Dysgu: efficient structural variant calling using short or long reads

Cleal, Kez and Baird, Duncan ORCID: https://orcid.org/0000-0001-8408-5467 2022. Dysgu: efficient structural variant calling using short or long reads. Nucleic Acids Research 50 (9) , e53. 10.1093/nar/gkac039

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Abstract

Structural variation (SV) plays a fundamental role in genome evolution and can underlie inherited or acquired diseases such as cancer. Long-read sequencing technologies have led to improvements in the characterization of structural variants (SVs), although paired-end sequencing offers better scalability. Here, we present dysgu, which calls SVs or indels using paired-end or long reads. Dysgu detects signals from alignment gaps, discordant and supplementary mappings, and generates consensus contigs, before classifying events using machine learning. Additional SVs are identified by remapping of anomalous sequences. Dysgu outperforms existing state-of-the-art tools using paired-end or long-reads, offering high sensitivity and precision whilst being among the fastest tools to run. We find that combining low coverage paired-end and long-reads is competitive in terms of performance with long-reads at higher coverage values.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Medicine
Additional Information:	This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/)
Publisher:	Oxford University Press
ISSN:	0305-1048
Funders:	Cancer Research UK
Date of First Compliant Deposit:	14 January 2022
Date of Acceptance:	24 January 2022
Last Modified:	22 Jul 2024 16:13
URI:	https://orca.cardiff.ac.uk/id/eprint/146692

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