Title | Paragraph: a graph-based structural variant genotyper for short-read sequence data. |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | Chen, S, Krusche, P, Dolzhenko, E, Sherman, RM, Petrovski, R, Schlesinger, F, Kirsche, M, Bentley, DR, Schatz, MC, Sedlazeck, FJ, Eberle, MA |
Journal | Genome Biol |
Volume | 20 |
Issue | 1 |
Pagination | 291 |
Date Published | 2019 Dec 19 |
ISSN | 1474-760X |
Keywords | Genome, Human, Genomic Structural Variation, Genotyping Techniques, Humans |
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. |
DOI | 10.1186/s13059-019-1909-7 |
Alternate Journal | Genome Biol |
PubMed ID | 31856913 |
PubMed Central ID | PMC6921448 |
Grant List | R01-HG006677 / / Foundation for the National Institutes of Health / International UM1-HG008898 / / Foundation for the National Institutes of Health / International |
Paragraph: a graph-based structural variant genotyper for short-read sequence data.
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