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.
CONCLUSION: 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.