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STRING v10: protein-protein interaction networks, integrated over the tree of life.

Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C - Nucleic Acids Res. (2014)

Bottom Line: However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness.For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution.Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland.

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Access to STRING from R/Bioconductor. Left: example session describing how to initialize a human protein network from the STRING database backend, and how to map a set of gene names against it. A subset of the proteins is then plotted as a STRING network (right), complete with auxiliary numerical payload-information highlighting some nodes of interest (red color halos).
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Figure 3: Access to STRING from R/Bioconductor. Left: example session describing how to initialize a human protein network from the STRING database backend, and how to map a set of gene names against it. A subset of the proteins is then plotted as a STRING network (right), complete with auxiliary numerical payload-information highlighting some nodes of interest (red color halos).

Mentions: Apart from directly browsing and searching the website, data access in STRING is possible also via a REST-based API (application programing interface) and via wholesale data download. With version 10.0, we have introduced a further option: direct access from the R programming environment, following the Bioconductor standard (39). The corresponding package is named STRINGdb (Figure 3), and can be downloaded from the Bioconductor repository (http://www.bioconductor.org/packages/release/bioc/html/STRINGdb.html). The package interacts with the STRING server via the REST API and via additional, dedicated web services. To optimize the speed of subsequent accesses, the entire interaction network and associated data for a given organism are downloaded from the server and cached locally in the R environment, whenever possible. The package is built around the iGraph framework (40), which handles the complexity of the network data structures and provides fast query/analysis functions. Once a network is loaded/cached into an iGraph object, high-level functions facilitate the most common user tasks, such as mapping protein names onto their corresponding STRING identifiers, retrieving the neighbors of a protein of interest, retrieving PubMed IDs for publications that support a given interaction, finding clusters of proteins in the network and generating stable links back to the STRING website.


STRING v10: protein-protein interaction networks, integrated over the tree of life.

Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C - Nucleic Acids Res. (2014)

Access to STRING from R/Bioconductor. Left: example session describing how to initialize a human protein network from the STRING database backend, and how to map a set of gene names against it. A subset of the proteins is then plotted as a STRING network (right), complete with auxiliary numerical payload-information highlighting some nodes of interest (red color halos).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4383874&req=5

Figure 3: Access to STRING from R/Bioconductor. Left: example session describing how to initialize a human protein network from the STRING database backend, and how to map a set of gene names against it. A subset of the proteins is then plotted as a STRING network (right), complete with auxiliary numerical payload-information highlighting some nodes of interest (red color halos).
Mentions: Apart from directly browsing and searching the website, data access in STRING is possible also via a REST-based API (application programing interface) and via wholesale data download. With version 10.0, we have introduced a further option: direct access from the R programming environment, following the Bioconductor standard (39). The corresponding package is named STRINGdb (Figure 3), and can be downloaded from the Bioconductor repository (http://www.bioconductor.org/packages/release/bioc/html/STRINGdb.html). The package interacts with the STRING server via the REST API and via additional, dedicated web services. To optimize the speed of subsequent accesses, the entire interaction network and associated data for a given organism are downloaded from the server and cached locally in the R environment, whenever possible. The package is built around the iGraph framework (40), which handles the complexity of the network data structures and provides fast query/analysis functions. Once a network is loaded/cached into an iGraph object, high-level functions facilitate the most common user tasks, such as mapping protein names onto their corresponding STRING identifiers, retrieving the neighbors of a protein of interest, retrieving PubMed IDs for publications that support a given interaction, finding clusters of proteins in the network and generating stable links back to the STRING website.

Bottom Line: However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness.For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution.Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland.

Show MeSH
Related in: MedlinePlus