skip to main content


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

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.
 [1] ;  [2] ;  [3] ;  [4] ;  [3] ;  [2] ;  [3]
  1. Department of Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
  2. Department of Anatomy and Neurobiology, The University of Tennessee, Memphis, TN 38163, USA
  3. Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
  4. Department of Computer Science, The University of Tennessee, Knoxville, TN 37996, USA, Harvard Center for Neurodegeneration & Repair and Brigham and Women's Hospital, Harvard Medical School, Harvard University, Boston, MA 02115, USA
Publication Date:
Grant/Contract Number:
DE–AC05–00OR33735; DE–AC05–4000029264
Published Article
Journal Name:
Journal of Biomedicine and Biotechnology
Additional Journal Information:
Journal Volume: 2005; Journal Issue: 2; Related Information: CHORUS Timestamp: 2016-08-23 03:44:53; Journal ID: ISSN 1110-7243
Hindawi Publishing Corporation
Sponsoring Org:
Country of Publication:
Country unknown/Code not available
OSTI Identifier: