TY - JOUR
T1 - GeneSigDB
T2 - A manually curated database and resource for analysis of gene expression signatures
AU - Culhane, Aedín C.
AU - Schröder, Markus S.
AU - Sultana, Razvan
AU - Picard, Shaita C.
AU - Martinelli, Enzo N.
AU - Kelly, Caroline
AU - Haibe-Kains, Benjamin
AU - Kapushesky, Misha
AU - St Pierre, Anne Alyssa
AU - Flahive, William
AU - Picard, Kermshlise C.
AU - Gusenleitner, Daniel
AU - Papenhausen, Gerald
AU - O'Connor, Niall
AU - Correll, Mick
AU - Quackenbush, John
PY - 2012/1
Y1 - 2012/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84862216273&partnerID=8YFLogxK
U2 - 10.1093/nar/gkr901
DO - 10.1093/nar/gkr901
M3 - Article
C2 - 22110038
AN - SCOPUS:84862216273
SN - 0305-1048
VL - 40
SP - D1060-D1066
JO - Nucleic Acids Research
JF - Nucleic Acids Research
IS - D1
ER -
