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Open Access Methodology article

A jumping profile Hidden Markov Model and applications to recombination sites in HIV and HCV genomes

Anne-Kathrin Schultz1, Ming Zhang12, Thomas Leitner2, Carla Kuiken2, Bette Korber23, Burkhard Morgenstern1 and Mario Stanke1*

Author Affiliations

1 Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany

2 Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

3 The Santa Fe Institute, Santa Fe, NM 87501, USA

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BMC Bioinformatics 2006, 7:265  doi:10.1186/1471-2105-7-265

Published: 22 May 2006

Abstract

Background

Jumping alignments have recently been proposed as a strategy to search a given multiple sequence alignment A against a database. Instead of comparing a database sequence S to the multiple alignment or profile as a whole, S is compared and aligned to individual sequences from A. Within this alignment, S can jump between different sequences from A, so different parts of S can be aligned to different sequences from the input multiple alignment. This approach is particularly useful for dealing with recombination events.

Results

We developed a jumping profile Hidden Markov Model (jpHMM), a probabilistic generalization of the jumping-alignment approach. Given a partition of the aligned input sequence family into known sequence subtypes, our model can jump between states corresponding to these different subtypes, depending on which subtype is locally most similar to a database sequence. Jumps between different subtypes are indicative of intersubtype recombinations. We applied our method to a large set of genome sequences from human immunodeficiency virus (HIV) and hepatitis C virus (HCV) as well as to simulated recombined genome sequences.

Conclusion

Our results demonstrate that jumps in our jumping profile HMM often correspond to recombination breakpoints; our approach can therefore be used to detect recombinations in genomic sequences. The recombination breakpoints identified by jpHMM were found to be significantly more accurate than breakpoints defined by traditional methods based on comparing single representative sequences.