SiGPAT 0.1 – Finding significant Expression Patterns of Gene Set

SiGPAT 0.1

:: DESCRIPTION

SiGPAT is a useful tool based on gene set analysis for microarray data. It can find significant expression patterns of gene sets by pri-defined biological knowledge. To be unique to other tools, SiGPAT assignes two statistics for each gene sets and classifies expression patterns of gene sets form two dimension distribution of set-level statistics. The tool was evaluated with better performance than current tools such as GSEA and SAM-GS

::DEVELOPER

Zuguang Gu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R package

:: DOWNLOAD

  SiGPAT

:: MORE INFORMATION

GSCA 2.0 – Gene Set Context Analysis

GSCA 2.0

:: DESCRIPTION

GSCA is an open source software package to transform massive amounts of Publicly available gene Expression Data (PED) into a tool for making new discoveries.

::DEVELOPER

Zhicheng Ji

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R

:: DOWNLOAD

 GSCA

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2016 Jan 8;44(1):e8. doi: 10.1093/nar/gkv873. Epub 2015 Sep 8.
Turning publicly available gene expression data into discoveries using gene set context analysis.
Ji Z, Vokes SA, Dang CV, Ji H

Moksiskaan 2.04 – Translate Gene Sets to Networks

Moksiskaan 2.04

:: DESCRIPTION

Moksiskaan is a generic database that can be used to integrate information about the connections between genes, proteins, pathways, drugs, and other biological entities. Moksiskaan provides various pathway components for Anduril and may be used to extends its capabilities.

::DEVELOPER

Hautaniemi Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 Moksiskaan

:: MORE INFORMATION

Citation

Marko Laakso and Sampsa Hautaniemi
Integrative platform to translate gene sets to networks
Bioinformatics (2010) 26 (14): 1802-1803.

MGSA 1.39.0 – Model-based Gene Set Analysis

MGSA 1.39.0

:: DESCRIPTION

MGSA (model-based gene set analysis) is an effective alternative to classical gene set enrichment analysis.

::DEVELOPER

Gagneur lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ WIndows/ MacOsX
  • R package
  • BioConductor

:: DOWNLOAD

   MGSA

:: MORE INFORMATION

Citation

Model-based gene set analysis for Bioconductor.
Bauer S, Robinson PN, Gagneur J.
Bioinformatics. 2011 Jul 1;27(13):1882-3. doi: 10.1093/bioinformatics/btr296

MEGA-V – Mutation Enrichment Gene set Analysis of Variants

MEGA-V

:: DESCRIPTION

MEGA-V is an open-source R application with a Shiny web interface. It identifies gene sets with a significantly higher number of variants in a cohort of interest (cohort A) as compared to (1) a control cohort (cohort B) or (2) a random distribution generated using Monte Carlo.

::DEVELOPER

the Ciccarelli Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux
  • R

:: DOWNLOAD

MEGA-V

:: MORE INFORMATION

Citation:

Gambardella G, Cereda M, Benedetti L, Ciccarelli FD.
MEGA-V: detection of variant gene sets in patient cohorts.
Bioinformatics. 2017 Apr 15;33(8):1248-1249. doi: 10.1093/bioinformatics/btw809. PMID: 28003259; PMCID: PMC5408849.

StemChecker – Discover and Explore Stemness Signatures in Gene Set

StemChecker

:: DESCRIPTION

StemChecker is a web-server that enables researchers to rapidly check whether a given list of genes can be associated with stemness.

::DEVELOPER

Systems Biology and Bioinformatics Laboratory @ University of Algarve

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

StemChecker: a web-based tool to discover and explore stemness signatures in gene sets.
Pinto JP, Kalathur RK, Oliveira DV, Barata T, Machado RS, Machado S, Pacheco-Leyva I, Duarte I, Futschik ME.
Nucleic Acids Res. 2015 May 24. pii: gkv529

Broad-Enrich 2.14.0 – Gene Set Enrichment Testing for Sets of Broad Genomic Regions

Broad-Enrich 2.14.0

:: DESCRIPTION

Broad-Enrich tests sets of broad genomic regions (e.g., from ChIP-seq data for histone modifications or copy number variations) for enriched biological pathways, Gene Ontology terms, or other gene sets.

::DEVELOPER

The Sartor Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows / MacOsX
  • R

:: DOWNLOAD

 Broad-Enrich

:: MORE INFORMATION

Citation

Broad-Enrich: functional interpretation of large sets of broad genomic regions.
Cavalcante RG, Lee C, Welch RP, Patil S, Weymouth T, Scott LJ, Sartor MA.
Bioinformatics. 2014 Sep 1;30(17):i393-i400. doi: 10.1093/bioinformatics/btu444.

ChIP-Enrich 2.14.0 – Gene Set Enrichment Testing for ChIP-seq data

ChIP-Enrich 2.14.0

:: DESCRIPTION

ChIP-Enrich tests ChIP-seq peak data for enrichment of biological pathways, Gene Ontology terms, and other types of gene sets

::DEVELOPER

The Sartor Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R package

:: DOWNLOAD

 ChIP-Enrich

:: MORE INFORMATION

Citation

ChIP-Enrich: gene set enrichment testing for ChIP-seq data.
Welch RP, Lee C, Imbriano PM, Patil S, Weymouth TE, Smith RA, Scott LJ, Sartor MA.
Nucleic Acids Res. 2014 May 30. pii: gku463.

 

PAEA – Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool

PAEA

:: DESCRIPTION

PAEA (Principal Angle Enrichment Analysis) is a new multivariate approach to gene-set enrichment which uses the geometrical concept of the principal angle to quantify gene-set enrichment.

::DEVELOPER

Ma’ayan Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Proceedings (IEEE Int Conf Bioinformatics Biomed). 2015 Nov;2015:256-262.
Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.
Clark NR, Szymkiewicz M, Wang Z, Monteiro CD, Jones MR1, Ma’ayan A.

TopoGSA – Network Topological Gene Set Analysis

TopoGSA

:: DESCRIPTION

TopoGSA (Topology-based Gene Set Analysis) computes and visualise the topological properties of a set of genes/proteins mapped onto a molecular interaction network. Different topological characteristics, such as the centrality of nodes in the network or their tendency to form clusters, are computed and compared against those of known cellular pathways and processes (KEGG, BioCarta, GO, etc.).

::DEVELOPER

TopoGSA team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Bioinformatics. 2010 May 1;26(9):1271-2. doi: 10.1093/bioinformatics/btq131. Epub 2010 Mar 24.
TopoGSA: network topological gene set analysis.
Glaab E, Baudot A, Krasnogor N, Valencia A.