Genomics | Microbial Evolution & Genomics | Bioinformatics
Probabilistic inference of viral quasispecies subject to recombination
Armin Töpfer*
*Corresponding author: Armin Töpfer
Computational Biology group, ETH Zürich, Basel, Switzerland
F1000Posters 2012, 3: 434 (slide presentation) [English]
Slide Presentation [5.38 MB]
Presented at
Research in Computational Molecular Biology (RECOMB) 2012,
21 - 24 Apr 2012, 100
RNA viruses are present in a single host as a population of different but related strains. This population, shaped by the combination of genetic change and selection, is called quasispecies. Genetic change is due to both point mutations and recombination events.
We present a hidden Markov model to infer the viral quasispecies based on next generation sequencing data, the implementation of the EM algorithm to find the maximum likelihood estimates of the model parameters, and a method to estimate the distribution of viral strains in the quasispecies. The model is validated on simulated data, showing the advantages of explicitly taking the recombination process into account, and applied to a dataset obtained from a clinical sample of an HIV-infected patient.
No relevant competing interests disclosed.
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