Dataset Information

Reproducibility of Variant Calls in Replicate Next Generation Sequencing Experiments.

ABSTRACT: Nucleotide alterations detected by next generation sequencing are not always true biological changes but could represent sequencing errors. Even highly accurate methods can yield substantial error rates when applied to millions of nucleotides. In this study, we examined the reproducibility of nucleotide variant calls in replicate sequencing experiments of the same genomic DNA. We performed targeted sequencing of all known human protein kinase genes (kinome) (~3.2 Mb) using the SOLiD v4 platform. Seventeen breast cancer samples were sequenced in duplicate (n=14) or triplicate (n=3) to assess concordance of all calls and single nucleotide variant (SNV) calls. The concordance rates over the entire sequenced region were >99.99%, while the concordance rates for SNVs were 54.3-75.5%. There was substantial variation in basic sequencing metrics from experiment to experiment. The type of nucleotide substitution and genomic location of the variant had little impact on concordance but concordance increased with coverage level, variant allele count (VAC), variant allele frequency (VAF), variant allele quality and p-value of SNV-call. The most important determinants of concordance were VAC and VAF. Even using the highest stringency of QC metrics the reproducibility of SNV calls was around 80% suggesting that erroneous variant calling can be as high as 20-40% in a single experiment. The sequence data have been deposited into the European Genome-phenome Archive (EGA) with accession number EGAS00001000826.

SUBMITTER: Qi Y

PROVIDER: S-EPMC4489803 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Reproducibility of Variant Calls in Replicate Next Generation Sequencing Experiments.

Qi Yuan Y Liu Xiuping X Liu Chang-Gong CG Wang Bailing B Hess Kenneth R KR Symmans W Fraser WF Shi Weiwei W Pusztai Lajos L

PloS one 20150702 7

Nucleotide alterations detected by next generation sequencing are not always true biological changes but could represent sequencing errors. Even highly accurate methods can yield substantial error rates when applied to millions of nucleotides. In this study, we examined the reproducibility of nucleotide variant calls in replicate sequencing experiments of the same genomic DNA. We performed targeted sequencing of all known human protein kinase genes (kinome) (~3.2 Mb) using the SOLiD v4 platform. ...[more]

PMID: 26136146

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Project description:BACKGROUND:Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. With recent advances in NGS technology and software tools, the majority of variants called using NGS alone are in fact accurate and reliable. However, a small subset of difficult-to-call variants that still do require orthogonal confirmation exist. For this reason, many clinical laboratories confirm NGS results using orthogonal technologies such as Sanger sequencing. Here, we report the development of a deterministic machine-learning-based model to differentiate between these two types of variant calls: those that do not require confirmation using an orthogonal technology (high confidence), and those that require additional quality testing (low confidence). This approach allows reliable NGS-based calling in a clinical setting by identifying the few important variant calls that require orthogonal confirmation. RESULTS:We developed and tested the model using a set of 7179 variants identified by a targeted NGS panel and re-tested by Sanger sequencing. The model incorporated several signals of sequence characteristics and call quality to determine if a variant was identified at high or low confidence. The model was tuned to eliminate false positives, defined as variants that were called by NGS but not confirmed by Sanger sequencing. The model achieved very high accuracy: 99.4% (95% confidence interval: +/-?0.03%). It categorized 92.2% (6622/7179) of the variants as high confidence, and 100% of these were confirmed to be present by Sanger sequencing. Among the variants that were categorized as low confidence, defined as NGS calls of low quality that are likely to be artifacts, 92.1% (513/557) were found to be not present by Sanger sequencing. CONCLUSIONS:This work shows that NGS data contains sufficient characteristics for a machine-learning-based model to differentiate low from high confidence variants. Additionally, it reveals the importance of incorporating site-specific features as well as variant call features in such a model.

Project description:Next generation sequencing (NGS) has been leading the genetic study of human disease into an era of unprecedented productivity. Many bioinformatics pipelines have been developed to call variants from NGS data. The performance of these pipelines depends crucially on the variant caller used and on the calling strategies implemented. We studied the performance of four prevailing callers, SAMtools, GATK, glftools and Atlas2, using single-sample and multiple-sample variant-calling strategies. Using the same aligner, BWA, we built four single-sample and three multiple-sample calling pipelines and applied the pipelines to whole exome sequencing data taken from 20 individuals. We obtained genotypes generated by Illumina Infinium HumanExome v1.1 Beadchip for validation analysis and then used Sanger sequencing as a "gold-standard" method to resolve discrepancies for selected regions of high discordance. Finally, we compared the sensitivity of three of the single-sample calling pipelines using known simulated whole genome sequence data as a gold standard. Overall, for single-sample calling, the called variants were highly consistent across callers and the pairwise overlapping rate was about 0.9. Compared with other callers, GATK had the highest rediscovery rate (0.9969) and specificity (0.99996), and the Ti/Tv ratio out of GATK was closest to the expected value of 3.02. Multiple-sample calling increased the sensitivity. Results from the simulated data suggested that GATK outperformed SAMtools and glfSingle in sensitivity, especially for low coverage data. Further, for the selected discrepant regions evaluated by Sanger sequencing, variant genotypes called by exome sequencing versus the exome array were more accurate, although the average variant sensitivity and overall genotype consistency rate were as high as 95.87% and 99.82%, respectively. In conclusion, GATK showed several advantages over other variant callers for general purpose NGS analyses. The GATK pipelines we developed perform very well.

Dataset Information

Reproducibility of Variant Calls in Replicate Next Generation Sequencing Experiments.

Publications

Reproducibility of Variant Calls in Replicate Next Generation Sequencing Experiments.

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