Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Accurate detection of complex structural variations using single-molecule sequencing

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 1.

    Weischenfeldt, J., Symmons, O., Spitz, F. & Korbel, J. O. Phenotypic impact of genomic structural variation: insights from and for human disease. Nat. Rev. Genet. 14, 125–138 (2013).

    Article  PubMed  CAS  Google Scholar 

  2. 2.

    Lupski, J. R. Structural variation mutagenesis of the human genome: impact on disease and evolution. Environ. Mol. Mutagen. 56, 419–436 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. 3.

    Macintyre, G., Ylstra, B. & Brenton, J. D. Sequencing structural variants in cancer for precision therapeutics. Trends Genet. 32, 530–542 (2016).

    Article  PubMed  CAS  Google Scholar 

  4. 4.

    Hedges, D. J. et al. Evidence of novel fine-scale structural variation at autism spectrum disorder candidate loci. Mol. Autism 3, 2 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. 5.

    Rovelet-Lecrux, A. et al. APP locus duplication causes autosomal dominant early-onset Alzheimer disease with cerebral amyloid angiopathy. Nat. Genet. 38, 24–26 (2006).

    Article  PubMed  CAS  Google Scholar 

  6. 6.

    Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. 7.

    Dennenmoser, S. et al. Copy number increases of transposable elements and protein-coding genes in an invasive fish of hybrid origin. Mol. Ecol. 26, 4712–4724 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. 8.

    Jeffares, D. C. et al. Transient structural variations have strong effects on quantitative traits and reproductive isolation in fission yeast. Nat. Commun. 8, 14061 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. 9.

    Zichner, T. et al. Impact of genomic structural variation in Drosophila melanogaster based on population-scale sequencing. Genome Res. 23, 568–579 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. 10.

    Imprialou, M. et al. Genomic rearrangements in Arabidopsis considered as quantitative traits. Genetics 205, 1425–1441 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. 11.

    Sebat, J. et al. Large-scale copy number polymorphism in the human genome. Science 305, 525–528 (2004).

    Article  PubMed  CAS  Google Scholar 

  12. 12.

    Kadalayil, L. et al. Exome sequence read depth methods for identifying copy number changes. Brief. Bioinform. 16, 380–392 (2015).

    Article  PubMed  CAS  Google Scholar 

  13. 13.

    Alkan, C., Coe, B. P. & Eichler, E. E. Genome structural variation discovery and genotyping. Nat. Rev. Genet. 12, 363–376 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. 14.

    Layer, R. M., Chiang, C., Quinlan, A. R. & Hall, I. M. LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 15, R84 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. 16.

    Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  17. 17.

    Huddleston, J. et al. Discovery and genotyping of structural variation from long-read haploid genome sequence data. Genome Res. 27, 677–685 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. 18.

    English, A. C., Salerno, W. J. & Reid, J. G. PBHoney: identifying genomic variants via long-read discordance and interrupted mapping. BMC Bioinformatics 15, 180 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. 19.

    Mills, R. E. et al. Mapping copy number variation by population-scale genome sequencing. Nature 470, 59–65 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. 20.

    Tattini, L., D’Aurizio, R. & Magi, A. Detection of genomic structural variants from next-generation sequencing data. Front. Bioeng. Biotechnol 3, 92 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Teo, S. M., Pawitan, Y., Ku, C. S., Chia, K. S. & Salim, A. Statistical challenges associated with detecting copy number variations with next-generation sequencing. Bioinformatics 28, 2711–2718 (2012).

    Article  PubMed  CAS  Google Scholar 

  22. 22.

    Lucas Lledó, J. I. & Cáceres, M. On the power and the systematic biases of the detection of chromosomal inversions by paired-end genome sequencing. PLoS One 8, e61292 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. 23.

    Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

    Article  PubMed  CAS  Google Scholar 

  24. 24.

    Kiełbasa, S. M., Wan, R., Sato, K., Horton, P. & Frith, M. C. Adaptive seeds tame genomic sequence comparison. Genome Res. 21, 487–493 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. 25.

