RT Journal Article
SR Electronic
T1 Paragraph: A graph-based structural variant genotyper for short-read sequence data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 635011
DO 10.1101/635011
A1 Sai Chen
A1 Peter Krusche
A1 Egor Dolzhenko
A1 Rachel M. Sherman
A1 Roman Petrovski
A1 Felix Schlesinger
A1 Melanie Kirsche
A1 David R. Bentley
A1 Michael C. Schatz
A1 Fritz J. Sedlazeck
A1 Michael A. Eberle
YR 2019
UL http://biorxiv.org/content/early/2019/09/24/635011.abstract
AB 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.SVstructural variationbpbase pairTRtandem repeat