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Accurate detection of complex structural variations using single-molecule sequencing

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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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Fig. 1: The main steps implemented in NGMLR and Sniffles.
Fig. 2: Improved alignment by NGMLR for a 228-bp deletion and a 150-bp inversion.
Fig. 3: Tool evaluation with simulated data.
Fig. 4: Systematic error in short-read-based SV calling.
Fig. 5: Nested SVs in the SKBR3 cancer cell line.
Fig. 6: Dependence of SV detection accuracy on the level of coverage.

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Acknowledgements

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.

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Author notes

  1. These authors contributed equally: Fritz J. Sedlazeck, Philipp Rescheneder.

Authors and Affiliations

  1. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    Fritz J. Sedlazeck

  2. Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Vienna, Austria

    Philipp Rescheneder, Moritz Smolka & Arndt von Haeseler

  3. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Han Fang, Maria Nattestad & Michael C. Schatz

  4. Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria

    Arndt von Haeseler

  5. Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA

    Michael C. Schatz

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Contributions

F.J.S., P.R., and M.S. developed the software. F.J.S., P.R., M.S., H.F., and M.N. performed analysis. F.J.S., P.R., M.C.S., and A.v.H. wrote the manuscript. M.C.S. and A.v.H. directed the project. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Fritz J. Sedlazeck or Michael C. Schatz.

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Competing interests

M.C.S. and F.J.S. have participated in PacBio-sponsored meetings over the past few years and have received travel reimbursement and honoraria for presenting at these events. Since the initial submission of this paper, P.R. has become an employee of Oxford Nanopore. PacBio and Oxford Nanopore had no role in decisions related to the study/work, data collection, or analysis of data described in this paper.

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Supplementary information

Supplementary Text and Figures

Supplementary Notes 1–5

Reporting Summary

Supplementary Table 1

Raw statistics over the mapper evaluation

Supplementary Table 2

SV caller statistics over simulated reads

Supplementary Table 3

Mapping comparison over simulated reference and real reads

Supplementary Table 4

SV caller comparison over simulated reference and real reads

Supplementary Table 5

Used real datasets and accessions

Supplementary Table 6

GiaB trio comparison

Supplementary Table 7

Comparison of existing NA12878 datasets

Supplementary Table 8

NA12878 indel assessments using Illumina short-read data

Supplementary Table 9

Analysis of potential biases in short-read calling

Supplementary Table 10

Runtime comparisons over NA12878

Supplementary Table 11

Insertion and deletion assessment for simulated data

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Sedlazeck, F.J., Rescheneder, P., Smolka, M. et al. Accurate detection of complex structural variations using single-molecule sequencing. Nat Methods 15, 461–468 (2018). https://doi.org/10.1038/s41592-018-0001-7

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