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Recco: Recombination Analysis using Cost Optimization

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

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

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Citation

Maydt, J., & Lengauer, T. (2006). Recco: Recombination Analysis using Cost Optimization. Bioinformatics, 22(9), 1064-1071. doi:10.1093/bioinformatics/btl057.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-23CD-A
Abstract
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