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

delete2019-12-19
delete100
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OA
AI
S
Sai Chen
P
Peter Krusche
E
Egor Dolzhenko
R
Rachel M. Sherman
R
Roman Petrovski
F
Felix Schlesinger
M
Melanie Kirsche
D
David Bentley
M
Michael C. Schatz
F
Fritz J. Sedlazeck
M
Michael A. Eberle *
DOI:10.1186/s13059-019-1909-7delete
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摘要

摘要

En 中文
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.
Keyword:
Sequence graphs
Targeted variant calling
Structural variation
Population studies
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期刊

G
Genome Biology
IF:
9.4
论文数:
6.4K
被引数:
7.3W

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I
illumina
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1.1K
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被引数: 1
B
Baylor College of Medicine
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被引数: 4.2W
J
Johns Hopkins University
学者数:
10.2W
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