The BioGRID Interaction Database: 2011 update

By Chris Stark, Bobby-Joe Breitkreutz, A. Chatr-aryamontri, Lorrie Boucher, Rose Oughtred, M. Livstone, Julie Nixon, K. V. Auken, Xiaodong Wang, Xiaoqi Shi and 5 more. Nucleic Acids Res., 2010

473 citations recorded by the source corpus.

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein interaction data from model organisms and humans (http://www.thebiogrid.org). BioGRID currently holds 347 966 interactions (170 162 genetic, 177 804 protein) curated from both high-throughput data sets and individual focused studies, as derived from over 23 000 publications in the primary literature. Complete coverage of the entire literature is maintained for budding yeast (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe) and thale cress (Arabidopsis thaliana), and efforts to expand curation across multiple metazoan species are underway. The BioGRID houses 48 831 human protein interactions that have been curated from 10 247 publications. Current curation drives are focused on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health. The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. An automated Interaction Management System (IMS) is used to prioritize, coordinate and track curation across international sites and projects. BioGRID provides interaction data to several model organism databases, resources such as Entrez-Gene and other interaction meta-databases. The entire BioGRID 3.0 data collection may be downloaded in multiple file formats, including PSI MI XML. Source code for BioGRID 3.0 is freely available without any restrictions.

Subfields: Molecular Biology

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Citations in the corpus

Interactome3D: adding structural details to protein networks (2013, methodology): “This database is generated by merging the data available from nine major public protein-interaction databases: Intact 20 , MINT 21 , DIP 44 , MPIDB 45 , MatrixDb 46 , InnateDb 47 , BioGRID 48 , BIND (from the PSI-MI 2.5 version by Gary Bader’s laboratory 49 ) and HPRD 50 .”
g:Profiler—a web server for functional interpretation of gene lists (2011 update) (2011, unknown): “We currently cover >300 000 protein–protein interactions from the Biogrid database (20), for seven species including human, mouse, fly and yeast.”
Mining breast cancer genes with a network based noise-tolerant approach (2013, methodology): “The human PPI data were derived from five sources: HPRD [41], BioGRID [42], homoMINT [43], IntAct [44] and a manually curated human signalling network [45].”
ExoCarta as a resource for exosomal research (2012, methodology): “ExoCarta provides users with protein interactors for their molecule of interest [the protein-protein interaction data is obtained from Biological General Repositories for Interaction Datasets (BioGRID) (44) and Human Protein Reference Database (HPRD) (45)].”
Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes (2012, methodology): “…similarity scores S(i,j) , which reflect the similarity between proteins i and j : Protein physical interactions: We acquired protein-protein physical interaction data from MiMI (Michigan Molecular Interactions) [54], BIND [21], BioGRID [24], DIP [55], IntAct [22], IPI, MINT [23] and Reactome [56].”
Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes (2013, methodology): “Physical and genetic interaction data from BioGRID [70], IntAct [71], Mint [72], and MIPS [73] were processed as counts of experimental assays that support an interaction between two genes (e.”
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec (2017, background): “Protein-Protein Interactions (PPI) [32] is a subgraph of PPI network for Homo Sapiens. labels are obtained from the hallmark gene sets and represent biological states.”
HINT: High-quality protein interactomes and their applications in understanding human disease (2012, methodology): “Although there are numerous databases [9-16] that try to systematically curate the entire repository of interactions for different organisms, there has been very little effort in filtering out unreliable ones.”
A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome (2015, methodology): “We use IntAct, MINT, BioGRID [45] and HPRD [46], resulting in 88,349 interactions between 11,798 proteins. iv.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “…Ding et al., 2012 BioGRID Integrated protein–protein interaction data http://thebiogrid.org Stark et al., 2011 BioProfiling Inference of network data from expression patterns http://www.bioprofiling.de Antonov et al., 2009 DIP Experimental…”
The BioGRID interaction database: 2013 update (2012, methodology): “All BioGRID interaction records are directly mapped to experimental evidence in the supporting publication, as classified by a structured set of evidence codes (12).”
STRING v9.1: protein-protein interaction networks, with increased coverage and integration (2012, unknown): “Apart from the primary databases that hold the experimental data in this ﬁeld (16–20) and hand-curated databases serving expert annotations (21,22), a number of resources take a meta-analysis approach, similar to STRING.”
Systems Pharmacology: Network Analysis to Identify Multiscale Mechanisms of Drug Action (2012, background): “In the context of studying drug action, edges may represent protein-protein interactions (5), drug-target interactions (12), or transcriptional regulation (13).”