Building evolutionary networks of serial samples via a recombination detection approach
Traditional phylogenetic methods assume tree-like evolutionary models and are known to perform poorly when provided with sequence data from recombining, fast-evolving 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 needed — one that is mindful of the sampling times of the input sequences and that reconstructs the viral evolutionary relationships in the form of a network structure with implicit representations of recombination events. The underlying network organization may reveal unique patterns of viral evolution and could help explain the emergence of disease-associated mutants and drug-resistant strains, with implications for patient prognosis and treatment strategies. The method we developed, referred to as Sliding MinPD, reconstructs evolutionary networks of serially-sampled sequences by combining minimum pairwise distance measures with automated recombination detection based on a sliding window approach. The method, an improved version of the MinPD method , was tested using simulated data and was also applied to a set of serially-sampled HIV sequences from a single patient. The type of recombinant networks output by the Sliding MinPD method are referred to as serial evolutionary networks, and are a generalization of the evolutionary framework introduced by Holmes et al. in his 1992 study  .
 Buendia, P. and Narasimhan, G. (2004). MinPD: Distance-based Phylogenetic Analysis and Recombination Detection of Serially-Sampled HIV Quasispecies. Proc. IEEE Comput. Sys. Bioinform. Conf., Stanford, CA
 Holmes, E. C., Zhang, L. Q., Simmonds, P., Ludlam, C. A., and Brown, A. J. (1992). Convergent and divergent sequence evolution in the surface envelope glycoprotein of human immunodeficiency virus type 1 within a single infected patient. Proc. Natl. Acad. Sci. U. S. A.:4835-4839