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Nat Methods. 2018 Jun;15(6):461-468. doi: 10.1038/s41592-018-0001-7. Epub 2018 Apr 30.

Accurate detection of complex structural variations using single-molecule sequencing.

Author information

1
Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA. fritz.sedlazeck@bcm.edu.
2
Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Vienna, Austria.
3
Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
4
Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria.
5
Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. mschatz@cs.jhu.edu.
6
Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA. mschatz@cs.jhu.edu.

Abstract

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/ngmlr ) 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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