David Kulp and David Haussler Martin G. Reese and Frank H. Eeckman
Baskin Center for Computer
Engineering and Information Sciences
University of California
Santa Cruz, CA 95064
Lawrence Berkeley Laboratory
Genome Informatics Group
1 Cyclotron Road
Berkeley, CA, 94720
Martin G. Reese and Frank H. Eeckman
The description and results of an implementation of such a gene-finding model, called Genie, is presented. The exon sensor is a codon frequency model conditioned on windowed nucleotide frequency and the preceding codon. Two neural networks are used, as in (Brunak et al., 1991), for splice site prediction.
We show that this simple model performs quite well. For a cross-validated standard test set of 304 genes [ftp://www-hgc.lbl.gov/pub/genesets] in human DNA, our gene-finding system identified up to 85% of protein-coding bases correctly with a specificity of 80%. 58% of exons were exactly identified with a specificity of 51%. Genie is shown to perform favorably compared with several other gene-finding systems.
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