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The BioGRID interaction database: 2017 update.

Andrew Chatr-Aryamontri1, Rose Oughtred2, Lorrie Boucher3

  • 1Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Quebec H3T 1J4, Canada.

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Summary
This summary is machine-generated.

The Biological General Repository for Interaction Datasets (BioGRID) database now archives over 1 million genetic and protein interactions, including chemical-protein interactions for drug discovery. This resource supports biological research across 66 model organisms and humans.

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Area of Science:

  • * Molecular Biology
  • * Bioinformatics
  • * Systems Biology

Background:

  • * The Biological General Repository for Interaction Datasets (BioGRID) is a vital open-access database for curated biological interaction data.
  • * It archives protein, genetic, and chemical interactions across human and major model organisms.

Purpose of the Study:

  • * To present the updated BioGRID database (build 3.4.140) as of September 2016.
  • * To highlight the expansion of curated interaction data and the inclusion of chemical-protein interactions for drug discovery.
  • * To introduce a new dynamic network viewer for data exploration.

Main Methods:

  • * Manual curation of protein, genetic, and chemical interactions from the biomedical literature.
  • * Integration of chemical-protein interaction data from the DrugBank database.
  • * Development of a dynamic network viewer for interactive data visualization and filtering.

Main Results:

  • * BioGRID build 3.4.140 contains 1,072,173 genetic and protein interactions and 38,559 post-translational modifications from 48,114 publications.
  • * The dataset covers 66 model organisms, representing a 30% increase from the previous year.
  • * Incorporation of 27,501 chemical-protein interactions for human drug targets.
  • * A new dynamic viewer facilitates navigation and filtering of interaction data and bioactive compounds.

Conclusions:

  • * BioGRID continues to be a comprehensive and expanding resource for biological interaction data.
  • * The inclusion of chemical-protein interactions enhances its utility for drug discovery and network-based research.
  • * Freely available data and advanced visualization tools promote accessibility and further research.