GeneSigDB: A Manually Curated Database and Resource for Analysis of Gene Expression Signatures

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GeneSigDB: A Manually Curated Database and Resource for Analysis of Gene Expression Signatures

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Title: GeneSigDB: A Manually Curated Database and Resource for Analysis of Gene Expression Signatures
Author: Culhane, Aedín C.; Schröder, Markus S.; Sultana, Razvan; Picard, Shaita C.; Martinelli, Enzo N.; Kelly, Caroline; Kapushesky, Misha; St Pierre, Anne-Alyssa; Flahive, William; Gusenleitner, Daniel; Papenhausen, Gerald; O'Connor, Niall; Correll, Mick; Haibe-Kains, Benjamin; Picard, Kermshlise Cathchusca; Quackenbush, John

Note: Order does not necessarily reflect citation order of authors.

Citation: Culhane, Aedín C., Markus S. Schröder, Razvan Sultana, Shaita C. Picard, Enzo N. Martinelli, Caroline Kelly, Benjamin Haibe-Kains, et al. 2012. GeneSigDB: A manually curated database and resource for analysis of gene expression signatures. Nucleic Acids Research 40(D1): D1060-D1066.
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Abstract: GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a ‘basket’ feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org.
Published Version: doi:10.1093/nar/gkr901
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245038/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:8604804
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