Bioinformatics Advance Access published online on September 16, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti025
Bioinformatics © Oxford University Press 2004; all rights reserved
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1 Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
* To whom correspondence should be addressed. E-mail: cclu{at}ee.nthu.edu.tw.
Motivation: Thanks to the complete sequencing of the human and many other genomes, huge amounts of DNA sequence data have already been accumulated. In bioinformatics, an important issue is how to predict the complete structure of genes from the genomic DNA sequence, especially in the human genome. A crucial part in 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 inter-dependency between base positions in a splice site. The establishment of dependency between two positions is based on a chi-square 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 resoning). Compared with existing models such as WMM, WAM, MDD, Cai et al.'s Tree model in the literature as well as the second-order and third-order Markov chain models, which has not been well studied in the literature, the expanded Bayesian networks from our dependency graph models perform the best in nearly all cases studied. Availability: Software (a program called DGSplicer) and datasets used are available at http://csrl.ee.nthu.edu.tw/bioinf/.
Revised August 11, 2004
Accepted May 3, 2004
Article
Prediction of splice sites with dependency graphs and their expanded bayesian networks
2 Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA
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