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Bioinformatics Advance Access published online on November 7, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl512
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received April 19, 2006
Revised October 3, 2006
Accepted October 3, 2006

Article

Application of a simple likelihood ratio approximant to protein sequence classification

László Kaján 1, Attila Kertész-Farkas 2, Dino Franklin 3, Neli Ivanova 3, András Kocsor 2 *, and Sándor Pongor 4

1 Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Padriciano 99, I-34012 Trieste, Italy; Present address: BioInfoBank Institute, 60-744 Poznan, Poland, Email: kajla@bioinfo.pl
2 Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged, Aradi vértanúk tere 1., H-6720 Szeged, Hungary
3 Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Padriciano 99, I-34012 Trieste, Italy
4 Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Padriciano 99, I-34012 Trieste, Italy; Bioinformatics Group, Biological Research Centre, Hungarian Academy of Sciences, Temesvári krt. 62, H-6701 Szeged, Hungary

* To whom correspondence should be addressed.
András Kocsor, E-mail: kocsor{at}inf.u-szeged.hu


  Abstract

Motivation: Likelihood ratio approximants have been widely used for model comparison in statistics. The present study was undertaken in order to explore their utility as a scoring (ranking) function in the classification of protein sequences.

Results: We used a simple likelihood ratio approximant (LRA) based on the maximal similarity (or minimal distance) scores of the two top ranking sequence classes. The scoring methods (Smith-Waterman, BLAST, local alignment kernel, amino acid composition vector-distance and compression based distances) were compared on datasets designed to test sequence similarities between proteins distantly related in terms of structure or evolution. It was found that LRA-based scoring can significantly outperform simple scoring methods.

Supplementary Materials: http://www.inf.u-szeged.hu/~kfa/lra06/.

Associate Editor: Martin Bishop
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