BioGRID: a general repository for interaction datasets

By Chris Stark, Bobby-Joe Breitkreutz, Teresa Reguly, Lorrie Boucher, Ashton Breitkreutz, Mike Tyers. 2005

4,071 citations recorded by the source corpus.

Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at . BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. Over 30 000 interactions have recently been added from 5778 sources through exhaustive curation of the Saccharomyces cerevisiae primary literature. An internally hyper-linked web interface allows for rapid search and retrieval of interaction data. Full or user-defined datasets are freely downloadable as tab-delimited text files and PSI-MI XML. Pre-computed graphical layouts of interactions are available in a variety of file formats. User-customized graphs with embedded protein, gene and interaction attributes can be constructed with a visualization system called Osprey that is dynamically linked to the BioGRID.

Subfields: Molecular Biology

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Citations in the corpus

Graphite: Iterative Generative Modeling of Graphs (2018, methodology): “We compared across four standard benchmark datasets: a Protein-Protein Interaction (PPI) network for Homo Sapiens [17, 5] with proteins as nodes and interactions as edges, and three citation networks Cora, Citeseer, and Pubmed with papers as nodes and citations as edges [15].”
Computational approaches in target identification and drug discovery (2016, methodology): “The Biological General Repository for Interaction Datasets (BioGRID) [30] is an interaction repository with compiled biological data freely available in standardized formats, linked with software platforms for visualization of complex interaction networks such as Osprey [31] and Cytoscape [32].”
Walking the interactome for prioritization of candidate disease genes. (2008, methodology): “These datasets comprise interactions extracted from HPRD, 20 BIND, 21 and BioGrid 22 .”
Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge (2017, methodology): “Protein–protein physical interaction data was retrieved from BioGRID [39] (168,956 interactions from BIOGRID-ORGANISMHomo_sapiens-3.”
Detecting overlapping protein complexes in protein-protein interaction networks (2012, methodology): “RNSC Collins [7] Krogan [6] Gavin [5] BioGRID [8] core extended Shuffling diversification length 5 9 9 9 9 Diversification frequency 50 20 20 20 20 Number of experiments 3 3 3 3 3 Naive stopping tolerance 10 10 20 20 20 Scaled stopping tolerance 5 5 1 5 5 Tabu length 100 10 50 100 10 Tabu tolerance ”
The Index-Based Subgraph Matching Algorithm (ISMA): Fast Subgraph Enumeration in Large Networks Using Optimized Search Trees (2013, background): “The second consists of protein-protein interactions in yeast (X, undirected, obtained from the BioGRID [34] database), protein-protein interactions in human (Y, undirected, obtained from the BioGRID [34] and STRING [35] databases), and orthology relations between human and yeast proteins (Z,…”
Where Have All the Interactions Gone? Estimating the Coverage of Two-Hybrid Protein Interaction Maps (2007, background): “Counts of reported interaction partners from other screens are from BioGRID [53].”
Utilizing graph machine learning within drug discovery and development (2021, background): “Drugs (structure, indications, targets) [117–121] Dr Drug combinations [122, 123] DC Protein (structure, sequence) [124] PS Protein interactions [125, 126] PI Gene annotations [127–129] GA Diseases [127, 128, 130, 131] Di”
Data Mining Techniques for the Life Sciences (2010, methodology): “BioGRID provides the most up-to-date and virtually complete set of interaction data reported in the published literatures for both the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe (42).”
Massive mining of publicly available RNA-seq data from human and mouse (2017, methodology): “MAP, BioGRID, and BioPLEX respectively (Fig.”