Probabilistic Inference of Viral Quasispecies Subject to Recombination

[Denis Jacob Machado]

Topfer, A. et al. 2013. Probabilistic Inference of Viral Quasispecies. JOURNAL OF COMPUTATIONAL BIOLOGY

Volume 20(2):113-123. DOI: 10.1089/cmb.2012.0232.

ABSTRACT

RNA viruses exist in their hosts as populations of different but related strains. The virus

population, often called quasispecies, is shaped by a combination of genetic change and

natural selection. Genetic change is due to both point mutations and recombination events.

We present a jumping hidden Markov model that describes the generation of viral qua-

sispecies and a method to infer its parameters from next-generation sequencing data. The

model introduces position-specific probability tables over the sequence alphabet to explain

the diversity that can be found in the population at each site. Recombination events are

indicated by a change of state, allowing a single observed read to originate from multiple

sequences. We present a specific implementation of the expectation maximization (EM)

algorithm to find maximum a posteriori 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 advantage of explicitly taking the recombination process into

account, and applied to reads obtained from a clinical HIV sample.