Paragraph: a graph-based structural variant genotyper for short-read sequence data.

 
TitleParagraph: a graph-based structural variant genotyper for short-read sequence data.
Publication TypeJournal Article
Year of Publication2019
AuthorsChen, S, Krusche, P, Dolzhenko, E, Sherman, RM, Petrovski, R, Schlesinger, F, Kirsche, M, Bentley, DR, Schatz, MC, Sedlazeck, FJ, Eberle, MA
JournalGenome Biol
Volume20
Issue1
Pagination291
Date Published2019 12 19
ISSN1474-760X
Abstract

Accurate detection and genotyping of structural variations (SVs) from short-read data is a long-standing area of development in genomics research and clinical sequencing pipelines. We introduce Paragraph, an accurate genotyper that models SVs using sequence graphs and SV annotations. We demonstrate the accuracy of Paragraph on whole-genome sequence data from three samples using long-read SV calls as the truth set, and then apply Paragraph at scale to a cohort of 100 short-read sequenced samples of diverse ancestry. Our analysis shows that Paragraph has better accuracy than other existing genotypers and can be applied to population-scale studies.

DOI10.1186/s13059-019-1909-7
Alternate JournalGenome Biol.
PubMed ID31856913
PubMed Central IDPMC6921448
Grant ListR01-HG006677 / / Foundation for the National Institutes of Health / International
UM1-HG008898 / / Foundation for the National Institutes of Health / International