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

• Type of Material: Article
• Authors: Alex V. Kotlar, Emory University; Cristina E. Trevino, Emory University; Michael Zwick, Emory University; David J Cutler, Emory University; Thomas Wingo, Emory University
• Language: English
• Date: 2018-02-06
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © 2018 The Author(s).
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s13059-018-1387-3
• Title of Journal or Parent Work: Genome Biology
• ISSN: 1474-7596
• Volume: 19
• Start Page: 14
• End Page: 14
• Grant/Funding Information: 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: Biotechnology & Applied Microbiology; GENETIC-VARIATION; Science & Technology; Filtering; Bioinformatics; Web; Life Sciences & Biomedicine; Genomics; SEQUENCING DATA; Natural-language search; POPULATION; Cloud; Big data; Genetics & Heredity; Annotation; Online
• Research Categories: Biology, Neuroscience; Biology, Genetics

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.