    Chaisson, M. J. & Tesler, G. Mapping single molecule sequencing reads using basic local alignment with successive refinement (BLASR): application and theory. BMC Bioinformatics 13, 238 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. 26.

    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv Preprint at https://arxiv.org/abs/1303.3997 (2013).

  27. 27.

    Sović, I. et al. Fast and sensitive mapping of nanopore sequencing reads with GraphMap. Nat. Commun. 7, 11307 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. 28.

    Xiao, C. L. et al. MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads. Nat. Methods 14, 1072–1074 (2017).

    Article  PubMed  CAS  Google Scholar 

  29. 29.

    Li, H. Minimap2: fast pairwise alignment for long nucleotide sequences. arXiv Preprint at https://arxiv.org/abs/1708.01492 (2017).

  30. 30.

    Chaisson, M. J. et al. Resolving the complexity of the human genome using single-molecule sequencing. Nature 517, 608–611 (2015).

    Article  PubMed  CAS  Google Scholar 

  31. 31.

    Sedlazeck, F. J., Rescheneder, P. & von Haeseler, A. NextGenMap: fast and accurate read mapping in highly polymorphic genomes. Bioinformatics 29, 2790–2791 (2013).

    Article  PubMed  CAS  Google Scholar 

  32. 32.

    Carvalho, C. M. et al. Inverted genomic segments and complex triplication rearrangements are mediated by inverted repeats in the human genome. Nat. Genet. 43, 1074–1081 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. 33.

    Shimojima, K. et al. Pelizaeus-Merzbacher disease caused by a duplication-inverted triplication-duplication in chromosomal segments including the PLP1 region. Eur. J. Med. Genet. 55, 400–403 (2012).

    Article  PubMed  Google Scholar 

  34. 34.

    Carvalho, C. M. & Lupski, J. R. Mechanisms underlying structural variant formation in genomic disorders. Nat. Rev. Genet. 17, 224–238 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. 35.

    Mühle, C., Zenker, M., Chuzhanova, N. & Schneider, H. Recurrent inversion with concomitant deletion and insertion events in the coagulation factor VIII gene suggests a new mechanism for X-chromosomal rearrangements causing hemophilia A. Hum. Mutat. 28, 1045 (2007).

    Article  PubMed  Google Scholar 

  36. 36.

    Gusfield, D. Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology (Cambridge Univ. Press, Cambridge, UK, 1997).

  37. 37.

    Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. 38.

    Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. 39.

    Zook, J. M. et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci. Data 3, 160025 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. 40.

    Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat. Biotechnol. 36, 338–345 (2018).

  41. 41.

    Zook, J. M. et al. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat. Biotechnol. 32, 246–251 (2014).

    Article  PubMed  CAS  Google Scholar 

  42. 42.

    Eberle, M. A. et al. A reference data set of 5.4 million phased human variants validated by genetic inheritance from sequencing a three-generation 17-member pedigree. Genome Res. 27, 157–164 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. 43.

    Zimin, A. V., Smith, D. R., Sutton, G. & Yorke, J. A. Assembly reconciliation. Bioinformatics 24, 42–45 (2008).

    Article  PubMed  CAS  Google Scholar 

  44. 44.

    Beri, S., Bonaglia, M. C. & Giorda, R. Low-copy repeats at the human VIPR2 gene predispose to recurrent and nonrecurrent rearrangements. Eur. J. Hum. Genet. 21, 757–761 (2013).

    Article  PubMed  CAS  Google Scholar 

  45. 45.

    Nattestad, M. et al. Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line. bioRxiv Preprint at https://www.biorxiv.org/content/early/2017/08/10/174938 (2017).

  46. 46.

    Merker, J. D. et al. Long-read genome sequencing identifies causal structural variation in a Mendelian disease. Genet. Med. 20, 159–163 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. 47.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. 48.

    Jeffares, D. C. et al. Transient structural variations alter gene expression and quantitative traits in Schizosaccharomyces pombe. Nat. Commun. 8, 14061 (2017).

Download references

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.

Author information

Affiliations

Authors

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.

Ethics declarations

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.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing