal Article

ENGLAND Bioinformatics 9808944 1367-4803 0 Macromolecular Substances 0 Proteins 63231-63-0 RNA 9007-49-2 DNA IM DNA genetics metabolism Databases, Factual Humans Macromolecular Substances Models, Statistical Molecular Biology Proteins genetics metabolism RNA genetics metabolism 2000 6 28 11 0 2000 10 7 11 1 2000 6 28 11 0 ppublish 10871269 11125103 2001 01 04 2001 02 08 2014 06 15

1362-4962 29 1 2001 Jan 1 Nucleic Acids Res. BIND--The Biomolecular Interaction Network Database. 242-5 The Biomolecular Interaction Network Database (BIND; http://binddb. org) is a database designed to store full descriptions of interactions, molecular complexes and pathways. Development of the BIND 2.0 data model has led to the incorporation of virtually all components of molecular mechanisms including interactions between any two molecules composed of proteins, nucleic acids and small molecules. Chemical reactions, photochemical activation and conformational changes can also be described. Everything from small molecule biochemistry to signal transduction is abstracted in such a way that graph theory methods may be applied for data mining. The database can be used to study networks of interactions, to map pathways across taxonomic branches and to generate information for kinetic simulations. BIND anticipates the coming large influx of interaction information from high-throughput proteomics efforts including detailed information about post-translational modifications from mass spectrometry. Version 2.0 of the BIND data model is discussed as well as implementation, content and the open nature of the BIND project. The BIND data specification is available as ASN.1 and XML DTD. Bader G D GD Department of Biochemistry, University of Toronto, Canada, Samuel Lunenfeld Research Institute, 600 University Avenue, Toronto M5G 1X5, Canada. Donaldson I I Wolting C C Ouellette B F BF Pawson T T Hogue C W CW eng Journal Article Research Support, Non-U.S. Gov't

ENGLAND Nucleic Acids Res 0411011 0305-1048 0 Proteins 9007-49-2 DNA IM Proc Natl Acad Sci U S A. 2000 Feb 1;97(3):1143-7 10655498 Nucleic Acids Res. 2000 Jan 1;28(1):289-91 10592249 Nature. 2000 Feb 10;403(6770):623-7 10688190 Nat Genet. 2000 May;25(1):25-9 10802651 Bioinformatics. 2000 Mar;16(3):269-85 10869020 Bioinformatics. 2000 May;16(5):465-77 10871269 Oncogene. 1994 Oct;9(10):2827-36 8084588 Nature. 1995 Feb 16;373(6515):573-80 7531822 Trends Biochem Sci. 1997 Aug;22(8):314-6 9270306 Bioinformatics. 1999 Apr;15(4):339-40 10320402 Pac Symp Biocomput. 1999;:228-39 10380200 Science. 1999 Jun 18;284(5422):1948-50 10400537 Science. 1999 Jul 30;285(5428):751-3 10427000 Nucleic Acids Res. 2000 Jan 1;28(1):10-4 10592169 Nature. 2000 Feb 10;403(6770):591-2 10688172 Binding, Competitive DNA chemistry metabolism Databases, Factual Information Services Internet Kinetics Models, Molecular Protein Binding Proteins chemistry metabolism PMC29820 2000 1 11 19 15 2001 3 3 10 1 2000 1 11 19 15 ppublish 11125103 PMC29820