Abstract

Whole genome shotgun sequencing strategies generate sequence data prior to the application of assembly methodologies that result in contiguous sequence. Sequence reads can be employed to indicate regions of conservation between closely related species for which only one genome has been assembled. Consequently, by using pairwise sequence alignments methods it is possible to identify novel, non-repetitive, conserved segments in non-coding sequence that exist between the assembled human genome and mouse whole genome shotgun sequencing fragments. Conserved non-coding regions identify potentially functional DNA that could be involved in transcriptional regulation. Local sequence alignment methods were applied employing mouse fragments and the assembled human genome. In addition, transcription factor binding sites were detected by aligning their corresponding positional weight matrices to the sequence regions. These methods were applied to a set of transcripts corresponding to 502 genes associated with a variety of different human diseases taken from the Online Mendelian Inheritance in Man database. Using statistical arguments we have shown that conserved non-coding segments contain an enrichment of transcription factor binding sites when compared to the sequence background in which the conserved segments are located. This enrichment of binding sites was not observed in coding sequence. Conserved non-coding segments are not extensively repeated in the genome and therefore their identification provides a rapid means of finding genes with related conserved regions, and consequently potentially related regulatory mechanism. Conserved segments in upstream regions are found to contain binding sites that are co-localized in a manner consistent with experimentally known transcription factor pairwise co-occurrences and afford the identification of novel co-occurring Transcription Factor (TF) pairs. This study provides a methodology and more evidence to suggest that conserved non-coding regions are biologically significant since they contain a statistical enrichment of regulatory signals and pairs of signals that enable the construction of regulatory models for human genes. samuel.levy@celera.com.

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