ICOSSY

An online tool for context-specific subnetwork discovery from gene expression data

Ashis Saha, Minji Jeon, Aik-Choon Tan, Jaewoo Kang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Pathway analyses help reveal underlying molecular mechanisms of complex biological phenotypes. Biologists tend to perform multiple pathway analyses on the same dataset, as there is no single answer. It is often inefficient for them to implement and/or install all the algorithms by themselves. Online tools can help the community in this regard. Here we present an online gene expression analytical tool called iCOSSY which implements a novel pathway-based COntext-specific Subnetwork discoverY (COSSY) algorithm. iCOSSY also includes a few modifications of COSSY to increase its reliability and interpretability. Users can upload their gene expression datasets, and discover important subnetworks of closely interacting molecules to differentiate between two phenotypes (context). They can also interactively visualize the resulting subnetworks. iCOSSY is a web server that finds subnetworks that are differentially expressed in two phenotypes. Users can visualize the subnetworks to understand the biology of the difference.

Original languageEnglish
Article numbere0131656
JournalPLoS One
Volume10
Issue number7
DOIs
Publication statusPublished - 2015 Jul 6

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Gene expression
Phenotype
Gene Expression
phenotype
gene expression
Servers
Molecules
biologists
Biological Sciences
Datasets

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

ICOSSY : An online tool for context-specific subnetwork discovery from gene expression data. / Saha, Ashis; Jeon, Minji; Tan, Aik-Choon; Kang, Jaewoo.

In: PLoS One, Vol. 10, No. 7, e0131656, 06.07.2015.

Research output: Contribution to journalArticle

Saha, Ashis ; Jeon, Minji ; Tan, Aik-Choon ; Kang, Jaewoo. / ICOSSY : An online tool for context-specific subnetwork discovery from gene expression data. In: PLoS One. 2015 ; Vol. 10, No. 7.
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