Nucleic Acids Research, Vol 27, Issue 19 3911-3920, Copyright © 1999 by Oxford University Press
J Besemer and M Borodovsky
Computer methods of accurate gene finding in DNA sequences require models
of protein coding and non-coding regions derived either from experimentally
validated training sets or from large amounts of anonymous DNA sequence.
Here we propose a new, heuristic method producing fairly accurate
inhomogeneous Markov models of protein coding regions. The new method needs
such a small amount of DNA sequence data that the model can be built 'on
the fly' by a web server for any DNA sequence >400 nt. Tests on 10
complete bacterial genomes performed with the GeneMark.hmm program
demonstrated the ability of the new models to detect 93.1% of annotated
genes on average, while models built by traditional training predict an
average of 93.9% of genes. Models built by the heuristic approach could be
used to find genes in small fragments of anonymous prokaryotic genomes and
in genomes of organelles, viruses, phages and plasmids, as well as in
highly inhomogeneous genomes where adjustment of models to local DNA
composition is needed. The heuristic method also gives an insight into the
mechanism of codon usage pattern evolution.
ARTICLES
Heuristic approach to deriving models for gene finding
School of Biology, Georgia Institute of Technology, Atlanta, GA 30332- 0230, USA.
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