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 chi2-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@ee.nthu.edu.tw.