This article is part of the supplement: EGASP '05: ENCODE Genome Annotation Assessment Project
JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions
1 Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
2 Department of Computer Science, John Hopkins University, Baltimore, MD 21218, USA
3 Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA
Citation and License
Genome Biology 2006, 7(Suppl 1):S9 doi:10.1186/gb-2006-7-s1-s9Published: 7 August 2006
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.
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.
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.