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.

  • Technical Report
  • Published:

Linked-read analysis identifies mutations in single-cell DNA-sequencing data

Nature Genetics volume 51, pages 749–754 (2019)Cite this article

Subjects

Abstract

Whole-genome sequencing of DNA from single cells has the potential to reshape our understanding of mutational heterogeneity in normal and diseased tissues. However, a major difficulty is distinguishing amplification artifacts from biologically derived somatic mutations. Here, we describe linked-read analysis (LiRA), a method that accurately identifies somatic single-nucleotide variants (sSNVs) by using read-level phasing with nearby germline heterozygous polymorphisms, thereby enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of LiRA.
Fig. 2: Performance of LiRA compared to other calling methods.

Similar content being viewed by others

Code availability

LiRA is available at https://github.com/parklab/LiRA.

Data availability

LiRA was applied to single-neuron and bulk sequencing data collected from the postmortem brain, heart (UMB1465 and UMB4638), and liver (UMB4643) tissue of three individuals. These data were acquired as part of a previous study5 and are available in the NCBI SRA under accession nos. SRP041470 (UMB1465) and SRP061939 (UMB4638 and UMB4643). The neuron counts by individual were: UMB1465 (16); UMB4638 (10); and UMB4643 (10).

References

  1. Leung, M. L., Wang, Y., Waters, J. & Navin, N. E. SNES: single nucleus exome sequencing. Genome Biol. 16, 55 (2015).

    Article  Google Scholar 

  2. Xu, X. et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886–895 (2012).

    Article  CAS  Google Scholar 

  3. Hou, Y. et al. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell 148, 873–885 (2012).

    Article  CAS  Google Scholar 

  4. Baslan, T. et al. Genome-wide copy number analysis of single cells. Nat. Protoc. 7, 1024–1041 (2012).

    Article  CAS  Google Scholar 

  5. Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).

    Article  CAS  Google Scholar 

  6. Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

    Article  CAS  Google Scholar 

  7. Zong, C., Lu, S., Chapman, A. R. & Xie, X. S. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338, 1622–1626 (2012).

    Article  CAS  Google Scholar 

  8. Dean, F. B. et al. Comprehensive human genome amplification using multiple displacement amplification. Proc. Natl Acad. Sci. USA 99, 5261–5266 (2002).

    Article  CAS  Google Scholar 

  9. Chen, C. et al. Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI). Science 356, 189–194 (2017).

    Article  CAS  Google Scholar 

  10. Huang, L., Ma, F., Chapman, A., Lu, S. & Xie, X. S. Single-cell whole-genome amplification and sequencing: methodology and applications. Annu. Rev. Genomics Hum. Genet. 16, 79–102 (2015).

    Article  CAS  Google Scholar 

  11. Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).

    Article  CAS  Google Scholar 

  12. de Bourcy, C. F. A. et al. A quantitative comparison of single-cell whole genome amplification methods. PLoS ONE 9, e105585 (2014).

    Article  Google Scholar 

  13. Esteban, J. A., Salas, M. & Blanco, L. Fidelity of phi 29 DNA polymerase: comparison between protein-primed initiation and DNA polymerization. J. Biol. Chem. 268, 2719–2726 (1993).

    CAS  PubMed  Google Scholar 

  14. Fryxell, K. J. & Zuckerkandl, E. Cytosine deamination plays a primary role in the evolution of mammalian isochores. Mol. Biol. Evol. 17, 1371–1383 (2000).

    Article  CAS  Google Scholar 

  15. Lindahl, T. & Nyberg, B. Heat-induced deamination of cytosine residues in deoxyribonucleic acid. Biochemistry 13, 3405–3410 (1974).

    Article  CAS  Google Scholar 

  16. Frederico, L. A., Kunkel, T. A. & Shaw, B. R. A sensitive genetic assay for the detection of cytosine deamination: determination of rate constants and the activation energy. Biochemistry 29, 2532–2537 (1990).

    Article  CAS  Google Scholar 

  17. Usuyama, N. et al. HapMuC: somatic mutation calling using heterozygous germ line variants near candidate mutations. Bioinformatics 30, 3302–3309 (2014).

    Article  CAS  Google Scholar 

  18. Freed, D. & Pevsner, J. The contribution of mosaic variants to autism spectrum disorder. PLoS Genet. 12, e1006245 (2016).

    Article  Google Scholar 

  19. Ju, Y. S. et al. Somatic mutations reveal asymmetric cellular dynamics in the early human embryo. Nature 543, 714–718 (2017).

