An integrative network approach to map the transcriptome to the phenome

Michael R. Mehan, Juan Nunez-Iglesias, Mrinal Kalakrishnan, Michael S. Waterman, Xianghong Jasmine Zhou

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

Abstract

Although many studies have been successful in the discovery of cooperating groups of genes, mapping these groups to phenotypes has proved a much more challenging task. In this article, we present the first genome-wide mapping of gene coexpression modules onto the phenome. We annotated coexpression networks from 136 microarray datasets with phenotypes from the Unified Medical Language System (UMLS). We then designed an efficient graph-based simulated annealing approach to identify coexpression modules frequently and specifically occurring in datasets related to individual phenotypes. By requiring phenotype-specific recurrence, we ensure the robustness of our findings. We discovered 118,772 modules specific to 42 phenotypes, and developed validation tests combining Gene Ontology, GeneRIF and UMLS. Our method is generally applicable to any kind of abundant network data with defined phenotype association, and thus paves the way for genome-wide, gene network-phenotype maps.

Original languageEnglish
Pages (from-to)1023-1034
Number of pages12
JournalJournal of Computational Biology
Volume16
Issue number8
DOIs
Publication statusPublished - 1 Aug 2009
Externally publishedYes

Keywords

  • Algorithms
  • Computational molecular biology
  • Dynamic programming

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