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The Biogrid Interaction Database D470–D478 Nucleic Acids Research, 2015, Vol. 43, Database issue Published online 26 November 2014 doi: 10.1093/nar/gku1204 The BioGRID interaction database: 2015 update Andrew Chatr-aryamontri1, Bobby-Joe Breitkreutz2, Rose Oughtred3, Lorrie Boucher2, Sven Heinicke3, Daici Chen1, Chris Stark2, Ashton Breitkreutz2, Nadine Kolas2, Lara O’Donnell2, Teresa Reguly2, Julie Nixon4, Lindsay Ramage4, Andrew Winter4, Adnane Sellam5, Christie Chang3, Jodi Hirschman3, Chandra Theesfeld3, Jennifer Rust3, Michael S. Livstone3, Kara Dolinski3 and Mike Tyers1,2,4,* 1Institute for Research in Immunology and Cancer, Universite´ de Montreal,´ Montreal,´ Quebec H3C 3J7, Canada, 2The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada, 3Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA, 4School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK and 5Centre Hospitalier de l’UniversiteLaval´ (CHUL), Quebec,´ Quebec´ G1V 4G2, Canada Received September 26, 2014; Revised November 4, 2014; Accepted November 5, 2014 ABSTRACT semi-automated text-mining approaches, and to en- hance curation quality control. The Biological General Repository for Interaction Datasets (BioGRID: http://thebiogrid.org) is an open access database that houses genetic and protein in- INTRODUCTION teractions curated from the primary biomedical lit- Massive increases in high-throughput DNA sequencing erature for all major model organism species and technologies (1) have enabled an unprecedented level of humans. As of September 2014, the BioGRID con- genome annotation for many hundreds of species (2–6), tains 749 912 interactions as drawn from 43 149 pub- which has led to tremendous progress in the understand- lications that represent 30 model organisms. This ing of gene organization, genome evolution and the genetic interaction count represents a 50% increase com- basis for disease. At the same time, sequencing-based meth- pared to our previous 2013 BioGRID update. BioGRID ods have uncovered many intricacies of gene regulation at data are freely distributed through partner model or- a genomic scale, including expression patterns, alternative splicing, non-coding transcription and the myriad of regu- ganism databases and meta-databases and are di- latory factors that bind DNA and RNA (7–11). Proteomics rectly downloadable in a variety of formats. In addi- approaches, largely based on mass spectrometry, have sim- tion to general curation of the published literature ilarly mapped the abundance and post-translational mod- for the major model species, BioGRID undertakes ifications of proteins at impressive depth of coverage (12– themed curation projects in areas of particular rele- 15). At the phenotypic level, genome-wide reagent collec- vance for biomedical sciences, such as the ubiquitin- tions for systematic perturbation of gene function have led proteasome system and various human disease- to compendia of functional profiles for many different phe- associated interaction networks. BioGRID curation notypic characteristics (16–19). This wealth of new data has is coordinated through an Interaction Management been accrued in model organism systems, and particularly System (IMS) that facilitates the compilation inter- in humans, in both normal and disease contexts. In spite of action records through structured evidence codes, this data deluge, the fundamental problem of how genotype is translated into phenotype, and how genetic mutations can phenotype ontologies, and gene annotation. The Bi- affect this complex relationship, remains a formidable road- oGRID architecture has been improved in order to block in our understanding of fundamental biology and the support a broader range of interaction and post- basis for human disease. translational modification types, to allow the repre- It is now evident that genes and their encoded proteins sentation of more complex multi-gene/protein inter- function in the context of a vast, dynamic network of inter- actions, to account for cellular phenotypes through actions (20–23). The generation of comprehensive genetic structured ontologies, to expedite curation through and protein interaction maps will thus be essential for un- raveling the many complexities of biological processes and for understanding the general genotype to phenotype map- *To whom correspondence should be addressed. Tel: +1 514 343 6668; Fax: +1 514 343 5839; Email: [email protected] C The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] Nucleic Acids Research, 2015, Vol. 43, Database issue D471 ping problem (24). For example, the integration of genetic In 2014, the BioGRID user base was located primarily in interaction networks with other genome-wide data types the USA (30%), followed by China (8%), United Kingdom has helped to explain how sets of genes function differently (7%), Canada (6%), Germany (6%), Japan (6%), India (4%), in specific cellular contexts, conditions or tissues (25–28). France (4%), Spain (2%) and all other countries (27%). The systematic experimental identification and characteri- zation of protein and genetic interaction networks in ma- jor model organism species and humans has continued to DATA CURATION AND QUALITY CONTROL grow in pace and scale (21,23,29–35). With such interaction BioGRID continues to maintain complete curation of the datasets in hand, it has been possible to implement compu- primary literature for genetic and protein interactions in tational methods for analysis and prediction of the response the model yeasts Saccharomyces cerevisiae (342 878 total of cellular networks to perturbation by disease-associated interactions) and Schizosaccharomices pombe (68 015 to- mutation or pathogen infection (28,36–38). tal interactions). These datasets are updated on a monthly The comprehensive annotation and compilation of all basis and released for redistribution through the Saccha- known biological interactions in a computable form is es- romyces Genome Database (41) and PomBase (43). In ad- sential for network-based approaches to understanding bi- dition to these two yeasts, BioGRID contains interaction ological systems and human disease (39). The Biological data for more than 30 model organisms at varying depths General Repository for Interaction Datasets (BioGRID: of coverage. However, the immense extent of the biomedi- http://thebiogrid.org) was established in order to help cap- cal literature––more than 24 million articles in PubMed as ture biological interaction data from the primary biomed- of August 2014––and its ever-accelerating rate of growth ical literature and to provide this data in a readily com- render the complete manual curation of all interaction data putable format (40). BioGRID collects and annotates ge- virtually impossible (39). The identification of publications netic and protein interaction data from the published lit- that contain actual interaction data is a non-trivial step in erature for all major model organism species and humans. the curation workflow (54). Although the entire BioGRID When available, data on the influence of protein post- dataset is drawn directly from just 43 149 publications, in translational modifications, including phosphorylation and reality several-fold more publications have been directly ubiquitination, is also captured. The complete BioGRID parsed by curators, usually in an entirely manual fashion dataset is freely accessible through a dedicated web-based (55). While our initial strategy for the identification of rele- search portal and is also available for download in various vant papers was based on simple PubMed searches based on standardized formats. BioGRID data content is updated keywords and/or gene names, we now prioritize literature and permanently archived on a monthly basis, and in ad- queues for different projects through advanced text-mining dition to the BioGRID web interface, is disseminated to approaches. For example, BioGRID has several projects the research community through model organism database that are facilitated by Support Vector Machine (SVM) (MOD) partners (41–46) and other biological resources analyses carried out in collaboration with the Textpresso and meta-databases (47–52). The interaction datasets in Bi- text-mining group (56). We have also begun to use text- oGRID thus provide a resource for biomedical researchers mining for the curation of protein phosphorylation sites who study the function of individual genes and pathways, as through a collaboration with developers of the RLIMS-P well as for computational biologists who analyze the prop- system (57). To facilitate the development of improved text- erties of large biological networks. mining approaches, the BioGRID routinely contributes to the BioCreative (Critical Assessment of Information Ex- traction in Biology) challenge by providing test datasets and DATABASE GROWTH AND STATISTICS curation expertise (58–60). The current BioGRID release (August 2014, version Curation accuracy and consistency are critical for the 3.2.115) houses a total of 749 912 interactions (515 032 non- integrity of the BioGRID resource.
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