Motivation: Phylogenetic shadowing is a comparative genomics principle that allows for the discovery of conserved regions in sequences from multiple closely related organisms. We develop a formal probabilistic framework for combining phylogenetic shadowing with feature-based functional annotation methods. The resulting model, a generalized hidden Markov phylogeny (GHMP), applies to a variety of situations where functional regions are to be inferred from evolutionary constraints. Results: We show how GHMPs can be used to predict complete shared gene structures in multiple primate sequences. We also describe SHADOWER, our implementation of such a prediction system. We find that SHADOWER outperforms previously reported ab initio gene finders, including comparative human–mouse approaches, on a small sample of diverse exonic regions. Finally, we report on an empirical analysis of SHADOWER's performance which reveals that as few as five well-chosen species may suffice to attain maximal sensitivity and specificity in exon demarcation.
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