Jasmine and Iris: population-scale structural variant comparison and analysis

Kirsche, Melanie, Prabhu, Gautam, Sherman, Rachel, Ni, Bohan, Battle, Alexis, Aganezov, Sergey, Schatz, Michael C (March 2023) Jasmine and Iris: population-scale structural variant comparison and analysis. Nature Methods, 20 (3). pp. 408-417. ISSN 1548-7091

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Abstract

The availability of long reads is revolutionizing studies of structural variants (SVs). However, because SVs vary across individuals and are discovered through imprecise read technologies and methods, they can be difficult to compare. Addressing this, we present Jasmine and Iris ( https://github.com/mkirsche/Jasmine/ ), for fast and accurate SV refinement, comparison and population analysis. Using an SV proximity graph, Jasmine outperforms six widely used comparison methods, including reducing the rate of Mendelian discordance in trio datasets by more than fivefold, and reveals a set of high-confidence de novo SVs confirmed by multiple technologies. We also present a unified callset of 122,813 SVs and 82,379 indels from 31 samples of diverse ancestry sequenced with long reads. We genotype these variants in 1,317 samples from the 1000 Genomes Project and the Genotype-Tissue Expression project with DNA and RNA-sequencing data and assess their widespread impact on gene expression, including within medically relevant genes.

Item Type:	Paper
Subjects:	bioinformatics > genomics and proteomics > analysis and processing bioinformatics bioinformatics > genomics and proteomics > genetics & nucleic acid processing bioinformatics > genomics and proteomics Investigative techniques and equipment Investigative techniques and equipment > assays bioinformatics > genomics and proteomics > genetics & nucleic acid processing > genomes
CSHL Authors:	Schatz, Michael C.
Communities:	CSHL labs > Schatz lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	March 2023
Date Deposited:	29 Sep 2023 18:50
Last Modified:	10 Jan 2024 19:56
PMCID:	PMC10006329
Related URLs:	Publisher
URI:	https://repository.cshl.edu/id/eprint/41080

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