A phylogenetic generalized hidden Markov model for predicting alternatively spliced exons

Jonathan E. Allen, Steven L. Salzberg

Research output: Contribution to journalArticle

Abstract

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. Conclusion: 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.

Original languageEnglish (US)
Article number14
JournalAlgorithms for Molecular Biology
Volume1
Issue number1
DOIs
StatePublished - Aug 25 2006
Externally publishedYes

ASJC Scopus subject areas

  • Structural Biology
  • Molecular Biology
  • Computational Theory and Mathematics
  • Applied Mathematics

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