期刊
NATURE METHODS
卷 15, 期 6, 页码 461-+出版社
NATURE PORTFOLIO
DOI: 10.1038/s41592-018-0001-7
关键词
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资金
- National Science Foundation [DBI-1350041, IOS-1732253, IOS-1445025]
- US National Institutes of Health [R01-HG006677, UM1 HG008898]
- DK RNA Biology [W1207-B09]
- University of Vienna
- Medical University of Vienna
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1627442] Funding Source: National Science Foundation
Structural variations are the greatest source of genetic variation, but they remain poorly understood because of technological limitations. Single-molecule long-read sequencing has the potential to dramatically advance the field, although high error rates are a challenge with existing methods. Addressing this need, we introduce open-source methods for long-read alignment (NGMLR;https://github.com/philres/ngmir) and structural variant identification (Sniffles;https://github.com/fritzsedlazeck/Sniffles) that provide unprecedented sensitivity and precision for variant detection, even in repeat-rich regions and for complex nested events that can have substantial effects on human health. In several long-read datasets, including healthy and cancerous human genomes, we discovered thousands of novel variants and categorized systematic errors in short-read approaches. NGMLR and Sniffles can automatically filter false events and operate on low-coverage data, thereby reducing the high costs that have hindered the application of long reads in clinical and research settings.
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