Gary D Bader

Publications (85)857.56 Total impact

• Article: Cytoscape App Store.

  Samad Lotia, Jason Montojo, Yue Dong, Gary D Bader, Alexander R Pico
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  ABSTRACT: 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. AVAILABILITY: The App Store is available at http://apps.cytoscape.org. CONTACT: apico@gladstone.ucsf.edu.
  Bioinformatics 04/2013; · 5.47 Impact Factor
• Source

  Available from: Xiaofeng Xin

  Article: SH3 interactome conserves general function over specific form.

  Xiaofeng Xin, David Gfeller, Jackie Cheng, Raffi Tonikian, Lin Sun, Ailan Guo, Lianet Lopez, Alevtina Pavlenco, Adenrele Akintobi, Yingnan Zhang, [......], Dryden Bouamalay, Adrien Lugari, David E Hill, Mark L Grimes, David G Drubin, Barth D Grant, Marc Vidal, Charles Boone, Sachdev S Sidhu, Gary D Bader
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  ABSTRACT: 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.
  Molecular Systems Biology 04/2013; 9:652. · 8.63 Impact Factor
• Article: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers.

  Jüri Reimand, Gary D Bader
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  ABSTRACT: 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.
  Molecular Systems Biology 01/2013; 9:637. · 8.63 Impact Factor
• Article: Predicting PDZ domain mediated protein interactions from structure.

  Shirley Hui, Xiang Xing, Gary D Bader
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  ABSTRACT: BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
  BMC Bioinformatics 01/2013; 14(1):27. · 2.75 Impact Factor
• Source

  Available from: Gianni Cesareni

  Dataset: Nat. Methods 2011 Aranda

  Bruno Aranda, Hagen Blankenburg, Samuel Kerrien, Fiona S L Brinkman, Arnaud Ceol, Emilie Chautard, Jose M Dana, Javier De Las Rivas, Marine Dumousseau, Eugenia Galeota, [......], Gary D Bader, Gianni Cesareni, Ian M Donaldson, David Eisenberg, Gerard J Kleywegt, John Overington, Sylvie Ricard-Blum, Mike Tyers, Mario Albrecht, Henning Hermjakob
• Article: The Biology/Disease-driven Human Proteome Project (B/D-HPP): Enabling Protein Research for the Life Sciences Community.

  Ruedi Aebersold, Gary D Bader, Aled M Edwards, Jennifer E van Eyk, Martin Kussmann, Jun Qin, Gilbert S Omenn
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  ABSTRACT: 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.
  Journal of Proteome Research 12/2012; · 5.11 Impact Factor
• Article: A travel guide to Cytoscape plugins.

  Rintaro Saito, Michael E Smoot, Keiichiro Ono, Johannes Ruscheinski, Peng-Liang Wang, Samad Lotia, Alexander R Pico, Gary D Bader, Trey Ideker
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  ABSTRACT: 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.
  Nature Methods 11/2012; 9(11):1069-76. · 19.28 Impact Factor
• Article: Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study.

  Mohammed Alshalalfa, Gary D Bader, Anna Goldenberg, Quaid Morris, Reda Alhajj
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  ABSTRACT: BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
  BMC Systems Biology 08/2012; 6(1):112. · 3.15 Impact Factor
• Source

  Available from: Giuseppe Cinalli

  Article: Subgroup-specific structural variation across 1,000 medulloblastoma genomes.

  Paul A Northcott, David J H Shih, John Peacock, Livia Garzia, A Sorana Morrissy, Thomas Zichner, Adrian M Stütz, Andrey Korshunov, Jüri Reimand, Steven E Schumacher, [......], Eric Bouffet, Stephen W Scherer, James T Rutka, David Malkin, Steven C Clifford, Steven J M Jones, Jan O Korbel, Stefan M Pfister, Marco A Marra, Michael D Taylor
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  ABSTRACT: 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.
  Nature 07/2012; 488(7409):49-56. · 36.28 Impact Factor
• Source

  Available from: Magali Michaut

  Article: Multiple genetic interaction experiments provide complementary information useful for gene function prediction.

  Magali Michaut, Gary D Bader
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  ABSTRACT: 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.
  PLoS Computational Biology 06/2012; 8(6):e1002559. · 5.22 Impact Factor
• Article: Domain-mediated protein interaction prediction: From genome to network.

  Jüri Reimand, Shirley Hui, Shobhit Jain, Brian Law, Gary D Bader
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  ABSTRACT: 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.
  FEBS letters 05/2012; 586(17):2751-63. · 3.54 Impact Factor
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  Available from: Magali Michaut

  Article: Mapping the Hsp90 genetic interaction network in Candida albicans reveals environmental contingency and rewired circuitry.

