Accurate detection of complex structural variations using single-molecule sequencing



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; and structural variant identification (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.

Author information

Author notes

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


  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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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.

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.

Corresponding authors

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

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Notes 1–5

  2. Reporting Summary

  3. Supplementary Table 1

    Raw statistics over the mapper evaluation

  4. Supplementary Table 2

    SV caller statistics over simulated reads

  5. Supplementary Table 3

    Mapping comparison over simulated reference and real reads

  6. Supplementary Table 4

    SV caller comparison over simulated reference and real reads

  7. Supplementary Table 5

    Used real datasets and accessions

  8. Supplementary Table 6

    GiaB trio comparison

  9. Supplementary Table 7

    Comparison of existing NA12878 datasets

  10. Supplementary Table 8

    NA12878 indel assessments using Illumina short-read data

  11. Supplementary Table 9

    Analysis of potential biases in short-read calling

  12. Supplementary Table 10

    Runtime comparisons over NA12878

  13. Supplementary Table 11

    Insertion and deletion assessment for simulated data