What can we learn from noncoding regions of similarity between genomes?

Background: In addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to assume that these conserved regions are more likely to contain functional elements than less-conserved portions of the genome. Methods: Here we used a motif-oriented machine learning method based on the Relevance Vector Machine algorithm to extract the strongest signal from a set of non-coding conserved sequences. Results: We successfully fitted models to reflect the non-coding sequences, and showed that the results were quite consistent for repeated training runs. Using the learned models to scan genomic sequence, we found that they often made predictions close to the start of annotated genes. We compared this method with other published promoter-prediction systems, and showed that the set of promoters which are detected by this method is substantially similar to that detected by existing methods. Conclusions:...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Thomas A. Down, Tim J. P. Hubbard
• Keywords: BMCBI 2004, Genome, Mouse Genome, Non-coding Sequences