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Open Access Research article

More accurate recombination prediction in HIV-1 using a robust decoding algorithm for HMMs

Jakub Truszkowski* and Daniel G Brown

Author Affiliations

David R Cheriton School of Computer Science, University of Waterloo, Waterloo ON N2L 3G1 Canada

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BMC Bioinformatics 2011, 12:168  doi:10.1186/1471-2105-12-168

Published: 17 May 2011

Abstract

Background

Identifying recombinations in HIV is important for studying the epidemiology of the virus and aids in the design of potential vaccines and treatments. The previous widely-used tool for this task uses the Viterbi algorithm in a hidden Markov model to model recombinant sequences.

Results

We apply a new decoding algorithm for this HMM that improves prediction accuracy. Exactly locating breakpoints is usually impossible, since different subtypes are highly conserved in some sequence regions. Our algorithm identifies these sites up to a certain error tolerance. Our new algorithm is more accurate in predicting the location of recombination breakpoints. Our implementation of the algorithm is available at http://www.cs.uwaterloo.ca/~jmtruszk/jphmm_balls.tar.gz webcite.

Conclusions

By explicitly accounting for uncertainty in breakpoint positions, our algorithm offers more reliable predictions of recombination breakpoints in HIV-1. We also document a new domain of use for our new decoding approach in HMMs.