Deciphering the exact breakpoints of structural variations using long sequencing reads with DeBreak

Yu Chen, Amy Y. Wang, Courtney A. Barkley, Yixin Zhang, Xinyang Zhao, Min Gao, Mick D. Edmonds and Zechen Chong ()
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Yu Chen: University of Alabama at Birmingham
Amy Y. Wang: University of Alabama at Birmingham
Courtney A. Barkley: University of Alabama at Birmingham
Yixin Zhang: University of Alabama at Birmingham
Xinyang Zhao: University of Alabama at Birmingham
Min Gao: University of Alabama at Birmingham
Mick D. Edmonds: University of Alabama at Birmingham
Zechen Chong: University of Alabama at Birmingham

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Long-read sequencing has demonstrated great potential for characterizing all types of structural variations (SVs). However, existing algorithms have insufficient sensitivity and precision. To address these limitations, we present DeBreak, a computational method for comprehensive and accurate SV discovery. Based on alignment results, DeBreak employs a density-based approach for clustering SV candidates together with a local de novo assembly approach for reconstructing long insertions. A partial order alignment algorithm ensures precise SV breakpoints with single base-pair resolution, and a k-means clustering method can report multi-allele SV events. DeBreak outperforms existing tools on both simulated and real long-read sequencing data from both PacBio and Nanopore platforms. An important application of DeBreak is analyzing cancer genomes for potentially tumor-driving SVs. DeBreak can also be used for supplementing whole-genome assembly-based SV discovery.

Date: 2023
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DOI: 10.1038/s41467-023-35996-1

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