A phylogenetic generalized hidden Markov model for predicting alternatively spliced exons.
BACKGROUND
An important challenge in eukaryotic gene prediction is accurate identification of alternatively spliced exons. Functional transcripts can go undetected in gene expression studies when alternative splicing only occurs under specific biological conditions. Non-expression based computational methods support identification of rarely expressed transcripts.


RESULTS
A non-expression based statistical method is presented to annotate alternatively spliced exons using a single genome sequence and evidence from cross-species sequence conservation. The computational method is implemented in the program ExAlt and an analysis of prediction accuracy is given for Drosophila melanogaster.


CONCLUSIONS
ExAlt identifies the structure of most alternatively spliced exons in the test set and cross-species sequence conservation is shown to improve the precision of predictions. The software package is available to run on Drosophila genomes to search for new cases of alternative splicing.
Allen
Jonathan E
JE
Center for Bioinformatics and Computational Biology, University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA. jeallen@umiacs.umd.edu
Salzberg
Steven L
SL