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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We thank W.R. McCombie, S. Wheelan, S. Goodwin, H. Li, and B.Q. Minh for helpful discussions. This work was supported by the National Science Foundation (DBI- 1350041, IOS-1732253, and IOS-1445025 to M.C.S.) and the US National Institutes of Health (R01-HG006677 and UM1 HG008898 to M.C.S. and F.J.S.). P.R. acknowledges support from DK RNA Biology (W1207-B09). A.v.H. and M.S. acknowledge financial support from the University of Vienna and the Medical University of Vienna.
Supplementary Notes 1–5
Raw statistics over the mapper evaluation
SV caller statistics over simulated reads
Mapping comparison over simulated reference and real reads
SV caller comparison over simulated reference and real reads
Used real datasets and accessions
GiaB trio comparison
Comparison of existing NA12878 datasets
NA12878 indel assessments using Illumina short-read data
Analysis of potential biases in short-read calling
Runtime comparisons over NA12878
Insertion and deletion assessment for simulated data
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