Bioinformatics Advance Access published online on February 17, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl057
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1 Max-Planck Institut für Informatik, Saarbrücken, Germany
* To whom correspondence should be addressed.
Motivation: Recombination plays an important role in the evolution of many pathogens, such as HIV or malaria. Despite substantial prior work, there is still a pressing need for efficient and effective methods of detecting recombination and analyzing recombinant sequences. Results: We introduce Recco, a novel fast method that, given a multiple sequence alignment scores the cost of obtaining one of the sequences from the others by mutation and recombination. The algorithm comes with an illustrative visualization tool for locating recombination breakpoints. We analyze the sequence alignment with respect to all choices of the parameter Availability: Recco is available at http://bioinf.mpi-inf.mpg.de/recco/.
Received December 8, 2005
Revised February 13, 2006
Accepted February 13, 2006
Article
Recco: recombination analysis using cost optimization
Jochen Maydt 1 *
and
Thomas Lengauer 1
Jochen Maydt, E-mail: jmaydt{at}mpi-inf.mpg.de
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Abstract
weighting recombination cost against mutation cost. The analysis of the resulting cost curve yields additional information as to which sequence might be recombinant. On random genealogies Recco is comparable in its power of detecting recombination to the algorithm Geneconv (Sawyer 1989). For specific relevant recombination scenarios Recco significantly outperforms Geneconv.
Associate Editor: Martin Bishop
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