4.8 Article

The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets

期刊

NUCLEIC ACIDS RESEARCH
卷 49, 期 D1, 页码 D605-D612

出版社

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkaa1074

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资金

  1. Swiss Institute of Bioinformatics
  2. Novo Nordisk Foundation [NNF14CC0001]
  3. European Molecular Biology Laboratory (EMBL Heidelberg)
  4. Academy of Finland [332844]
  5. University of Zurich

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The STRING database integrates various associations between proteins, including physical interactions and functional associations, by collecting evidence from sources such as literature, databases, computational predictions, and systematic transfers. STRING aims for wide coverage and provides extensive user interface features and querying methods for experimental data.
Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein-protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data.

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