:: DESCRIPTION
GS2D(Gene set to diseases) computes disease enrichment analysis on gene sets using biomedical literature data.
::DEVELOPER
Computational Biology and Data Mining (CBDM) Group
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Andrade-Navarro MA, Fontaine JF (2016).
Gene Set to Diseases (GS2D): Disease Enrichment Analysis on Human Gene Sets with Literature Data.
Genomics and Computational Biology, 2(1): e33.
:: DESCRIPTION
mBISON (Analysis on miRNA binding site over-representation) finds over-represented miRNA targets in a gene list you provide.
::DEVELOPER
Computational Biology and Data Mining (CBDM) Group
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
BMC Res Notes. 2015 Apr 16;8:157. doi: 10.1186/s13104-015-1118-8.
mBISON: Finding miRNA target over-representation in gene lists from ChIP-sequencing data.
Gebhardt ML, Mer AS, Andrade-Navarro MA.
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:: DESCRIPTION
NGSEA (network-based GSEA) measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network.
::DEVELOPER
Network Biomedicine Laboratory at Yonsei University, Korea
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Mol Cells. 2019 Aug 31;42(8):579-588. doi: 10.14348/molcells.2019.0065.
NGSEA: Network-Based Gene Set Enrichment Analysis for Interpreting Gene Expression Phenotypes with Functional Gene Sets.
Han H, Lee S, Lee I
:: DESCRIPTION
CLEAN is a computational framework for analytically and visually integrating knowledge-based functional categories with the cluster analysis of genomics data. The framework is based on the simple, conceptually appealing, and biologically interpretable gene-specific functional coherence score (CLEAN score). The score is derived by correlating the clustering structure as a whole with functional categories of interest.
:: DEVELOPER
Laboratory for Statistical Genomics, Univ. Cincinnati
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Freudenberg JM, Joshi VK, Hu Z, Medvedovic M.
CLEAN: CLustering Enrichment ANalysis.
BMC Bioinformatics (2009) 10:234. Pubmed. Poster presented at OCCBIO 2008.
:: DESCRIPTION
MSEA is a web-based tool to help identify and interpret patterns of metabolite concentration changes in a biologically meaningful context for human and mammalian metabolomic studies.
::DEVELOPER
the Wishart Research Group, University of Alberta
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W71-7. doi: 10.1093/nar/gkq329. Epub 2010 May 10.
MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data.
Xia J, Wishart DS.
:: DESCRIPTION
The i-GSEA4GWAS (improved GSEA for GWAS) web server is a web-based resource for analysis of GWAS data (typically each SNP’s -log(P-value)) to identify pathways/gene sets correlated to certain traits by implementing an improved Gene Set Enrichment Analysis (i-GSEA) approach. i-GSEA4GWAS aims to establish an open platform to help further interpret the GWAS data to provide new insights in complex disease study, especially in complementation to the standard single variant/gene based analysis.
::DEVELOPER
Bioinformatics Lab, Institute of Psychology, Chinese Academy of Sciences
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
i-GSEA4GWAS: a web server for identification of pathways/gene sets associated with traits by applying an improved gene set enrichment analysis to genome-wide association study.
Kunlin Zhang; Sijia Cui; Suhua Chang; Liuyan Zhang; Jing Wang
Nucleic Acids Research 2010; doi: 10.1093/nar/gkq324
:: DESCRIPTION
GeneTerm Linker is a new algorithm for functional annotation of a list of genes that provides a set of functional metagroups in a single output. GeneTerm Linker filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Fontanillo C, Nogales-Cadenas R, Pascual-Montano A, De Las Rivas J (2011)
Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms.
PLoS ONE 6(9): e24289. doi:10.1371/journal.pone.0024289
:: DESCRIPTION
SubGSE is developed for gene set enrichment analysis.The primary objective of this program is to measure the enrichment of differentially expressed or phenotype associated genes in given gene sets. The input for the program includes the gene expression data of several samples with corresponding phenotypic data (categorical or continuous), and several given gene sets. The program will assess the significance of the enrichment of genes, whose gene expression profiles are associated with the phenotypic data, in each given gene set.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Xiting Yan and Fengzhu Sun
Testing gene set enrichment for subset of genes: Sub-GSE
BMC Bioinformatics 2008, 9:362
:: DESCRIPTION
SEAS is a computational tool for pathway enrichment analysis over a given set of genes in a specified organism against pathways (or subsystems) in the SEED database, a popular pathway database for bacteria. SEAS maps a given set of genes in a bacterium to genes currently covered by SEED through gene ID and/or orthology mapping, and then calculates the statistical significance of the enrichment of each relevant SEED pathway by the mapped genes.
::DEVELOPER
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
PLoS One. 2011;6(7):e22556. Epub 2011 Jul 22.
SEAS: a system for SEED-based pathway enrichment analysis.
Mao X, Zhang Y, Xu Y.
