Automatic detection of exonic splicing enhancers (ESEs) using SVMs

Background: Exonic splicing enhancers (ESEs) activate nearby splice sites and promote the inclusion (vs. exclusion) of exons in which they reside, while being a binding site for SR proteins. To study the impact of ESEs on alternative splicing it would be useful to have a possibility to detect them in exons. Identifying SR protein-binding sites in human DNA sequences by machine learning techniques is a formidable task, since the exon sequences are also constrained by their functional role in coding for proteins. Results: The choice of training examples needed for machine learning approaches is difficult since there are only few exact locations of human ESEs described in the literature which could be considered as positive examples. Additionally, it is unclear which sequences are suitable as negative examples. Therefore, we developed a motif-oriented data-extraction method that extracts exon sequences around experimentally or theoretically determined ESE patterns. Positive examples are ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Britta Mersch, Alexander Gepperth, Sándor Suhai, Agnes Hotz-Wagenblatt
• Keywords: Activate Nearby Splice, BMCBI 2008, Exons, Motif-oriented Data-extraction Method