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JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions

dc.contributor.authorAllen, Jonathan E.
dc.contributor.authorMajoros, William H.
dc.contributor.authorPertea, Mihaela
dc.contributor.authorSalzberg, Steven L.
dc.date.accessioned2008-06-13T13:14:24Z
dc.date.available2008-06-13T13:14:24Z
dc.date.issued2006-08-07
dc.identifier.citationJIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions. J.E. Allen, W.H. Majoros, M. Pertea, and S.L. Salzberg. Genome Biology 2006, 7(Suppl):S9.en
dc.identifier.urihttp://hdl.handle.net/1903/7999
dc.description.abstractBackground: 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.en
dc.format.extent362938 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.publisherGenome Biologyen
dc.subjectDNAen
dc.subjectENCODE regionsen
dc.subjectJIGSAWen
dc.subjectGeneZillaen
dc.subjectGlimmerHMMen
dc.subjectgenesen
dc.titleJIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regionsen
dc.typeArticleen
dc.relation.isAvailableAtCollege of Computer, Mathematical & Physical Sciencesen_us
dc.relation.isAvailableAtComputer Scienceen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us


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