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Bioinformatics Advance Access published online on January 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth028
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received August 2, 2003
Accepted November 10, 2003

Article

Effects of choice of DNA sequence model structure on gene identification accuracy

Rajeev K. Azad 1 Mark Borodovsky 2*

1 School of Biology, Georgia Institute of Technology, Atlanta, GA 30332-0230, USA
2 School of Biology, Georgia Institute of Technology, Atlanta, GA 30332-0230, USA; School of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0230, USA

* To whom correspondence should be addressed. E-mail: mark{at}amber.biology.gatech.edu.


  Abstract

Motivation: Markov chain models of DNA sequences have frequently been used in gene finding algorithms. Performance of the algorithm critically depends on the model structure and parameters. Still, the issue of choosing the model structure has not been studied with sufficient attention.

Results: We have assessed performance of several types of Markov chain models, both fixed order models and models with interpolation, within the framework of the GeneMark algorithm. The performance was measured in two ways: (i) the accuracy of detection of protein-coding potential in artificial DNA sequences; and (ii) the accuracy of identifying genes in real prokaryotic genomes. We observed that the models built by deleted interpolation slightly outperformed other models in detecting protein-coding potential in artificial DNA sequences with GC content in the medium range and also in detecting genes in real genomes with medium GC content. For artificial and real genomic DNA with high or low GC content, we observed that the models built by deleted interpolation were in some cases slightly outperformed by fixed order models.


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