Nucleic Acids Research, Vol 26, Issue 4 1107-1115, Copyright © 1998 by Oxford University Press
AV Lukashin and M Borodovsky
The number of completely sequenced bacterial genomes has been growing fast.
There are computer methods available for finding genes but yet there is a
need for more accurate algorithms. The GeneMark. hmm algorithm presented
here was designed to improve the gene prediction quality in terms of
finding exact gene boundaries. The idea was to embed the GeneMark models
into naturally derived hidden Markov model framework with gene boundaries
modeled as transitions between hidden states. We also used the specially
derived ribosome binding site pattern to refine predictions of translation
initiation codons. The algorithm was evaluated on several test sets
including 10 complete bacterial genomes. It was shown that the new
algorithm is significantly more accurate than GeneMark in exact gene
prediction. Interestingly, the high gene finding accuracy was observed even
in the case when Markov models of order zero, one and two were used. We
present the analysis of false positive and false negative predictions with
the caution that these categories are not precisely defined if the public
database annotation is used as a control.
ARTICLES
GeneMark.hmm: new solutions for gene finding
School of Biology and Schools of Biology and Mathematics, Georgia Institute of Technology, Atlanta, GA 30332-0230, USA.
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J. Walter, M. Mangold, and G. W. Tannock Construction, Analysis, and {beta}-Glucanase Screening of a Bacterial Artificial Chromosome Library from the Large-Bowel Microbiota of Mice Appl. Envir. Microbiol., May 1, 2005; 71(5): 2347 - 2354. [Abstract] [Full Text] [PDF]
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