Transcribed Enhancer Landscape
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In mammalian cells, transcribed enhancers (TrEn) play important roles in regulation of gene expression and maintenance of expression levels in space and time. Deciphering the genomic code of TrEn is a challenging problem that may improve our current knowledge of enhancers functional roles. This is particularly critical, as several recent studies have linked genomic characteristics of TrEn to their functional role. To date, only a limited number of enhancer sequence characteristics have been investigated, leaving space for exploring the enhancers genomic code in a more systematic way. Here we address this problem, by exploring the discriminative capabilities of several short nucleotide motifs and their combinations.

We developed a novel computational method called TELS, and used it to compile a comprehensive catalogue of the most informative combinations of nucleotide motifs for all known TrEn identified by the FANTOM5 consortium. Our results show that the identified motifs can discriminate successfully enhancers transcribed in one cell-type or tissue from enhancers transcribed in other tissues as well as TrEn from DNA sequences with no enhancer activity.

The proposed work provides an in-silico approach to explore systematically the DNA code of human TrEn and identify distinct and predictive nucleotide signatures.

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