Computer Science Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1593

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Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments
(Genome Biology, 2008-01) Haas, Brian U.; Salzberg, Steven L.; Zhu, Wei; Pertea, Mihaela; Allen, Jonathan E.; Orvis, Joshua; White, Owen; Buell, C Robin; Wortman, Jennifer R
EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation.
JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions
(Genome Biology, 2006-08-07) Allen, Jonathan E.; Majoros, William H.; Pertea, Mihaela; Salzberg, Steven L.
Background: Predicting complete protein-coding genes in human DNA remains a significant challenge. Though a number of promising approaches have been investigated, an ideal suite of tools has yet to emerge that can provide near perfect levels of sensitivity and specificity at the level of whole genes. As an incremental step in this direction, it is hoped that controlled gene finding experiments in the ENCODE regions will provide a more accurate view of the relative benefits of different strategies for modeling and predicting gene structures. Results: Here we describe our general-purpose eukaryotic gene finding pipeline and its major components, as well as the methodological adaptations that we found necessary in accommodating human DNA in our pipeline, noting that a similar level of effort may be necessary by ourselves and others with similar pipelines whenever a new class of genomes is presented to the community for analysis. We also describe a number of controlled experiments involving the differential inclusion of various types of evidence and feature states into our models and the resulting impact these variations have had on predictive accuracy. Conclusions: While in the case of the non-comparative gene finders we found that adding model states to represent specific biological features did little to enhance predictive accuracy, for our evidence-based ‘combiner’ program the incorporation of additional evidence tracks tended to produce significant gains in accuracy for most evidence types, suggesting that improved modeling efforts at the hidden Markov model level are of relatively little value. We relate these findings to our current plans for future research.
A phylogenetic generalized hidden Markov model for predicting alternatively spliced exons
(Algorithms for Molecular Biology, 2006-08-25) Allen, Jonathan E.; Salzburg, Steven S.
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.