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Journal of Biomedicine and Biotechnology
Volume 2005, Issue 2, Pages 172-180
Research article

Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

1Department of Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
2Department of Anatomy and Neurobiology, The University of Tennessee, Memphis, TN 38163, USA
3Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
4Harvard Center for Neurodegeneration & Repair and Brigham and Women's Hospital, Harvard Medical School, Harvard University, Boston, MA 02115, USA

Received 24 June 2004; Revised 12 September 2004; Accepted 14 September 2004

Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.