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Download Abstract - Anil Jegga - Cincinnati Children`s Hospital
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Identification of Compositionally Similar Cis-element Clusters in Coordinately Regulated Genes Anil G Jegga, Ashima Gupta, Andrew T Pinski, James W Carman, Bruce J Aronow Cincinnati Children’s Hospital Medical Center, Cincinnati, OH-45229 The combinatorial interaction of sequence specific trans-acting factors with localized genomic cis-elements is the principal underlying mechanism for regulating tissue specific and developmental gene expression. Recent computational approaches have addressed the problem of identification of cis-regulatory regions and cis regulatory elements in single genes or in phylogenetically conserved gene ortholog pairs. However, a singular efficient method to decipher the underlying transcriptional machinery in functionally related or co-expressed higher eukaryotic genes is still elusive. We have explored the extension of comparative genomics approach to tackle this problem. Starting with an earlier developed method for identification of cis-clusters in phylogenetic footprints (http://trafac.chmcc.org), we extended the query to identify compositionally similar cis regulatory element clusters that occur in groups of co-regulated genes within each of their ortholog-pair evolutionarily conserved cis-regulatory regions (“peak analyzer”). These computationally predicted cis-clusters, which we call as cismols, could serve as valuable probes for genome wide identification of regulatory regions. We have tested series of functionally related or co-expressed ortholog pairs of promoters and genes using known regulatory regions as training sets and microarray array profile based co-regulated genes as test sets in the central nervous system, liver, and immuno-hematologic systems. Our results suggest that the combinatorial approach is broadly capable of identifying known regulatory regions and potential regulatory regions that contain cis-elements for known compartment-specific trans-acting factors. Improved integration and refinement of these algorithms should allow for the development of efficient methods to identify the regulatory switches and put forth hypotheses amenable for experimental validation.