QuasiRecomb

Probabilistic inference of viral Quasispecies subjected to Recombination

Introduction

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 jumping hidden Markov model that describes the generation of the viral quasispecies and a method to infer its parameters by analysing next generation sequencing data. We offer an implementation of the 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 tested by applying to reads obtained from experimental HIV samples.

Features

• First algorithm that models the recombination process
• Allows position-wise mutation events
• Infers a parametric probability distribution from the underlying viral population
• Estimates position-wise sequencing error-rates
• Local, gene- and genome-wide reconstruction
• Reports SNV (single nucleotide variant) posteriors
• Incorporates paired-end information
• Uses PHRED scores to weight each base of each read
• Input may contain paired-end and single reads
• Supports reads of all current sequencing technologies (454/Roche, Illumina and PacBio)
• Suitable for amplicon and shotgun sequencing projects
• Reports reconstructed haplotypes and their relative frequencies
• Reports translated proteins in all three reading frames with their relative frequencies
• Input data can be in BAM or SAM format

Download

Latest binary file at SourceForge

Source code on Github

Readme

Help and FAQ is available at README@github

Authors and contributions:

• Armin Töpfer
• Osvaldo Zagordi
• Niko Beerenwinkel

Publications

Armin Töpfer, Osvaldo Zagordi, Sandhya Prabhakaran, Volker Roth, Eran Halperin, and Niko Beerenwinkel.
Probabilistic inference of viral quasispecies subject to recombination.
Journal of Computational Biology. February 2013, 20(2): 113-123.
DOI: 10.1089/cmb.2012.0232

Osvaldo Zagordi, Armin Töpfer, Sandhya Prabhakaran, Volker Roth, Eran Halperin, and Niko Beerenwinkel.
Probabilistic inference of viral quasispecies subject to recombination.
In B. Chor, editor, RECOMB 2012 – Research in Computational Molecular Biology, volume 7262 of Lecture Notes in Bioinformatics, pages 342–354. Springer, 2012.

Tags

Haplotype reconstruction, viral quasispecies, RNA virus, probabilistic inference, Hidden Markov-Model, Maximum A Posteriori estimate, regularization, recombination, HIV-1