Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

CSN and CAVA: variant annotation tools for rapid, robust next-generation sequencing analysis in the clinic

Márton Münz, Elise Ruark, Anthony Renwick, Emma Ramsay, Matthew Clarke, Shazia Mahamdallie, Victoria Cloke, Sheila Seal, Ann Strydom, Gerton Lunter, Nazneen Rahman
doi: https://doi.org/10.1101/016808
Márton Münz
1Wellcome Trust Center for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Elise Ruark
2Division of Genetics & Epidemiology, Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anthony Renwick
2Division of Genetics & Epidemiology, Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Emma Ramsay
2Division of Genetics & Epidemiology, Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Matthew Clarke
2Division of Genetics & Epidemiology, Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shazia Mahamdallie
2Division of Genetics & Epidemiology, Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG, UK
3TGLclinical, Institute of Cancer Research,, 15 Cotswold Road, London, SM2 5NG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Victoria Cloke
3TGLclinical, Institute of Cancer Research,, 15 Cotswold Road, London, SM2 5NG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sheila Seal
2Division of Genetics & Epidemiology, Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG, UK
3TGLclinical, Institute of Cancer Research,, 15 Cotswold Road, London, SM2 5NG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ann Strydom
2Division of Genetics & Epidemiology, Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG, UK
3TGLclinical, Institute of Cancer Research,, 15 Cotswold Road, London, SM2 5NG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gerton Lunter
1Wellcome Trust Center for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nazneen Rahman
2Division of Genetics & Epidemiology, Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG, UK
3TGLclinical, Institute of Cancer Research,, 15 Cotswold Road, London, SM2 5NG, UK
4The Royal Marsden NHS Foundation Trust, Downs Road Sutton, SM2 5PT, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

ABSTRACT

Background Next-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.

Methods We 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.

Results CAVA 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.

Conclusions CAVA is a freely available tool that provides rapid, robust, high-throughput clinical annotation of NGS data, using a standardized Clinical Sequencing Nomenclature.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted March 21, 2015.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
CSN and CAVA: variant annotation tools for rapid, robust next-generation sequencing analysis in the clinic
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
CSN and CAVA: variant annotation tools for rapid, robust next-generation sequencing analysis in the clinic
Márton Münz, Elise Ruark, Anthony Renwick, Emma Ramsay, Matthew Clarke, Shazia Mahamdallie, Victoria Cloke, Sheila Seal, Ann Strydom, Gerton Lunter, Nazneen Rahman
bioRxiv 016808; doi: https://doi.org/10.1101/016808
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
CSN and CAVA: variant annotation tools for rapid, robust next-generation sequencing analysis in the clinic
Márton Münz, Elise Ruark, Anthony Renwick, Emma Ramsay, Matthew Clarke, Shazia Mahamdallie, Victoria Cloke, Sheila Seal, Ann Strydom, Gerton Lunter, Nazneen Rahman
bioRxiv 016808; doi: https://doi.org/10.1101/016808

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3686)
  • Biochemistry (7774)
  • Bioengineering (5668)
  • Bioinformatics (21245)
  • Biophysics (10563)
  • Cancer Biology (8162)
  • Cell Biology (11915)
  • Clinical Trials (138)
  • Developmental Biology (6738)
  • Ecology (10388)
  • Epidemiology (2065)
  • Evolutionary Biology (13843)
  • Genetics (9694)
  • Genomics (13056)
  • Immunology (8123)
  • Microbiology (19956)
  • Molecular Biology (7833)
  • Neuroscience (42973)
  • Paleontology (318)
  • Pathology (1276)
  • Pharmacology and Toxicology (2256)
  • Physiology (3350)
  • Plant Biology (7208)
  • Scientific Communication and Education (1309)
  • Synthetic Biology (1999)
  • Systems Biology (5528)
  • Zoology (1126)