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
Conference paper

DOI: 10.1007/978-3-540-78839-3_20

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)
Cite this paper as:
Mehan M.R., Nunez-Iglesias J., Kalakrishnan M., Waterman M.S., Zhou X.J. (2008) An Integrative Network Approach to Map the Transcriptome to the Phenome. In: Vingron M., Wong L. (eds) Research in Computational Molecular Biology. RECOMB 2008. Lecture Notes in Computer Science, vol 4955. Springer, Berlin, Heidelberg

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 paper, 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 9,183 modules specific to 47 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Michael R. Mehan
    • 1
  • Juan Nunez-Iglesias
    • 1
  • Mrinal Kalakrishnan
    • 1
  • Michael S. Waterman
    • 1
  • Xianghong Jasmine Zhou
    • 1
  1. 1.Program in Computational Biology, Department of Biological SciencesUniversity of Southern CaliforniaLos AngelesUSA

Personalised recommendations