Cellular behavior is dictated by complex networks of protein interactions, which are reflected in dense networks of genetic interactions. Recent high throughput (HTP) determination of interactions and phenotypes affords the potential of systems-level understanding of biological responses. However, these approaches are hampered by a dearth of software tools to manage, integrate and query large datasets. We have developed an open database called the BioGRID (www.thebiogrid.org) that now contains over 150,000 interaction from many species including humans. BioGRID is widely used, with on average 80,000 queries and millions of retrieved interactions per month. BioGRID is dynamically linked to a visualization tool called Osprey that allows users to build, query and visualize fully annotated biological interaction networks in graphical format. To augment HTP data, we exhaustively curated the budding yeast literature for over 30,000 reliable protein and genetic interactions, and thereby greatly facilitated prediction of protein function and network attributes. Here, we propose to elaborate BioGRID and Osprey as follows: (i) curation of the yeast, worm, fly, plant and human literature for interactions and post translational modifications; (ii) expansion of the BioGRID platform to allow rapid access to and manipulation of many data types; and (iii) a major new release of Osprey that will enable cross-species network predictions, data integration and network interrogation. All software will be open source and engineered towards compatibility with the Generic Model Organism Database (GMOD) Project. The BioGRID will provide its interaction data and software tools to SGD, Wormbase, Flybase and other interested parties without restriction. The BioGRID/Opsrey platform will thus enable the biomedical and life sciences communities to access fully comprehensive datasets across multiple model organisms for hypothesis generation and network analysis. Relevance: Complex biological networks underlie human development, health and disease. Because human networks are highly analogous to networks in tractable model organisms such as yeast, worms and flies, it is essential that the hundreds of thousands of biological interactions that form networks in all species are housed in a freely available manner that is amenable to rigorous analysis. The BioGRID and Osprey software platforms serve this function for the international research community. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Research Project (R01)
Project #
5R01RR024031-02
Application #
7423861
Study Section
Special Emphasis Panel (ZRG1-GGG-A (52))
Program Officer
Watson, Harold L
Project Start
2007-05-15
Project End
2011-02-28
Budget Start
2008-03-01
Budget End
2009-02-28
Support Year
2
Fiscal Year
2008
Total Cost
$574,771
Indirect Cost
Name
MT Sinai Hosp-Samuel Lunenfeld Research Institute
Department
Type
DUNS #
208808949
City
Toronto
State
ON
Country
Canada
Zip Code
M5 3-L9
Li, Yongsheng; Sahni, Nidhi; Pancsa, Rita et al. (2017) Revealing the Determinants of Widespread Alternative Splicing Perturbation in Cancer. Cell Rep 21:798-812
Kanshin, Evgeny; Giguère, Sébastien; Jing, Cheng et al. (2017) Machine Learning of Global Phosphoproteomic Profiles Enables Discrimination of Direct versus Indirect Kinase Substrates. Mol Cell Proteomics 16:786-798
Wildenhain, Jan; Spitzer, Michaela; Dolma, Sonam et al. (2016) Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism. Sci Data 3:160095
Oughtred, Rose; Chatr-aryamontri, Andrew; Breitkreutz, Bobby-Joe et al. (2016) Use of the BioGRID Database for Analysis of Yeast Protein and Genetic Interactions. Cold Spring Harb Protoc 2016:pdb.prot088880
Oughtred, Rose; Chatr-aryamontri, Andrew; Breitkreutz, Bobby-Joe et al. (2016) BioGRID: A Resource for Studying Biological Interactions in Yeast. Cold Spring Harb Protoc 2016:pdb.top080754
Liu, Guomin; Knight, James D R; Zhang, Jian Ping et al. (2016) Data Independent Acquisition analysis in ProHits 4.0. J Proteomics 149:64-68
Wildenhain, Jan; Spitzer, Michaela; Dolma, Sonam et al. (2015) Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. Cell Syst 1:383-95
Torii, Manabu; Li, Gang; Li, Zhiwen et al. (2014) RLIMS-P: an online text-mining tool for literature-based extraction of protein phosphorylation information. Database (Oxford) 2014:
Chatr-Aryamontri, Andrew; Breitkreutz, Bobby-Joe; Heinicke, Sven et al. (2013) The BioGRID interaction database: 2013 update. Nucleic Acids Res 41:D816-23
Sadowski, Ivan; Breitkreutz, Bobby-Joe; Stark, Chris et al. (2013) The PhosphoGRID Saccharomyces cerevisiae protein phosphorylation site database: version 2.0 update. Database (Oxford) 2013:bat026

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