Highly Sensitive and Specific Detection of Rare Variants in Mixed Viral Populations from Massively Parallel Sequence Data

Abstract

Viruses diversify over time within hosts, often undercutting the effectiveness of host defenses and therapeutic interventions. To design successful vaccines and therapeutics, it is critical to better understand viral diversification, including comprehensively characterizing the genetic variants in viral intra-host populations and modeling changes from transmission through the course of infection. Massively parallel sequencing technologies can overcome the cost constraints of older sequencing methods and obtain the high sequence coverage needed to detect rare genetic variants (<1%) within an infected host, and to assay variants without prior knowledge. Critical to interpreting deep sequence data sets is the ability to distinguish biological variants from process errors with high sensitivity and specificity. To address this challenge, we describe V-Phaser, an algorithm able to recognize rare biological variants in mixed populations. V-Phaser uses covariation (i.e. phasing) between observed variants to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates base quality scores to increase specificity. Overall, V-Phaser achieved >97% sensitivity and >97% specificity on control read sets. On data derived from a patient after four years of HIV-1 infection, V-Phaser detected 2,015 variants across the ∼10 kb genome, including 603 rare variants (<1% frequency) detected only using phase information. V-Phaser identified variants at frequencies down to 0.2%, comparable to the detection threshold of allele-specific PCR, a method that requires prior knowledge of the variants. The high sensitivity and specificity of V-Phaser enables identifying and tracking changes in low frequency variants in mixed populations such as RNA viruses.

Vaccine-Induced Simian Immunodeficiency Virus-Specific CD8 + T-Cell Responses Focused on a Single Nef Epitope Select for Escape Variants Shortly after Infection

“…Furthermore, RC454 optimizes read alignments using coding-frame information. The V-Phaser algorithm was then used to distinguish an observed variant as a true variant from an amplification or sequencing artifact (29). All raw read files generated as part of this study are available at the NCBI sequence read archive under experiment accession number SRA278133.…”

Trace amounts of sporadically reappearing HCV RNA can cause infection

“…After cleaning, reads were passed to V-Phaser for variant calling. Briefly, V-Phaser uses phase and quality filtering with a probability model that recalibrates quality scores for individual bases to iteratively refine probabilities and to define the threshold required to statistically define a true variant from a sequencing artifact (39). Analyses were further subjected to manual inspection to identify and discard any sequencing artifacts.…”

Genome-Wide Patterns of Intrahuman Dengue Virus Diversity Reveal Associations with Viral Phylogenetic Clade and Interhost Diversity

“…Using this approach, we captured diversity at 40 to 98% (average, ϳ75%) of the DENV-2 genome from 25 serum samples collected from 22 individuals, with an average coverage of 110 to 812 reads per nucleotide in each sample, and employed variantcalling algorithms (45) to identify true variants. The scale of this data set allowed us to critically compare gene-wise diversity within and between samples, detect rare mutation events, and correlate multiple measures of intrahost diversity with interhost viral diversity in a manner that was not possible before.…”