Dataset Information

A variant by any name: quantifying annotation discordance across tools and clinical databases.

ABSTRACT: Clinical genomic testing is dependent on the robust identification and reporting of variant-level information in relation to disease. With the shift to high-throughput sequencing, a major challenge for clinical diagnostics is the cross-identification of variants called on their genomic position to resources that rely on transcript- or protein-based descriptions.We evaluated the accuracy of three tools (SnpEff, Variant Effect Predictor, and Variation Reporter) that generate transcript and protein-based variant nomenclature from genomic coordinates according to guidelines by the Human Genome Variation Society (HGVS). Our evaluation was based on transcript-controlled comparisons to a manually curated set of 126 test variants of various types drawn from data sources, each with HGVS-compliant transcript and protein descriptors. We further evaluated the concordance between annotations generated by Snpeff and Variant Effect Predictor and those in major germline and cancer databases: ClinVar and COSMIC, respectively.We find that there is substantial discordance between the annotation tools and databases in the description of insertions and/or deletions. Using our ground truth set of variants, constructed specifically to identify challenging events, accuracy was between 80 and 90% for coding and 50 and 70% for protein changes for 114 to 126 variants. Exact concordance for SNV syntax was over 99.5% between ClinVar and Variant Effect Predictor and SnpEff, but less than 90% for non-SNV variants. For COSMIC, exact concordance for coding and protein SNVs was between 65 and 88% and less than 15% for insertions. Across the tools and datasets, there was a wide range of different but equivalent expressions describing protein variants.Our results reveal significant inconsistency in variant representation across tools and databases. While some of these syntax differences may be clear to a clinician, they can confound variant matching, an important step in variant classification. These results highlight the urgent need for the adoption and adherence to uniform standards in variant annotation, with consistent reporting on the genomic reference, to enable accurate and efficient data-driven clinical care.

SUBMITTER: Yen JL

PROVIDER: S-EPMC5267466 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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A variant by any name: quantifying annotation discordance across tools and clinical databases.

Yen Jennifer L JL Garcia Sarah S Montana Aldrin A Harris Jason J Chervitz Stephen S Morra Massimo M West John J Chen Richard R Church Deanna M DM

Genome medicine 20170126 1

<h4>Background</h4>Clinical genomic testing is dependent on the robust identification and reporting of variant-level information in relation to disease. With the shift to high-throughput sequencing, a major challenge for clinical diagnostics is the cross-identification of variants called on their genomic position to resources that rely on transcript- or protein-based descriptions.<h4>Methods</h4>We evaluated the accuracy of three tools (SnpEff, Variant Effect Predictor, and Variation Reporter) t ...[more]

PMID: 28122645

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Project description:BackgroundNext-generation sequencing (NGS) offers unprecedented opportunities to expand clinical genomics. It also presents challenges with respect to integration with data from other sequencing methods and historical data. Provision of consistent, clinically applicable variant annotation of NGS data has proved difficult, particularly of indels, an important variant class in clinical genomics. Annotation in relation to a reference genome sequence, the DNA strand of coding transcripts and potential alternative variant representations has not been well addressed. Here we present tools that address these challenges to provide rapid, standardized, clinically appropriate annotation of NGS data in line with existing clinical standards.MethodsWe developed a clinical sequencing nomenclature (CSN), a fixed variant annotation consistent with the principles of the Human Genome Variation Society (HGVS) guidelines, optimized for automated variant annotation of NGS data. To deliver high-throughput CSN annotation we created CAVA (Clinical Annotation of VAriants), a fast, lightweight tool designed for easy incorporation into NGS pipelines. CAVA allows transcript specification, appropriately accommodates the strand of a gene transcript and flags variants with alternative annotations to facilitate clinical interpretation and comparison with other datasets. We evaluated CAVA in exome data and a clinical BRCA1/BRCA2 gene testing pipeline.ResultsCAVA generated CSN calls for 10,313,034 variants in the ExAC database in 13.44 hours, and annotated the ICR1000 exome series in 6.5 hours. Evaluation of 731 different indels from a single individual revealed 92 % had alternative representations in left aligned and right aligned data. Annotation of left aligned data, as performed by many annotation tools, would thus give clinically discrepant annotation for the 339 (46 %) indels in genes transcribed from the forward DNA strand. By contrast, CAVA provides the correct clinical annotation for all indels. CAVA also flagged the 370 indels with alternative representations of a different functional class, which may profoundly influence clinical interpretation. CAVA annotation of 50 BRCA1/BRCA2 gene mutations from a clinical pipeline gave 100 % concordance with Sanger data; only 8/25 BRCA2 mutations were correctly clinically annotated by other tools.ConclusionsCAVA is a freely available tool that provides rapid, robust, high-throughput clinical annotation of NGS data, using a standardized clinical sequencing nomenclature.

Dataset Information

A variant by any name: quantifying annotation discordance across tools and clinical databases.

Publications

A variant by any name: quantifying annotation discordance across tools and clinical databases.

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