HyperModules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery

Alvin Leung; Gary D Bader; Jüri Reimand

doi:10.1093/bioinformatics/btu172

HyperModules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery

Bioinformatics. 2014 Aug 1;30(15):2230-2. doi: 10.1093/bioinformatics/btu172. Epub 2014 Apr 8.

Authors

Alvin Leung¹, Gary D Bader¹, Jüri Reimand¹

Affiliation

¹ The Donnelly Centre, University of Toronto, 160 College Street, M5S 3E1 Toronto, Ontario, Canada.

Abstract

Summary: Correlating disease mutations with clinical and phenotypic information such as drug response or patient survival is an important goal of personalized cancer genomics and a first step in biomarker discovery. HyperModules is a network search algorithm that finds frequently mutated gene modules with significant clinical or phenotypic signatures from biomolecular interaction networks.

Availability and implementation: HyperModules is available in Cytoscape App Store and as a command line tool at www.baderlab.org/Sofware/HyperModules.

Contact: Juri.Reimand@utoronto.ca or Gary.Bader@utoronto.ca

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Algorithms*
Biomarkers, Tumor / genetics
Computer Graphics
Gene Regulatory Networks
Genomics
Humans
Internet
Mutation*
Neoplasms / genetics*
Neoplasms / metabolism*
Phenotype*
Protein Interaction Maps
Software*
Systems Biology / methods*

Substances

Biomarkers, Tumor

Grants and funding

P41 GM103504/GM/NIGMS NIH HHS/United States