Genomic and Functional Analyses of Conserved Noncoding R

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The major aims of the Genomic Functional Analysis Section are to identify cis-and trans-acting functional elements in vertebrate genomes using comparative analyses. The approach utilizes multi-species, whole-genome sequence alignments to identify conserved noncoding regions, examine patterns of sequence mutation within those elements, and test for changes in expression levels that correspond to changes in the sequences acquired through speciation events. Functional elements under consideration include any elements that comprise collections of transcription factor binding sites such as enhancers, silencers, promoters, and microRNA regulatory regions, elements that are exceptionally conserved, such as ultra-conserved elements, or those that contain uncharacterized structural features such as origins of replication. In addition to experimental verification of functional elements, we perform computational analyses to characterize functional elements in the genome. For instance, evolutionary analyses of promoters are being used to study selection on TATA motifs and CpG islands in multispecies sequence alignments of human/chimp/dog/mouse/chicken. By utilizing datasets in which we have confidence in the transcription start site, we are better able to document the occurrence of TATA boxes and assess evolutionary histories of them. Related computational approaches are being used to study mechanisms of regulated microRNA expression. Utilizing the database of ESTs, we identified bidirectional promoters in the human genome that were not previously characterized and showed a statistically significant association with genes implicated in breast and ovarian cancers. We are now testing tumor samples to check for epigenetic changes associated with these promoters in tumors. Methods to archive functional data are being explored in a project to develop a computational database housing microarray data from the ENCODE consortium. These important data contain information on transcription factor binding sites identified through ChIP-chip assays. The data are used as positive controls for the aforementioned predictions of genomic regulatory regions and for the identification of regions that are targets for future experimental analyses.