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GeneMark.hmm: new solutions for gene finding.

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Affiliations

  • 1. School of Biology and Schools of Biology and Mathematics, Georgia Institute of Technology, Atlanta, GA 30332-0230, USA.
      Authors
    • Lukashin AV 1
    (1 author)

Nucleic Acids Research, 01 Feb 1998, 26(4):1107-1115
https://doi.org/10.1093/nar/26.4.1107 PMID: 9461475 PMCID: PMC147337

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Abstract 

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.

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Nucleic Acids Res. 1998 Feb 15; 26(4): 1107–1115.
PMCID: PMC147337
PMID: 9461475

GeneMark.hmm: new solutions for gene finding.

A V Lukashin and M Borodovsky
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Abstract

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.

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Selected References

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