Integrating Database Homology in a Probabilistic Gene Structure Model

David Kulp and David Haussler
Baskin Center for Computer
Engineering and Information Sciences
University of California
Santa Cruz, CA 95064
{dkulp,haussler}@cse.ucsc.edu

Martin G. Reese and Frank H. Eeckman
Lawrence Berkeley Laboratory
Genome Informatics Group
1 Cyclotron Road
Berkeley, CA, 94720
{martinr,eeckman}@genome.lbl.gov

Abstract:

We present an improved stochastic model of genes in DNA, and describe a method for integrating database homology into the probabilistic framework. A generalized hidden Markov model (GHMM) describes the grammar of a legal parse of a DNA sequence. Probabilities are estimated for gene features by using dynamic programming to combine information from multiple sensors. We show how matches to homologous sequences from a database can be integrated into the probability estimation by interpreting the likelihood of a sequence in terms of the bit-cost to encode a sequence given a homology match. We also demonstrate how homology matches in protein databases can be exploited to help identify splice sites.

Our experiments show significant improvements in the sensitivity and specificity of gene structure identification when these new features are added to our gene-finding system, Genie. Experimental results in tests using a standard set of annotated genes showed that Genie identified 95% of coding nucleotides correctly with a specificity of 91%, and 77% of exons were identified exactly.

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