LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data

Li Fang, Charlly Kao, Michael V. Gonzalez, Fernanda A. Mafra, Renata Pellegrino da Silva, Mingyao Li, Sören-Sebastian Wenzel, Katharina Wimmer, Hakon Hakonarson and Kai Wang ()
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Li Fang: Children’s Hospital of Philadelphia
Charlly Kao: Children’s Hospital of Philadelphia
Michael V. Gonzalez: Children’s Hospital of Philadelphia
Fernanda A. Mafra: Children’s Hospital of Philadelphia
Renata Pellegrino da Silva: Children’s Hospital of Philadelphia
Mingyao Li: University of Pennsylvania
Sören-Sebastian Wenzel: Medical University of Innsbruck
Katharina Wimmer: Medical University of Innsbruck
Hakon Hakonarson: University of Pennsylvania
Kai Wang: Children’s Hospital of Philadelphia

Nature Communications, 2019, vol. 10, issue 1, 1-15

Abstract: Abstract Linked-read sequencing provides long-range information on short-read sequencing data by barcoding reads originating from the same DNA molecule, and can improve detection and breakpoint identification for structural variants (SVs). Here we present LinkedSV for SV detection on linked-read sequencing data. LinkedSV considers barcode overlapping and enriched fragment endpoints as signals to detect large SVs, while it leverages read depth, paired-end signals and local assembly to detect small SVs. Benchmarking studies demonstrate that LinkedSV outperforms existing tools, especially on exome data and on somatic SVs with low variant allele frequencies. We demonstrate clinical cases where LinkedSV identifies disease-causal SVs from linked-read exome sequencing data missed by conventional exome sequencing, and show examples where LinkedSV identifies SVs missed by high-coverage long-read sequencing. In summary, LinkedSV can detect SVs missed by conventional short-read and long-read sequencing approaches, and may resolve negative cases from clinical genome/exome sequencing studies.

Date: 2019
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DOI: 10.1038/s41467-019-13397-7

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