Bioinformatics Advance Access originally published online on September 16, 2004
Bioinformatics 2005 21(4):471-482; doi:10.1093/bioinformatics/bti025
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Bioinformatics vol. 21 issue 4 © Oxford University Press 2005; all rights reserved.
Prediction of splice sites with dependency graphs and their expanded bayesian networks
1 Department of Electrical Engineering, National Tsing Hua University Hsinchu 30013, Taiwan
2 Department of Ecology and Evolution, University of Chicago Chicago, IL 60637, USA
*To whom correspondence should be addressed.
Motivation: Owing to the complete sequencing of human and many other genomes, huge amounts of DNA sequence data have been accumulated. In bioinformatics, an important issue is how to predict the complete structure of genes from the genomic DNA sequence, especially the human genome. A crucial part in the gene structure prediction is to determine the precise exon–intron boundaries, i.e. the splice sites, in the coding region.
Results: We have developed a dependency graph model to fully capture the intrinsic interdependency between base positions in a splice site. The establishment of dependency between two position is based on a 
2-test from known sample data. To facilitate statistical inference, we have expanded the dependency graph (which is usually a graph with cycles that make probabilistic reasoning very difficult, if not impossible) into a Bayesian network (which is a directed acyclic graph that facilitates statistical reasoning).
When compared with the existing models such as weight matrix model, weight array model, maximal dependence decomposition, Cai et al.'s tree model as well as the less-studied second-order and third-order Markov chain models, the expanded Bayesian networks from our dependency graph models perform the best in nearly all the cases studied.
Availability: Software (a program called DGSplicer) and datasets used are available at http://csrl.ee.nthu.edu.tw/bioinf/
Contact: cclu{at}ee.nthu.edu.tw
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