  Stephanie Diezmann, Magali Michaut, Rebecca S Shapiro, Gary D Bader, Leah E Cowen
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  ABSTRACT: 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.
  PLoS Genetics 03/2012; 8(3):e1002562. · 8.69 Impact Factor
• Article: Chromatin is an ancient innovation conserved between Archaea and Eukarya.

  Ron Ammar, Dax Torti, Kyle Tsui, Marinella Gebbia, Tanja Durbic, Gary D Bader, Guri Giaever, Corey Nislow
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  ABSTRACT: 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.
  eLife. 01/2012; 1:e00078.
• Source

  Available from: Michael Hucka

  Article: Corrigendum: The BioPAX community standard for pathway data sharing.

  Emek Demir, Michael P Cary, Suzanne Paley, Ken Fukuda, Christian Lemer, Imre Vastrik, Guanming Wu, Peter D'Eustachio, Carl Schaefer, Joanne Luciano, [......], Susumu Goto, Michael Hucka, Nicolas Le Novère, Natalia Maltsev, Akhilesh Pandey, Paul Thomas, Edgar Wingender, Peter D Karp, Chris Sander, Gary D Bader
  Nature Biotechnology 01/2012; 30(4):365. · 29.50 Impact Factor
• Article: Phosphorylation sites of higher stoichiometry are more conserved.

  Chris Soon Heng Tan, Gary D Bader
  Nature Methods 01/2012; 9(4):317; author reply 318. · 19.28 Impact Factor
• Source

  Available from: Haiming Huang

  Article: MUSI: an integrated system for identifying multiple specificity from very large peptide or nucleic acid data sets.

  Taehyung Kim, Marc S Tyndel, Haiming Huang, Sachdev S Sidhu, Gary D Bader, David Gfeller, Philip M Kim
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  ABSTRACT: 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.
  Nucleic Acids Research 12/2011; 40(6):e47. · 8.03 Impact Factor
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  Available from: Anatoly Sorokin

  Article: Meeting report from the first meetings of the Computational Modeling in Biology Network (COMBINE).

  Nicolas Le Novère, Michael Hucka, Nadia Anwar, Gary D Bader, Emek Demir, Stuart Moodie, Anatoly Sorokin
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  ABSTRACT: 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.
  Standards in Genomic Sciences 11/2011; 5(2):230-42. · 1.62 Impact Factor
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  Available from: Malene L Urbanus

  Article: Compound prioritization methods increase rates of chemical probe discovery in model organisms.

  Iain M Wallace, Malene L Urbanus, Genna M Luciani, Andrew R Burns, Mitchell K L Han, Hao Wang, Kriti Arora, Lawrence E Heisler, Michael Proctor, Robert P St Onge, Terry Roemer, Peter J Roy, Carolyn L Cummins, Gary D Bader, Corey Nislow, Guri Giaever
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  ABSTRACT: 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.
  Chemistry & biology 10/2011; 18(10):1273-83. · 6.52 Impact Factor
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  Available from: pbtfus.org

  Article: Delineation of two clinically and molecularly distinct subgroups of posterior fossa ependymoma.

  Hendrik Witt, Stephen C Mack, Marina Ryzhova, Sebastian Bender, Martin Sill, Ruth Isserlin, Axel Benner, Thomas Hielscher, Till Milde, Marc Remke, [......], Olaf Witt, Gary D Bader, Cynthia E Hawkins, Uri Tabori, Abhijit Guha, James T Rutka, Peter Lichter, Andrey Korshunov, Michael D Taylor, Stefan M Pfister
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  ABSTRACT: 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.
  Cancer cell 08/2011; 20(2):143-57. · 25.29 Impact Factor
• Article: PSICQUIC and PSISCORE: accessing and scoring molecular interactions

  Bruno Aranda, Hagen Blankenburg, Samuel Kerrien, Fiona S L Brinkman, Arnaud Ceol, Emilie Chautard, Jose M Dana, Javier De Las Rivas, Marine Dumousseau, Eugenia Galeota, [......], Gary D Bader, Gianni Cesareni, Ian M Donaldson, David Eisenberg, Gerard J Kleywegt, John Overington, Sylvie Ricard-Blum, Mike Tyers, Mario Albrecht, Henning Hermjakob
  Nature Methods 06/2011; 8(7):528-529. · 19.28 Impact Factor