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An HMM posterior decoder for sequence feature prediction that includes homology information
Center for Genomics and Bioinformatics, Karolinska Institutet.ORCID iD: 0000-0001-5689-9797
2005 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1460-2059, Vol. 21, no Suppl.1, i251-i257 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil-coil structures, protein secondary structure or genes, extra support can be gained from homologs. Results: We present here a general hidden Markov model (HMM) decoding algorithm that combines probabilities for sequence features of homologs by considering the average of the posterior label probability of each position in a global sequence alignment. The algorithm is an extension of the previously described 'optimal accuracy' decoder, allowing homology information to be used. It was benchmarked using an HMM for transmembrane topology and signal peptide prediction, Phobius. We found that the performance was substantially increased when incorporating information from homologs.

Place, publisher, year, edition, pages
2005. Vol. 21, no Suppl.1, i251-i257 p.
National Category
Bioinformatics (Computational Biology)
URN: urn:nbn:se:kth:diva-48863DOI: 10.1093/bioinformatics/bti1014ISI: 000230273000028PubMedID: 15961464OAI: diva2:458737
QC 20111124Available from: 2011-11-23 Created: 2011-11-23 Last updated: 2011-11-24Bibliographically approved

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Käll, Lukas
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