Article

STRING: Known and predicted protein-protein associations, integrated and transferred across organisms

Radboud University Nijmegen, Nymegen, Gelderland, Netherlands
Nucleic Acids Research (Impact Factor: 9.11). 02/2005; 33(Database issue):D433-7. DOI: 10.1093/nar/gki005
Source: PubMed

ABSTRACT

A full description of a protein's function requires knowledge of all partner proteins with which it specifically associates.
From a functional perspective, ‘association’ can mean direct physical binding, but can also mean indirect interaction such
as participation in the same metabolic pathway or cellular process. Currently, information about protein association is scattered
over a wide variety of resources and model organisms. STRING aims to simplify access to this information by providing a comprehensive,
yet quality-controlled collection of protein–protein associations for a large number of organisms. The associations are derived
from high-throughput experimental data, from the mining of databases and literature, and from predictions based on genomic
context analysis. STRING integrates and ranks these associations by benchmarking them against a common reference set, and
presents evidence in a consistent and intuitive web interface. Importantly, the associations are extended beyond the organism
in which they were originally described, by automatic transfer to orthologous protein pairs in other organisms, where applicable.
STRING currently holds 730 000 proteins in 180 fully sequenced organisms, and is available at http://string.embl.de/.

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    • "Seven putative orthologous sequences were retrieved in R. irregularis; Uniprot accession numbers U9U1X0, U9UEK3, U9UFF5, U9TTI3, U9UF16, U9UI83 and U9UJR1, respectively. Furthermore, clusters of orthologous genes (COGs), gathering sequences from numerous organisms, were determined using STRING version 9.05[63]for each protein candidate. The resulting COGs were aligned using COBALT version 2.01[64]. "
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    • "Thus we use the hadoop framework for the extraction of chemicals from the abstracts. Currently a top-to-bottom approach is used in most of the text mining applications, which means parsing important words from vast amounts of text, and then spread downwards through databases or software such as STRING[7], STITCH[8] [9], Biotextquest[10], Bioalma[11], Whatizit[12] [13] or iHop[14].Other tools, reviewed in[11], try to analyze literature using semantic concepts, clustering algorithms or ranking algorithms and most generic attempts go one step further by using semantic web concepts to further enrich and annotate terms found in literature[1]. In the proposed system the user will just need to provide the name of the disease of interest. "

    Preview · Article · Dec 2015
    • "The default cutoff for confident interactions is 0.4 [11] [17]. Chemical– protein interactions were transferred among species based on the sequence similarity of the proteins [17]. The chemical–protein interactions have also been successfully utilized to predict nongenotoxic hepatocarcinogenicity [18] [19]. "
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