Probabilistic Inference of Viral Quasispecies Subject to Recombination

TitleProbabilistic Inference of Viral Quasispecies Subject to Recombination
Publication TypeConference Paper
Year of Publication2012
AuthorsZagordi O, Töpfer A, Prabhakaran S, Roth V, Halperin E, Beerenwinkel N
Page(s)342-354
Other Numbers3255
KeywordsHidden Markov model, Molecular sequence analysis, Next-generation sequencing, Sequencing and genotyping technologies, Viral quasispecies
Abstract

RNA viruses are present in a single host as a population ofdifferent but related strains. This population, shaped by the combinationof genetic change and selection, is called quasispecies. Genetic change isdue to both point mutations and recombination events. We present ajumping hidden Markov model that describes the generation of the viralquasispecies and a method to infer its parameters by analysing nextgeneration sequencing data. The model introduces position-specific probabilitytables over the sequence alphabet to explain the diversity that canbe found in the population at each site. Recombination events are indicatedby a change of state, allowing a single observed read to originatefrom multiple sequences. We present an implementation of the EM algorithmto find maximum likelihood estimates of the model parametersand a method to estimate the distribution of viral strains in the quasispecies.The model is validated on simulated data, showing the advantageof explicitly taking the recombination process into account, and appliedto reads obtained from two experimental HIV samples.

URLhttp://www.icsi.berkeley.edu/pubs/algorithms/ICSI_probabilisticinferenceofviral12.pdf
Bibliographic Notes

Proceedings of the 16th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2012), Barcelona, Spain, pp.342-354

Abbreviated Authors

O. Zagordi, A. Topfer, S. Prabhakaran, V. Roth, E. Halperin, and N. Beerenwinkel

ICSI Research Group

Algorithms

ICSI Publication Type

Article in conference proceedings