Complex protein and genetic interaction networks determine the properties of all biological systems and underlie human development, health and disease. Decades of biochemical, genetic and molecular biological experiments have identified myriad molecular processes that underpin specific biological functions, as documented in the primary biomedical literature. Recent technological innovations combined with complete genome sequence information have enabled various high-throughput (HTP) methods to generate protein and genetic interaction data on an unprecedented scale. Because human interaction networks are very frequently directly analogous to networks in tractable model organisms, it is essential that the hundreds of thousands of biological interactions that comprise these networks in across all major model organisms are archived in a well- annotated manner that is amenable to rigorous analysis. To capture, integrate and interrogate this wealth of data from both the literature and HTP datasets, we developed the BioGRID database as an open repository for protein and genetic interactions (www.thebiogrid.org). BioGRID is widely used by the biological and biomedical research community, with on average over 6,500 unique visitors per month who explore the over 330,000 interactions in BioGRID with the database search functions and visualization tools. In addition, the unique datasets in BioGRID are disseminated widely by a host of partner databases and applications. Here, we propose to markedly enhance the data content, the database architecture and the user interface of BioGRID. We will elaborate our comprehensive curation approach to main metazoan model organisms and humans, with an overall theme on conserved interaction networks that are implicated in human disease. As part of this curation effort, we will capture important attributes associated with protein and genetic interactions, including post-translational modifications, quantitative interaction data, allele information and interaction directionality. User access to these large datasets will be facilitated by data-rich interfaces, user-defined search and display parameters, customized user accounts and dynamically embedded network visualization datasets. All software will be open source and engineered toward compatibility and complementary with other academic database and software development efforts. The BioGRID will continue to provide its interaction data in their entirety and software tools to the model organism databases and other interested parties without restriction. The BioGRID 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.
Through this work, we will provide a comprehensive database of protein and genetic interaction networks for multiple model organisms and humans, along with the requisite resources and tools to explore this information. This database, called the BioGRID, will lead to a better understanding of human disease by allowing the comparison of gene and protein functions in human health and disease to those in model organisms. The large amounts of data in the BioGRID will be provided too many other databases and users without restriction and will thereby facilitate both basic understanding and early phases of drug discovery.
| Bertomeu, Thierry; Coulombe-Huntington, Jasmin; Chatr-Aryamontri, Andrew et al. (2018) A High-Resolution Genome-Wide CRISPR/Cas9 Viability Screen Reveals Structural Features and Contextual Diversity of the Human Cell-Essential Proteome. Mol Cell Biol 38: |
| Schapira, Matthieu; Tyers, Mike; Torrent, Maricel et al. (2017) WD40 repeat domain proteins: a novel target class? Nat Rev Drug Discov 16:773-786 |
| 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 |
| Islamaj Dogan, Rezarta; Kim, Sun; Chatr-Aryamontri, Andrew et al. (2017) The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions. Database (Oxford) 2017: |
| Chatr-Aryamontri, Andrew; Oughtred, Rose; Boucher, Lorrie et al. (2017) The BioGRID interaction database: 2017 update. Nucleic Acids Res 45:D369-D379 |
| Courcelles, Mathieu; Coulombe-Huntington, Jasmin; Cossette, Émilie et al. (2017) CLMSVault: A Software Suite for Protein Cross-Linking Mass-Spectrometry Data Analysis and Visualization. J Proteome Res 16:2645-2652 |
| 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 |
| Dolma, Sonam; Selvadurai, Hayden J; Lan, Xiaoyang et al. (2016) Inhibition of Dopamine Receptor D4 Impedes Autophagic Flux, Proliferation, and Survival of Glioblastoma Stem Cells. Cancer Cell 29:859-873 |
| 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 |
| Kim, Sun; Islamaj Do?an, Rezarta; Chatr-Aryamontri, Andrew et al. (2016) BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID. Database (Oxford) 2016: |
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