EECS Publication
Computational, Integrative and Comparative Methods for the Elucidation of Genetic Co-Expression Networks
Nicole E. Baldwin, Elissa J. Chesler, Stefan Kirov, Michael A. Langston, Jay R. Snoddy, Robert W. Williams and Bing Zhang
Gene expression microarray data can be used for the assembly of genetic co-expression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculusstrains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively co-regulated genes and their annotation using gene ontology analysis and cis-regulatory element discovery. The causal basis for co-regulation is detected through the use of quantitative trait locus mapping.
Published 2004-09-12 04:00:00 as ut-cs-04-530 (ID:191)