Bioinformatics Advance Access published online on August 23, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm413
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Sliding MinPD: Building evolutionary networks of serial samples via an automated recombination detection approach
1Bioinformatics Research Group (BioRG), School of Computing and Information Science, Florida International University, Miami, FL 33199, USA.
*To whom correspondence should be addressed. Giri Narasimhan, E-mail: giri{at}cis.fiu.edu
| Abstract |
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Motivation: Traditional phylogenetic methods assume tree-like evolutionary models and are likely to perform poorly when provided with sequence data from fast-evolving, recombining viruses. Furthermore, these methods assume that all the sequence data are from contemporaneous taxa, which is not valid for serially-sampled data. A more general approach is proposed here, referred to as the Sliding MinPD method, that reconstructs evolutionary networks for serially-sampled sequences in the presence of recombination.
Results: Sliding MinPD combines distance-based phylogenetic methods with automated recombination detection based on the best-known sliding window approaches to reconstruct serial evolutionary networks. Its performance was evaluated through comprehensive simulation studies and was also applied to a set of serially-sampled HIV sequences from a single patient. The resulting network organizations reveal unique patterns of viral evolution and may help explain the emergence of disease-associated mutants and drug-resistant strains, with implications for patient prognosis and treatment strategies.
Availability: From website http://biorg.cis.fiu.edu/SlidingMinPD
Contact: giri{at}cis.fiu.edu
Supplementary information: http://biorg.cis.fiu.edu/SlidingMinPD
Associate Editor: Prof. Martin Bishop
Received on May 16, 2007; revised on August 9, 2007; accepted on August 9, 2007
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