Tag: GSEA

GO2MSIG 20131106 – GO based GSEA Gene Set Generator

GO2MSIG 20131106

:: DESCRIPTION

GO2MSIG generates collections of gene sets in MSigDB format based on the Gene Ontology (GO) project hierarchy and gene association data, for use with the Gene Set Enrichment Analysis (GSEA) implementation available at the Broad Institute. This enables rapid creation of gene set collections for multiple species.

::DEVELOPER

Justin Powell (jacp10 at bioinformatics.org)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / windows/ MacOsX
  • Perl
  • Web Server
  • MySQL

:: DOWNLOAD

 GO2MSIG

:: MORE INFORMATION

GSEA 4.1.0 – Gene Set Enrichment Analysis

GSEA 4.1.0

:: DESCRIPTION

GSEA (Gene Set Enrichment Analysis) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes).

::DEVELOPER

GSEA team

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java

:: DOWNLOAD

 GSEA

:: MORE INFORMATION

Citation:

Subramanian, Tamayo, et al. (2005,)
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
PNAS 102, 15545-15550

SeqGSEA 1.33.0 – Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing

SeqGSEA 1.33.0

:: DESCRIPTION

SeqGSEA generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing.

::DEVELOPER

Xi Wang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Mac OsX / Windows
  • R package
  • BioConductor

:: DOWNLOAD

 SeqGSEA

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Mar 6. [Epub ahead of print]
SeqGSEA: a Bioconductor package for gene set enrichment analysis of RNA-Seq data integrating differential expression and splicing.
Wang X1, Cairns MJ.

BMC Bioinformatics. 2013;14 Suppl 5:S16. doi: 10.1186/1471-2105-14-S5-S16. Epub 2013 Apr 10.
Gene set enrichment analysis of RNA-Seq data: integrating differential expression and splicing.
Wang X, Cairns MJ.