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Author Notes:

Correspondence: thomas.wingo@emory.edu

Authors' contributions: AVK designed, wrote, and tested Bystro and performed experiments.

CET wrote Bystro documentation and performed quality control.

MEZ and DJC contributed to the design of Bystro and experiments.

TSW designed and wrote Bystro and designed and performed experiments.

AVK and TSW wrote the manuscript with contributions from all authors.

All authors read and approved the final manuscript.

We thank Kelly Shaw and Katherine Squires for beta testing and design suggestions.

We thank Viren Patel and the Emory Integrated Genomics Core (EIGC) for technical support.

Ethics approval and consent to participate: Not applicable.

Competing interests: The authors declare that they have no competing interests.

Subjects:

Research Funding:

This work was supported by the AWS Cloud Credits for Research program, the Molecules to Mankind program (a project of the Burroughs Wellcome Fund and the Laney Graduate School at Emory University), Veterans Health Administration (BX001820), and the National Institutes of Health (AG025688, AG056533, MH101720, and NS091859).

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Biotechnology & Applied Microbiology
  • Genetics & Heredity
  • Natural-language search
  • Genomics
  • Bioinformatics
  • Annotation
  • Filtering
  • Web
  • Online
  • Cloud
  • Big data
  • GENETIC-VARIATION
  • SEQUENCING DATA
  • POPULATION

Bystro: rapid online variant annotation and natural-language filtering at whole-genome scale

Journal Title:

Genome Biology

Volume:

Volume 19

Publisher:

, Pages 14-14

Type of Work:

Article | Final Publisher PDF

Abstract:

Accurately selecting relevant alleles in large sequencing experiments remains technically challenging. Bystro (https://bystro.io/ ) is the first online, cloud-based application that makes variant annotation and filtering accessible to all researchers for terabyte-sized whole-genome experiments containing thousands of samples. Its key innovation is a general-purpose, natural-language search engine that enables users to identify and export alleles and samples of interest in milliseconds. The search engine dramatically simplifies complex filtering tasks that previously required programming experience or specialty command-line programs. Critically, Bystro's annotation and filtering capabilities are orders of magnitude faster than previous solutions, saving weeks of processing time for large experiments.

Copyright information:

© 2018 The Author(s).

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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