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

Chen, S., Krusche, P., Dolzhenko, E., Sherman, R. M., Petrovski, R., Schlesinger, F., Kirsche, M., Bentley, D. R., Schatz, M. C., Sedlazeck, F. J., Eberle, M. A. (December 2019) Paragraph: A graph-based structural variant genotyper for short-read sequence data. Genome Biology, 20 (1). Article 291. ISSN 14747596 (ISSN)

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DOI: 10.1186/s13059-019-1909-7


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. © 2019 The Author(s).

Item Type: Paper
Subjects: bioinformatics
Investigative techniques and equipment
bioinformatics > computational biology > algorithms
Investigative techniques and equipment > assays
bioinformatics > computational biology
Investigative techniques and equipment > assays > whole genome sequencing
CSHL Authors:
Communities: CSHL labs > Schatz lab
Depositing User: Adrian Gomez
Date: 19 December 2019
Date Deposited: 06 Jan 2020 18:34
Last Modified: 01 Feb 2024 20:58
PMCID: PMC6921448
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