ROMA – Calculation of Gene Set Activities from Oomics data

ROMA

:: DESCRIPTION

ROMA (Representation and quantification Of Module Activities) is a software package written in Java for the quantification and representation of biological module activity using gene expression or other omics data.

::DEVELOPER

Computational Systems Biology of Cancer group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows
Java

:: DOWNLOAD

ROMA

:: MORE INFORMATION

Citation:

Martignetti L, Calzone L, Bonnet E, Barillot E, Zinovyev A.
ROMA: Representation and Quantification of Module Activity from Target Expression Data.
Front Genet. 2016 Feb 19;7:18. doi: 10.3389/fgene.2016.00018. PMID: 26925094; PMCID: PMC4760130.

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

GSCA / GSCALite – Gene Set Cancer Analysis

GSCA / GSCALite

:: DESCRIPTION

GSCA is an integrated database for genomic and immunogenomic gene set cancer analysis.

GSCALite is a web-based analysis platform for gene set cancer analysis. The alterations on DNA or RNA of cancer related genes may be contribute to the cancer initiation, progress, diagnosis, prognosis, therapy. As the cancer genomics big data available, it is very useful and urgent to provide a platform for gene set analysis in cancer.

::DEVELOPER

An-Yuan Guo’s Bioinformatics Laboratory

:: SCREENSHOTS

N/A

::REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Liu CJ, Hu FF, Xia MX, Han L, Zhang Q, Guo AY.
GSCALite: a web server for gene set cancer analysis.
Bioinformatics. 2018 Nov 1;34(21):3771-3772. doi: 10.1093/bioinformatics/bty411. PMID: 29790900.

ErmineJ 3.2 – Analysis of Gene sets in Expression Microarray data

ErmineJ 3.2

:: DESCRIPTION

ErmineJ performs analyses of gene sets in expression microarray data. A typical goal is to determine whether particular biological pathways are “doing something interesting” in the data. The software is designed to be used by biologists with little or no informatics background.

::DEVELOPER

Pavlidis lab

:: SCREENSHOTS

:: REQUIREMENTS

Windows / Linux / Mac OsX
java

:: DOWNLOAD

ErmineJ

:: MORE INFORMATION

Citation

Lee H.K., Braynen W., Keshav K. and Pavlidis P. (2005)
ErmineJ: Tool for functional analysis of gene expression data sets.
BMC Bioinformatics 6:269.

PEGS 0.6.4 – Peak-set Enrichment of Gene-Sets

PEGS 0.6.4

:: DESCRIPTION

PEGS is a Python bioinformatics utility for calculating enrichments of gene clusters at different genomic distances from peaks.

::DEVELOPER

Bioinformatics Core Facility in the Faculty of Biology Medicine and Health at the University of Manchester

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / Mac OsX
Python

:: DOWNLOAD

PEGS

:: MORE INFORMATION

Citation

Briggs P, Hunter AL, Yang SH, Sharrocks AD, Iqbal M.
PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals.
F1000Res. 2021 Jul 15;10:570. doi: 10.12688/f1000research.53926.2. PMID: 34504687; PMCID: PMC8406447.

MAGENTA 2.4 – Meta-Analysis Gene-set Enrichment of variaNT Associations

MAGENTA 2.4

:: DESCRIPTION

MAGENTA (Meta-Analysis Gene-set Enrichment of variaNT Associations) is a program that tests whether predefined biological processes or gene sets are enriched for genes associated with a complex disease or trait.

:DEVELOPER

the Broad Institute

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Mac OsX / Windows
Matlab

:: DOWNLOAD

MAGENTA

:: MORE INFORMATION

Citation

Ayellet V. Segrè, DIAGRAM Consortium, MAGIC investigators, Leif Groop, Vamsi K. Mootha, Mark J. Daly, and David Altshuler (2010).
Common Inherited Variation in Mitochondrial Genes is not Enriched for Associations with Type 2 Diabetes or Related Glycemic Traits.
PLoS Genetics Aug 12;6(8). pii: e1001058.

GSCA / cGSCA – Gene Set Control Analysis

GSCA / cGSCA

:: DESCRIPTION

GSCA is a new computational framework for linking gene sets with transcriptional control.

C-GSCA tool performs a hierarchical clustering of over-represented samples found by GSCA.

::DEVELOPER

GSCA team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Server

:: DOWNLOAD

:: MORE INFORMATION

Citation

Exp Hematol. 2013 Apr;41(4):354-66.e14. doi: 10.1016/j.exphem.2012.11.008. Epub 2012 Dec 4.
Gene set control analysis predicts hematopoietic control mechanisms from genome-wide transcription factor binding data.
Joshi A1, Hannah R, Diamanti E, Göttgens B.

DBGSA 1.2 – Distance-based Gene Set Functional Enrichment Analysis

DBGSA 1.2

:: DESCRIPTION

DBGSA is a novel distance-based gene set enrichment analysis method.

::DEVELOPER

DBGSA team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ Windows/ MacOsX
R

:: DOWNLOAD

DBGSA

:: MORE INFORMATION

Citation

J Hum Genet. 2012 Oct;57(10):642-53. doi: 10.1038/jhg.2012.86. Epub 2012 Jul 12.
DBGSA: a novel method of distance-based gene set analysis.
Li J, Wang L, Xu L, Zhang R, Huang M, Wang K, Xu J, Lv H, Shang Z, Zhang M, Jiang Y, Guo M, Li X.

Confero 0.1 – Integrated Contrast and Gene Set Platform

Confero 0.1

:: DESCRIPTION

Confero is a contrast data and gene set platform for downstream analysis and biological interpretation of omics data.

::DEVELOPER

Confero team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / MacOsX
Java
Perl
MySQL Server

:: DOWNLOAD

Confero

:: MORE INFORMATION

Citaton

BMC Genomics. 2013 Jul 29;14:514. doi: 10.1186/1471-2164-14-514.
Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data.
Hermida L1, Poussin C, Stadler MB, Gubian S, Sewer A, Gaidatzis D, Hotz HR, Martin F, Belcastro V, Cano S, Peitsch MC, Hoeng J.

npGSEA 1.28.0 – Permutation Approximation methods for Gene Set Enrichment Analysis

npGSEA 1.28.0

:: DESCRIPTION

npGSEA (non-permutation GSEA) calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results.

::DEVELOPER

Jessica Larson <larson.jess at gmail.com> and Art Owen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/ MacOsX
R
BioConductor

:: DOWNLOAD

npGSEA

:: MORE INFORMATION

Citation

Moment based gene set tests.
Larson JL, Owen AB.
BMC Bioinformatics. 2015 Apr 28;16(1):132.