| Autor: |
Jang-il Sohn, Min-Hak Choi, Dohun Yi, Vipin A. Menon, Yeon Jeong Kim, Junehawk Lee, Jung Woo Park, Sungkyu Kyung, Seung-Ho Shin, Byunggook Na, Je-Gun Joung, Young Seok Ju, Min Sun Yeom, Youngil Koh, Sung-Soo Yoon, Daehyun Baek, Tae-Min Kim, Jin-Wu Nam |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Nature biomedical engineering. |
| ISSN: |
2157-846X |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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