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Recco: recombination analysis using cost optimization

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45011

Maydt,  Jochen
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44907

Lengauer,  Thomas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Zitation

Maydt, J., & Lengauer, T. (2006). Recco: recombination analysis using cost optimization. Bioinformatics, 22, 1064-1071.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-23CD-A
Zusammenfassung
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 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 with the algorithm Geneconv (Sawyer, 1989). For specific relevant recombination scenarios Recco significantly outperforms Geneconv. Availability: Recco is available at http://bioinf.mpi-inf.mpg.de/recco/ Contact: jmaydt@mpi-inf.mpg.de