Email updates

Keep up to date with the latest news and content from Biology Direct and BioMed Central.

Open Access Research

Method of predicting Splice Sites based on signal interactions

Alexander Churbanov1*, Igor B Rogozin2, Jitender S Deogun3 and Hesham Ali1

Author Affiliations

1 Department of Computer Science, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE68182-0116, USA

2 NCBI/NLM/NIH, Bldg.38-A, room 5N505A, 8600 Rockville Pike, Bethesda, MD 20894, USA

3 Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588-0115, USA

For all author emails, please log on.

Biology Direct 2006, 1:10  doi:10.1186/1745-6150-1-10

Published: 3 April 2006

Abstract

Background

Predicting and proper ranking of canonical splice sites (SSs) is a challenging problem in bioinformatics and machine learning communities. Any progress in SSs recognition will lead to better understanding of splicing mechanism. We introduce several new approaches of combining a priori knowledge for improved SS detection. First, we design our new Bayesian SS sensor based on oligonucleotide counting. To further enhance prediction quality, we applied our new de novo motif detection tool MHMMotif to intronic ends and exons. We combine elements found with sensor information using Naive Bayesian Network, as implemented in our new tool SpliceScan.

Results

According to our tests, the Bayesian sensor outperforms the contemporary Maximum Entropy sensor for 5' SS detection. We report a number of putative Exonic (ESE) and Intronic (ISE) Splicing Enhancers found by MHMMotif tool. T-test statistics on mouse/rat intronic alignments indicates, that detected elements are on average more conserved as compared to other oligos, which supports our assumption of their functional importance. The tool has been shown to outperform the SpliceView, GeneSplicer, NNSplice, Genio and NetUTR tools for the test set of human genes. SpliceScan outperforms all contemporary ab initio gene structural prediction tools on the set of 5' UTR gene fragments.

Conclusion

Designed methods have many attractive properties, compared to existing approaches. Bayesian sensor, MHMMotif program and SpliceScan tools are freely available on our web site.

Reviewers

This article was reviewed by Manyuan Long, Arcady Mushegian and Mikhail Gelfand.