Abstract

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0195-6) contains supplementary material, which is available to authorized users.

Highlights

  • Next-generation sequencing (NGS) offers unprecedented opportunities to expand clinical genomics

  • Clinical Annotation of VAriants (CAVA) To provide clinical sequencing nomenclature (CSN) annotation in a robust and automated fashion, we developed a tool called CAVA (Clinical Annotation of VAriants) which is written in Python

  • Clinical sequencing nomenclature The CSN is based on the Human Genome Variation Society (HGVS) guidelines to facilitate integration with data generated by pre-NGS methods whilst providing standardization and compatibility with large-scale automated NGS data calling

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Summary

Introduction

Next-generation sequencing (NGS) offers unprecedented opportunities to expand clinical genomics. We present tools that address these challenges to provide rapid, standardized, clinically appropriate annotation of NGS data in line with existing clinical standards. Genetic testing has been an important clinical activity for over 20 years during which time many different mutation detection methods have been utilized and many thousands of clinically relevant variant datasets have been generated. In recent years next-generation sequencing (NGS) has been transforming clinical genomics, allowing rapid interrogation of tens of thousands of genes and the identification of millions of variants [1]. There are important, underappreciated differences in the outputs of pre-NGS and NGS gene sequencing methods which are hindering the required integration of data and the potential of genomics to impact health. Rs80357713 is associated with 12 different annotations on dbSNP, none of which is the standard clinical representation of the mutation: BRCA1 c.68_69delAG [2, 3]

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