Reconstruction of viral population structure from next-generation sequencing data using multicommodity flows

Abstract

BackgroundHighly mutable RNA viruses exist in infected hosts as heterogeneous populations of genetically close variants known as quasispecies. Next-generation sequencing (NGS) allows for analysing a large number of viral sequences from infected patients, presenting a novel opportunity for studying the structure of a viral population and understanding virus evolution, drug resistance and immune escape. Accurate reconstruction of genetic composition of intra-host viral populations involves assembling the NGS short reads into whole-genome sequences and estimating frequencies of individual viral variants. Although a few approaches were developed for this task, accurate reconstruction of quasispecies populations remains greatly unresolved.ResultsTwo new methods, AmpMCF and ShotMCF, for reconstruction of the whole-genome intra-host viral variants and estimation of their frequencies were developed, based on Multicommodity Flows (MCFs). AmpMCF was designed for NGS reads obtained from individual PCR amplicons and ShotMCF for NGS shotgun reads. While AmpMCF, based on covering formulation, identifies a minimal set of quasispecies explaining all observed reads, ShotMCS, based on packing formulation, engages the maximal number of reads to generate the most probable set of quasispecies. Both methods were evaluated on simulated data in comparison to Maximum Bandwidth and ViSpA, previously developed state-of-the-art algorithms for estimating quasispecies spectra from the NGS amplicon and shotgun reads, respectively. Both algorithms were accurate in estimation of quasispecies frequencies, especially from large datasets.ConclusionsThe problem of viral population reconstruction from amplicon or shotgun NGS reads was solved using the MCF formulation. The two methods, ShotMCF and AmpMCF, developed here afford accurate reconstruction of the structure of intra-host viral population from NGS reads. The implementations of the algorithms are available at http://alan.cs.gsu.edu/vira.html (AmpMCF) and http://alan.cs.gsu.edu/NGS/?q=content/shotmcf (ShotMCF).

Estimation of viral population structure from amplicon-based reads

“…VirA takes as input the sequenced reads, a reference genome, and the target-specific primers used for PCR. It estimates the viral population by performing the following steps: 1) align the reads to the reference [1] 2) identify each amplicon (i.e., targeted region) and locally reconstruct the population via kGEM[2] 3) construct a read-overlap graph 4) assemble the population using either a Maximum Bandwidth [3] or Multi-commodity Flow method [4].…”

Analysis of Heterogeneous Genomic Samples Using Image Normalization and Machine Learning

“…Analysis of heterogeneous viral populations is one of the most challenging bioinformatics tasks due both to the complexity of the underlying algorithmic problems and features and sheer amount of data [2,22]. These challenges became especially complicated in the recent decade with the advent of high-throughput sequencing (HTS), which has now become a major tool for viral research by allowing to sample viral populations on unprecedented depth [3,10,14,16,19,29,31]. Modern computational virology continues mostly to rely on classical approaches, which includes sequence analysis, phylogenetics/phylodynamics and structural bioinformatics [21,22].…”

Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction

“…In the recent years, a number of computational tools for inference of viral quasispecies populations from "noisy" NGS data have been proposed, including Savage [5], PredictHaplo [30], aBayesQR [1], QuasiRecomb [42], HaploClique [41], VGA [26], VirA [39,25], SHORAH [48], ViSpA [4], QURE [31] and others [49,38,36,6,45]. Even though these algorithms proved useful in many applications, accurate and scalable viral haplotyping remains a challenge.…”