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Bioinformatics Advance Access originally published online on August 23, 2007
Bioinformatics 2007 23(22):2993-3000; doi:10.1093/bioinformatics/btm413
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Sliding MinPD: building evolutionary networks of serial samples via an automated recombination detection approach

Patricia Buendia and Giri Narasimhan *

Bioinformatics Research Group (BioRG), School of Computing and Information Science, Florida International University, Miami, FL 33199, USA

*To whom correspondence should be addressed.


  Abstract

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: Martin Bishop

Received on May 16, 2007; revised on August 9, 2007; accepted on August 9, 2007

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