Cited by 1,858Bioinformatics and Genomic NetworksBiomedical Text Mining and OntologiesGene Regulatory Network Analysis

The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions

Open access
Oughtred, Rose, Rust, Jennifer, Chang, Christie, Breitkreutz, Bobby‐Joe, Stark, Chris, Willems, Andrew, Boucher, Lorrie, Leung, Genie +7 more·2021·Protein Science

Abstract

The BioGRID (Biological General Repository for Interaction Datasets, thebiogrid.org ) is an open‐access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ~1.93 million curated interactions in BioGRID can be used to build complex networks to facilitate biomedical discoveries, particularly as related to human health and disease. All BioGRID content is curated from primary experimental evidence in the biomedical literature, and includes both focused low‐throughput studies and large high‐throughput datasets. BioGRID also captures protein post‐translational modifications and protein or gene interactions with bioactive small molecules including many known drugs. A built‐in network visualization tool combines all annotations and allows users to generate network graphs of protein, genetic and chemical interactions. In addition to general curation across species, BioGRID undertakes themed curation projects in specific aspects of cellular regulation, for example the ubiquitin‐proteasome system, as well as specific disease areas, such as for the SARS‐CoV‐2 virus that causes COVID‐19 severe acute respiratory syndrome. A recent extension of BioGRID, named the Open Repository of CRISPR Screens (ORCS, orcs.thebiogrid.org ), captures single mutant phenotypes and genetic interactions from published high throughput genome‐wide CRISPR/Cas9‐based genetic screens. BioGRID‐ORCS contains datasets for over 1,042 CRISPR screens carried out to date in human, mouse and fly cell lines. The biomedical research community can freely access all BioGRID data through the web interface, standardized file downloads, or via model organism databases and partner meta‐databases.

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Crossref · 10.1002/pro.3978

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73 of 73 references verified0 citation errors · 0 not checkable

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  • Based on BioGRID, trans associations between TMEM119 and SELPLG, as well as GAL3ST2 and MUC-16 had no supporting interactions.

    “Based on BioGRID42,43, trans associations between TMEM119 and SELPLG, as well as GAL3ST2 and MUC-16 had no supporting interactions.”

    From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [43]

    The Protein and Genetic Interactions section describes BioGRID as a database of experimentally supported interactions that users can search by gene or protein. The paper does not report whether TMEM119–SELPLG or GAL3ST2–MUC-16 have supporting interactions, so our copy cannot verify the cited sentence’s specific finding. (LLM, conf=0.82)

    Experimental — accuracy not yet measured
  • In the case of BioGRID, none of the trans associations had support for a functional connection with GDF-15.

    “In the case of BioGRID42,43, none of the trans associations had support for a functional connection with GDF-15.”
  • 47% of the gold standard PPIs were confidently predicted (AF contact probability > 0.67), with lower ratios (31% and 24%) for candidate PPIs from the literature or supported by low-throughput experiments according to BIOGRID.

    “We found that 47% of the gold standard PPIs were confidently predicted, with lower ratios (31% and 24%) for candidate PPIs from the literature (http://interactome.dfci.harvard.edu/S_cerevisiae/download/LC_multiple.txt) (3) or supported by low-throughput experiments according to BIOGRID (21) (Fig. 1D).”

    From Computed structures of core eukaryotic protein complexes (2021) · cites this paper as [21]

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Authors
Oughtred, R., Rust, J., Chang, C., Breitkreutz, B., Stark, C., Willems, A., Boucher, L., Leung, G., Kolas, N., Zhang, F., Dolma, S., Coulombe‐Huntington, J., Chatr‐aryamontri, A., Dolinski, K., & Tyers, M.
Affiliations
  • Princeton University (US)
  • Mount Sinai Hospital (CA)
  • Hospital for Sick Children (CA)
  • Institute for Research in Immunology and Cancer (CA)
Corresponding author
  • Dolinski, Kara
  • Tyers, Mike
Venue
Protein Science
Publisher
Wiley
Published
2020-11-23
Volume
30
Issue
1
Pages
187-200
ISSN
  • 0961-8368 (ISSN-L)
  • 1469-896X
OpenAlex
W3093053585
Cited by
1,858
References
73
Citations by year
2026: 356 · 2025: 442 · 2024: 432 · 2023: 261 · 2022: 250 · 2021: 114 · 2020: 3
FWCI
51.51
Type
journal article
Language
en
Open access
Locations
  • Protein Science (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Canadian Institutes of Health Research (FDN‐167277)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
BioGRID, protein-protein interaction, genetic interactions, chemical interactions, post-translational modification, drug-target interactions, biological networks, network visualization, CRISPR screen, ubiquitin-proteasome system, SARS-CoV-2, cell regulation, model organisms
MeSH terms
COVID-19, SARS-CoV-2, Animals, Humans, Proteins, User-Computer Interface, Databases, Factual, Protein Interaction Mapping, Mice

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