Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks. Issue 2 (30th June 2005)

• Record Type: Journal Article
• Authors: Baldwin, Nicole E.; Chesler, Elissa J.; Kirov, Stefan; Langston, Michael A.; Snoddy, Jay R.; Williams, Robert W.; Zhang, Bing
• Is Part Of: Journal of biomedicine and biotechnology. Volume 2005:Issue 2(2005)
• Page Start: 172
• Page End: 180
• Publication Date: 2005-06-30
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Medicine; Biology; Biotechnology; Médecine; Biologie; Biotechnologie; Biology; Biotechnology; Medicine; Biotechnology; Biomedicine; Electronic journals; Periodical; Periodicals; Electronic journals; 610
• DOI: 10.1155/JBB.2005.172
• Languages: English
• ISSNs: 1110-7243

Abstract

Abstract : Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, 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.