Ultrafast prediction of somatic structural variations by filtering out reads matched to pan-genome k-mer sets
- Authors
- Sohn, Jang-Il; Choi, Min-Hak; Yi, Dohun; Menon, Vipin A.; Kim, Yeon Jeong; Lee, Junehawk; Park, Jung Woo; Kyung, Sungkyu; Shin, Seung-Ho; Na, Byunggook; Joung, Je-Gu; Ju, Young Seok; Yeom, Min Sun; Koh, Youngil; Yoon, Sung-Soo; Baek, Daehyun; Kim, Tae-Min; Nam, Jin-Wu
- Issue Date
- Jul-2023
- Publisher
- Nature Research
- Citation
- Nature Biomedical Engineering, v.7, no.7, pp.853 - 866
- Indexed
- SCIE
SCOPUS
- Journal Title
- Nature Biomedical Engineering
- Volume
- 7
- Number
- 7
- Start Page
- 853
- End Page
- 866
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/189001
- DOI
- 10.1038/s41551-022-00980-5
- ISSN
- 2157-846X
- Abstract
- Variant callers typically produce massive numbers of false positives for structural variations, such as cancer-relevant copy-number alterations and fusion genes resulting from genome rearrangements. Here we describe an ultrafast and accurate detector of somatic structural variations that reduces read-mapping costs by filtering out reads matched to pan-genome k-mer sets. The detector, which we named ETCHING (for efficient detection of chromosomal rearrangements and fusion genes), reduces the number of false positives by leveraging machine-learning classifiers trained with six breakend-related features (clipped-read count, split-reads count, supporting paired-end read count, average mapping quality, depth difference and total length of clipped bases). When benchmarked against six callers on reference cell-free DNA, validated biomarkers of structural variants, matched tumour and normal whole genomes, and tumour-only targeted sequencing datasets, ETCHING was 11-fold faster than the second-fastest structural-variant caller at comparable performance and memory use. The speed and accuracy of ETCHING may aid large-scale genome projects and facilitate practical implementations in precision medicine.
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