Sliding MinPD: building evolutionary networks of serial samples via an automated recombination detection approach.
BACKGROUND
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.
Buendia
Patricia
P
Bioinformatics Research Group (BioRG), School of Computing and Information Science, Florida International University, Miami, FL 33199, USA.
Narasimhan
Giri
G
D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D004252:DNA Mutational Analysis / Q000379:methods*; D019143:Evolution, Molecular; D016679:Genome, Viral / Q000235:genetics*; D008969:Molecular Sequence Data; D010802:Phylogeny; D011995:Recombination, Genetic / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
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