Coding exon detection using comparative sequences.
We introduce a new system, called shortHMM, for predicting exons, which predicts individual exons using two related genomes. In this system, we build a hidden semi-Markov model to identify exons. In the hidden Markov model, we propose joint probability models of nucleotides in introns, splice sites, 5'UTR, 3'UTR, and intergenic regions by exploiting the homology between related genomes. In order to reduce the false positive rate of the hidden Markov model, we develop a screening process which is able to identify intergenic regions. We then build a classifier by combining the statistics from the hidden Markov model and the screening process. We implement shortHMM on human-mouse sequence alignments. The source codes are available at < www.stat.purdue.edu/ jingwu/hmm >. Compared to TWINSCAN and SLAM, shortHMM is substantially more powerful in identifying AT-rich RefSeq exons (8% more AT-rich RefSeq exons were predicted), as well as slightly more powerful in identifying RefSeq exons (3-10% more RefSeq exons were predicted), at a similar or lower false positive rate, with less computing time and with less memory usage. Last, shortHMM is also capable of finding new potential exons.
Wu
Jing
J
Department of Statistics, Purdue University, West Lafayette, Indiana 47906, USA. jingwu@stat.purdue.edu
Haussler
David
D
D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D005091:Exons; D016678:Genome; D006801:Humans; D007438:Introns; D008390:Markov Chains; D051379:Mice; D008969:Molecular Sequence Data; D016415:Sequence Alignment