Abstract
BackgroundEstimating the number of different species (richness) in a mixed microbial population has been a main focus in metagenomic research. Existing methods of species richness estimation ride on the assumption that the reads in each assembled contig correspond to only one of the microbial genomes in the population. This assumption and the underlying probabilistic formulations of existing methods are not useful for quasispecies populations where the strains are highly genetically related.The lack of knowledge on the number of different strains in a quasispecies population is observed to hinder the precision of existing Viral Quasispecies Spectrum Reconstruction (QSR) methods due to the uncontrolled reconstruction of a large number of in silico false positives. In this work, we formulated a novel probabilistic method for strain richness estimation specifically targeting viral quasispecies. By using this approach we improved our recently proposed spectrum reconstruction pipeline ViQuaS to achieve higher levels of precision in reconstructed quasispecies spectra without compromising the recall rates. We also discuss how one other existing popular QSR method named ShoRAH can be improved using this new approach.ResultsOn benchmark data sets, our estimation method provided accurate richness estimates (< 0.2 median estimation error) and improved the precision of ViQuaS by 2%-13% and F-score by 1%-9% without compromising the recall rates. We also demonstrate that our estimation method can be used to improve the precision and F-score of ShoRAH by 0%-7% and 0%-5% respectively.ConclusionsThe proposed probabilistic estimation method can be used to estimate the richness of viral populations with a quasispecies behavior and to improve the accuracy of the quasispecies spectra reconstructed by the existing methods ViQuaS and ShoRAH in the presence of a moderate level of technical sequencing errors.Availabilityhttp://sourceforge.net/projects/viquas/
Highlights
Estimating the number of different species in a mixed microbial population has been a main focus in metagenomic research
A major observation on the Quasispecies Spectrum Reconstruction (QSR) methods ViQuaS and ShoRAH was that the precision of reconstructed spectra was less than the recall rate owing to the reconstruction of in silico false positives
We realize that the F-score values of the spectra reconstructed by ViQuaS and ShoRAH can be improved by controlling the generation of false positives without compromising the recall rates, but such an improvement cannot be achieved in QuRe and PredictHaplo as the spectra generated by them usually contain a lower number of strains than the actual number of strains in the population
Summary
Estimating the number of different species (richness) in a mixed microbial population has been a main focus in metagenomic research. The lack of knowledge on the number of different strains in a quasispecies population is observed to hinder the precision of existing Viral Quasispecies Spectrum Reconstruction (QSR) methods due to the uncontrolled reconstruction of a large number of in silico false positives. We formulated a novel probabilistic method for strain richness estimation targeting viral quasispecies By using this approach we improved our recently proposed spectrum reconstruction pipeline ViQuaS to achieve higher levels of precision in reconstructed quasispecies spectra without compromising the recall rates. We present a novel probabilistic method to estimate the number of strains in a viral quasispecies population and a strategy to improve the precision and F-score of ViQuaS analysis pipeline without compromising the recall rates, by reducing the number of in silico false positives using above estimates as input information. We show that the same strategy can be used to improve the performance of ShoRAH
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