.png){kind=icon}
.png){kind=icon}
Return
Paragraph: a graph-based structural variant genotyper for short-read sequence data
.png){kind=icon}
OA
AI
DOI：10.1186/s13059-019-1909-7.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
Sequence graphs
Targeted variant calling
Structural variation
Population studies
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
G
IF:
9.4
Papers:
6.4K
Citations:
7.3W
View more organizations