LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data
| Autor: | Sören-Sebastian Wenzel, Kai Wang, Katharina Wimmer, Li Fang, Renata Pellegrino da Silva, Charlly Kao, Hakon Hakonarson, Fernanda Abani Mafra, Mingyao Li, Michael Gonzalez |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Computer science Science DNA Mutational Analysis Sequencing data General Physics and Astronomy Computational biology Biology Genome informatics Barcode Genome Article General Biochemistry Genetics and Molecular Biology DNA sequencing law.invention Genomic analysis Chromosome Breakpoints 03 medical and health sciences 0302 clinical medicine law Sequencing Humans Exome Repeated sequence Indel lcsh:Science Exome sequencing Sequence Deletion Neurofibromin 1 Multidisciplinary Base Sequence Models Genetic Genome Human Breakpoint Variant allele Sequence Analysis DNA General Chemistry 030104 developmental biology 030220 oncology & carcinogenesis Genomic Structural Variation lcsh:Q Software |
| Zdroj: | Nature Communications, Vol 10, Iss 1, Pp 1-15 (2019) Nature Communications |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-019-13397-7 |
| Popis: | 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. Compared to single nucleotide variants and short indels, structural variants (SVs) are often more challenging to detect using high-throughput sequencing based methods. Here, the authors develop LinkedSV, a computational tool for SV detection using linked-read exome and genome sequencing data. |
| Databáze: | OpenAIRE |
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