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.2016 Jul 11;90(15):6884-95.
doi: 10.1128/JVI.00667-16. Print 2016 Aug 1.

Measurements of Intrahost Viral Diversity Are Extremely Sensitive to Systematic Errors in Variant Calling

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Measurements of Intrahost Viral Diversity Are Extremely Sensitive to Systematic Errors in Variant Calling

John T McCrone et al. J Virol..

Abstract

With next-generation sequencing technologies, it is now feasible to efficiently sequence patient-derived virus populations at a depth of coverage sufficient to detect rare variants. However, each sequencing platform has characteristic error profiles, and sample collection, target amplification, and library preparation are additional processes whereby errors are introduced and propagated. Many studies account for these errors by using ad hoc quality thresholds and/or previously published statistical algorithms. Despite common usage, the majority of these approaches have not been validated under conditions that characterize many studies of intrahost diversity. Here, we use defined populations of influenza virus to mimic the diversity and titer typically found in patient-derived samples. We identified single-nucleotide variants using two commonly employed variant callers, DeepSNV and LoFreq. We found that the accuracy of these variant callers was lower than expected and exquisitely sensitive to the input titer. Small reductions in specificity had a significant impact on the number of minority variants identified and subsequent measures of diversity. We were able to increase the specificity of DeepSNV to >99.95% by applying an empirically validated set of quality thresholds. When applied to a set of influenza virus samples from a household-based cohort study, these changes resulted in a 10-fold reduction in measurements of viral diversity. We have made our sequence data and analysis code available so that others may improve on our work and use our data set to benchmark their own bioinformatics pipelines. Our work demonstrates that inadequate quality control and validation can lead to significant overestimation of intrahost diversity.

Importance: Advances in sequencing technology have made it feasible to sequence patient-derived viral samples at a level sufficient for detection of rare mutations. These high-throughput, cost-effective methods are revolutionizing the study of within-host viral diversity. However, the techniques are error prone, and the methods commonly used to control for these errors have not been validated under the conditions that characterize patient-derived samples. Here, we show that these conditions affect measurements of viral diversity. We found that the accuracy of previously benchmarked analysis pipelines was greatly reduced under patient-derived conditions. By carefully validating our sequencing analysis using known control samples, we were able to identify biases in our method and to improve our accuracy to acceptable levels. Application of our modified pipeline to a set of influenza virus samples from a cohort study provided a realistic picture of intrahost diversity and suggested the need for rigorous quality control in such studies.

Copyright © 2016, American Society for Microbiology. All Rights Reserved.

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Figures

FIG 1
FIG 1
Example of an ROC curve. (A) Hypothetical variants are stratified by the log of theP value.P value thresholds are indicated as dashed colored lines. These “data” are intended to illustrate the concept and are not based on an actual experiment. (B) An ROC curve made from the hypothetical data shown in panel A. The dashed colored lines indicate the points on the curve corresponding to the thresholds in panel A.
FIG 2
FIG 2
Accuracy of DeepSNV. (A) Reconstituted cDNA genomes of influenza virus strain WSN33 were diluted serially in reconstituted cDNA genomes of PR8, generating artificial populations with 491 single-nucleotide variants from WSN (relative to PR8) at the indicated frequencies. (B) ROC curve measuring the accuracy of DeepSNV in identifying WSN33 variants mixed with PR8 at the indicated (by colors matching those in panel A) frequencies. (C) Summary of the data in panel B at aP value threshold of 0.01. Freq, frequency; Sens, sensitivity; TP, true positives; FP, false positives.
FIG 3
FIG 3
Accuracy of DeepSNV on populations approximating patient-derived samples. (A) Twenty viral supernatants, each with a single SNV, were diluted in a WSN33 viral supernatant to generate artificial viral populations with 20 mutations at the indicated frequencies. These populations were diluted further in basal medium to match the genome concentrations found in patient-derived samples (105 to 103 genomes/μl). (B) ROC curve measuring the accuracy of DeepSNV in identifying SNV at the indicated frequencies. (C) Summary of the data in panel B at aP value threshold of 0.01.
FIG 4
FIG 4
Accuracy can be improved through more stringent quality thresholds. (A) All called variants from the five samples with 105 genomes/μl andP values of <0.01 stratified by the mean mapping quality of the reads containing the variant and the mean Phred scores of the variant bases. The dashed lines indicate common cutoffs of 20 and 30 for mapping quality and Phred, respectively. (B) Histogram of average positions on a paired-end read of the variants that passed our mean MapQ threshold of 30 and mean Phred threshold of 35. (C) ROC curve measuring the accuracy of our analysis after applying the following quality cutoffs: mean MapQ score, >30; mean Phred score, >35; average read position, between 32 and 94 (the middle 50% of the read). (D) Summary of the data in panel C at aP value threshold of 0.01.
FIG 5
FIG 5
Accuracy of the frequency measurements for true-positive SNV in the samples with 105 genomes/μl. The black bars are the medians. The dashed line is where measured and expected frequencies are equal. Note that both axes are on a log scale.
FIG 6
FIG 6
Accuracy of LoFreq on populations with 105 genomes/μl. (A) Accuracy of LoFreq using standard parameters. The specificity of LoFreq was scaled to account for the same number of tests as performed in DeepSNV. (B) Summary of the data in panel A at aP value threshold of 0.01.
FIG 7
FIG 7
Accuracy of DeepSNV on populations with lower input nucleic acid levels. (A) ROC curve for the samples with 104 genomes/μl. (B) Summary of the data in panel A at aP value threshold of 0.01. (C) ROC curve for the samples with 103 genomes/μl. (D) Summary of the data in panel C at aP value threshold of 0.01.
FIG 8
FIG 8
At lower inputs, duplicate samples improve accuracy. (A) ROC curve of the samples with 104 genomes/μl processed in duplicate. Only SNV present in both samples were considered. (B) Summary of the data in panel A at aP value threshold of 0.01.
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