Patricia Buendia1, Giri Narasimhan. 1. Bioinformatics Research Group (BioRG), School of Computing and Information Science, Florida International University, Miami, FL 33199, USA.
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.
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.
Authors: H Imamichi; K A Crandall; V Natarajan; M K Jiang; R L Dewar; S Berg; A Gaddam; M Bosche; J A Metcalf; R T Davey ; H C Lane Journal: J Infect Dis Date: 2000-12-08 Impact factor: 5.226
Authors: R Shankarappa; J B Margolick; S J Gange; A G Rodrigo; D Upchurch; H Farzadegan; P Gupta; C R Rinaldo; G H Learn; X He; X L Huang; J I Mullins Journal: J Virol Date: 1999-12 Impact factor: 5.103