Molecular signature database (MSigDB) 3.0

Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Bioinformatics (Impact Factor: 4.98). 06/2011; 27(12):1739-40. DOI: 10.1093/bioinformatics/btr260
Source: PubMed

ABSTRACT Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale genomic data. The Molecular Signatures Database (MSigDB) is one of the most widely used repositories of such sets.
We report the availability of a new version of the database, MSigDB 3.0, with over 6700 gene sets, a complete revision of the collection of canonical pathways and experimental signatures from publications, enhanced annotations and upgrades to the web site.
MSigDB is freely available for non-commercial use at

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Available from: Pablo Tamayo, Sep 26, 2015
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    • "The application of integrated approaches such as Galahad[46], Expression2Kinases[47], and CellNOptR[48] which uses both genomic (or proteomic) profiles and protein-protein interaction (PPI) data, is also gaining attention. In its essence, all these prioritization processes involve comparing a molecular profiles (e.g., protein-target interaction or gene expression response) associated with a chemical with a database of disease or pathway signatures such as MSigDB[49], GeneSigDB[50], and EnrichR[51]. The comparisons can be performed using a variety of association measures [39] [52], but have limitations such as ignoring the topology of the regulatory networks and the relative rank of the strength of the association. "
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    • "GO annotation file is downloaded from [22] on Nov. 23rd, 2014. Pathway annotation from MSigDB is downloaded from GSEA [23]. Biological process GO terms and MSigDB pathways are tested for enrichment using Fisher's test() in R. The significance of threshold was set at 0.01. "
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    08/2015; 2015:685303. DOI:10.1155/2015/685303
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    • "Molecular signature database (MSigDB) (Liberzon et al., 2011) was used to obtain the biological pathways for the initially screened dataset (Ponzoni et al., 2014). We chose canonical pathways which include pathways from BioCarta, KEGG and Reactome databases. "
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