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PLoS One. 2015 Jul 6;10(7):e0131656. doi: 10.1371/journal.pone.0131656. eCollection 2015.

iCOSSY: An Online Tool for Context-Specific Subnetwork Discovery from Gene Expression Data.

Author information

1
Department of Computer Science and Engineering, Korea University, Seoul, Korea.
2
Department of Medicine/Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America.
3
Department of Computer Science and Engineering, Korea University, Seoul, Korea; Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Korea.

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.

PMID:
26147457
PMCID:
PMC4492968
DOI:
10.1371/journal.pone.0131656
[Indexed for MEDLINE]
Free PMC Article

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