STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

By Damian Szklarczyk, Annika L. Gable, D. Lyon, Alexander Junge, S. Wyder, Jaime Huerta-Cepas, Milan Simonovic, Nadezhda T. Doncheva, J. H. Morris, Peer Bork and 2 more. Nucleic Acids Res., 2018

15,157 citations recorded by the source corpus.

Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1b6d2c08e5b0b7ec50366b0175d266ed37ab7d77

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In Molecular Biology

1. Ivica Letunic
2. Peer Bork
3. Marinka Zitnik
4. Jure Leskovec
5. Arne Elofsson
6. Weihua Hu
7. Jian Tang
8. Yuxiao Dong

Citations in the corpus

• Limits and potential of combined folding and docking (2021, methodology): “Several interaction databases exist [24,25], and methods, including co-evolution based methods [26], to predict interactions have been developed.”
• 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”
• Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020, methodology): “Nodes represent proteins, and edges indicate different types of biologically meaningful associations between proteins, e.g. , physical interactions, co-expression or homology (Szklarczyk et al., 2019; Consortium, 2018).”
• Characterization of SARS-CoV-2 proteins reveals Orf6 pathogenicity, subcellular localization, host interactions and attenuation by Selinexor (2021, positive): “We used STRING database version 11 [39] to identify known interactions among the proteins in the list.”
• Spatiotemporal analysis of human intestinal development at single-cell resolution (2021, neutral): “In order to visualize morphogen pathway interactions and their modules, we downloaded high confidence interactions from STRING database (Szklarczyk et al., 2019) (version 11.”
• Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis (2020, neutral): “So far no resource offers both the intercellular communication annotations and the intracellular pathways and transcriptional regulation knowledge.”