The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored

By Damian Szklarczyk, Andrea Franceschini, Michael Kuhn, Milan Simonovic, Alexander C. J. Roth, Pablo Minguez, Tobias Doerks, M. Stark, Jean Muller, Peer Bork and 2 more. Nucleic Acids Res., 2010

3,517 citations recorded by the source corpus.

An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein–protein interaction information can be error-prone and require considerable effort to annotate. Here, we present an update on the online database resource Search Tool for the Retrieval of Interacting Genes (STRING); it provides uniquely comprehensive coverage and ease of access to both experimental as well as predicted interaction information. Interactions in STRING are provided with a confidence score, and accessory information such as protein domains and 3D structures is made available, all within a stable and consistent identifier space. New features in STRING include an interactive network viewer that can cluster networks on demand, updated on-screen previews of structural information including homology models, extensive data updates and strongly improved connectivity and integration with third-party resources. Version 9.0 of STRING covers more than 1100 completely sequenced organisms; the resource can be reached at http://string-db.org.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/ceee6447b291f8052a28c9eb00ca360d6f39f9b1

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Citations in the corpus

SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features (2014, methodology): “The same four centrality measures were calculated for each protein in a PPI network obtained fromSTRINGby filtering for human interactionswith experimental evidence [51].”
Review of Drug Repositioning Approaches and Resources (2018, background): “Collecting massive amounts of literature is a necessary task for researchers; hence, many medical literature databases [10, 12, 86] have been built.”
Protein interaction data curation: the International Molecular Exchange (IMEx) consortium (2012, methodology): “This data are also highly redundant, in that the manually curated data in primary databases are re-exported by several integrative databases such as iRefIndex 25 , Agile Protein Interaction DataAnalizer ( ) 27 and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 28 .”
Gender bias in autoimmunity is influenced by microbiota (2013, positive): “network of signaling pathways with the STRING database (Szklarczyk et al., 2011).”
eggNOG v4.0: nested orthology inference across 3686 organisms (2013, background): “Many methods for orthology analysis scale quadratically with an increasing number of genomes such that a naı¨ve expansion to include all available genomes is no longer feasible. eggNOG, and afﬁliated databases such as STRING (20), tentatively have addressed this issue by dividing species space into ”
SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics (2019, background): “Like scTensor, PyMINEr relies on automatically generated LR references from STRING.”
Network enrichment analysis: extension of gene-set enrichment analysis to gene networks (2012, background): “We observe the same trend for the human STRING network [31] (link confidence cutoff > 0.”
The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible (2016, methodology): “Programmatic access is provided via a REST-API (41), via an R / Bioconductor package (34) and via a mechanism to add additional user-provided interactions, as well as protein-centric information, onto the web-site (‘data payload’) (32).”
PaxDb, a Database of Protein Abundance Averages Across All Three Domains of Life (2012, methodology): “We therefore systematically compute abundance ratios for pairs of proteins that are known to be functionally connected ( i.e. those having an interaction score of at least 0.900 in the STRING database (54)).”
STITCH 3: zooming in on protein–chemical interactions (2011, methodology): “To provide crucial context for the aggregated protein–chemical interactions, protein–protein interactions from the STRING 9.0 database (22) are incorporated into a seamless network view.”
BioGPS and MyGene.info: organizing online, gene-centric information (2012, background): “One might also visit the Mouse Genome Database for information on the mouse knockout phenotype (2); or the UCSC genome browser for details on local regulatory elements (3); or Reactome for the relevant pathway context (4); or STRING to browse the local protein network (5).”
SMART 7: recent updates to the protein domain annotation resource (2011, methodology): “It indexes the complete annotation pages for all SMART and Pfam domains, as well as Uniprot, Ensembl and STRING protein descriptions, allowing users to quickly identify domains or proteins of interest.”