Dataset Information

Barcode-free next-generation sequencing error validation for ultra-rare variant detection.

ABSTRACT: The advent of next-generation sequencing (NGS) has accelerated biomedical research by enabling the high-throughput analysis of DNA sequences at a very low cost. However, NGS has limitations in detecting rare-frequency variants (?0.1~1%). NGS errors could be filtered out using molecular barcodes, by comparing read replicates among those with the same barcodes. Accordingly, these barcoding methods require redundant reads of non-target sequences, resulting in high sequencing cost. Here, we present a cost-effective NGS error validation method in a barcode-free manner. By physically extracting and individually amplifying the DNA clones of erroneous reads, we distinguish true variants of frequency?>?0.003% from the systematic NGS error and selectively validate NGS error after NGS. We achieve a PCR-induced error rate of 2.5×10-6 per base per doubling event, using 10 times less sequencing reads compared to those from previous studies.

SUBMITTER: Yeom H

PROVIDER: S-EPMC6395625 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Barcode-free next-generation sequencing error validation for ultra-rare variant detection.

Yeom Huiran H Lee Yonghee Y Ryu Taehoon T Noh Jinsung J Lee Amos Chungwon AC Lee Han-Byoel HB Kang Eunji E Song Seo Woo SW Kwon Sunghoon S

Nature communications 20190228 1

The advent of next-generation sequencing (NGS) has accelerated biomedical research by enabling the high-throughput analysis of DNA sequences at a very low cost. However, NGS has limitations in detecting rare-frequency variants (< 1%) because of high sequencing errors (> 0.1~1%). NGS errors could be filtered out using molecular barcodes, by comparing read replicates among those with the same barcodes. Accordingly, these barcoding methods require redundant reads of non-target sequences, resulting ...[more]

PMID: 30816127

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Project description:As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci.

Dataset Information

Barcode-free next-generation sequencing error validation for ultra-rare variant detection.

Publications

Barcode-free next-generation sequencing error validation for ultra-rare variant detection.

Similar Datasets

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