    Article  CAS  Google Scholar 

  20. Ramu, A. et al. DeNovoGear: de novo indel and point mutation discovery and phasing. Nat. Methods 10, 985–987 (2013).

    Article  CAS  Google Scholar 

  21. Dong, X. et al. Accurate identification of single-nucleotide variants in whole-genome-amplified single cells. Nat. Methods 14, 491–493 (2017).

    Article  CAS  Google Scholar 

  22. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  Google Scholar 

  23. Hoang, M. L. et al. Genome-wide quantification of rare somatic mutations in normal human tissues using massively parallel sequencing. Proc. Natl Acad. Sci. USA 113, 9846–9851 (2016).

    Article  CAS  Google Scholar 

  24. Zafar, H., Wang, Y., Nakhleh, L., Navin, N. & Chen, K. Monovar: single-nucleotide variant detection in single cells. Nat. Methods 13, 505–507 (2016).

    Article  CAS  Google Scholar 

  25. Koboldt, D. C. et al. VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 25, 2283–2285 (2009).

    Article  CAS  Google Scholar 

  26. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    Article  CAS  Google Scholar 

  27. Chen, L., Liu, P., Evans, T. C. Jr. & Ettwiller, L. M. DNA damage is a pervasive cause of sequencing errors, directly confounding variant identification. Science 355, 752–756 (2017).

    Article  CAS  Google Scholar 

  28. Lodato, M. A. et al. Aging and neurodegeneration are associated with increased mutations in single human neurons. Science 359, 555–559 (2018).

    Article  CAS  Google Scholar 

  29. Li, Y. et al. Single-cell sequencing analysis characterizes common and cell-lineage-specific mutations in a muscle-invasive bladder cancer. Gigascience 1, 12 (2012).

    Article  Google Scholar 

  30. Ho, J. R. et al. Deregulation of Rab and Rab effector genes in bladder cancer. PLoS ONE 7, e39469 (2012).

    Article  CAS  Google Scholar 

  31. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  32. Delaneau, O. et al. Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel. Nat. Commun. 5, 3934 (2014).

    Article  CAS  Google Scholar 

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

Download references

Acknowledgements

This work was mainly supported by the training grant in Bioinformatics and Integrative Genomics from the National Human Genome Research Institute (grant no. T32HG002295 to C.L.B., A.R.B., L.J.L., and V.V.), a Brain Somatic Mosaicism Network grant from the National Institute of Mental Health (grant no. U01MH106883 to P.J.P., C.A.W.), and Ludwig Center at Harvard Medical School (P.J.P.). I.C.-C. received funding from the European Union (Marie Curie Skłodowska-Curie grant agreement no. 703543).

Author information

Authors and Affiliations

  1. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Craig L. Bohrson, Alison R. Barton, Lovelace J. Luquette, Vinay V. Viswanadham, Doga C. Gulhan, Isidro Cortés-Ciriano, Maxwell A. Sherman, Minseok Kwon, Alon Galor & Peter J. Park

  2. Bioinformatics and Integrative Genomics PhD Program, Harvard Medical School, Boston, MA, USA

    Craig L. Bohrson, Alison R. Barton, Lovelace J. Luquette & Vinay V. Viswanadham

  3. Division of Genetics and Genomics, Manton Center for Orphan Disease, and Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, USA

    Michael A. Lodato, Rachel E. Rodin, Michael E. Coulter & Christopher A. Walsh

  4. Departments of Neurology and Pediatrics, Harvard Medical School, Boston, MA, USA

    Michael A. Lodato, Rachel E. Rodin, Michael E. Coulter & Christopher A. Walsh

  5. Broad Institute, Cambridge, MA, USA

    Michael A. Lodato, Rachel E. Rodin, Michael E. Coulter & Christopher A. Walsh

  6. Program in Neuroscience and Harvard/MIT MD-PHD Program, Harvard Medical School, Boston, MA, USA

    Rachel E. Rodin & Michael E. Coulter

  7. Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK

    Isidro Cortés-Ciriano

Authors
  1. Craig L. Bohrson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alison R. Barton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael A. Lodato
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rachel E. Rodin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lovelace J. Luquette
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Vinay V. Viswanadham
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Doga C. Gulhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Isidro Cortés-Ciriano
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Maxwell A. Sherman
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Minseok Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Michael E. Coulter
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Alon Galor
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Christopher A. Walsh
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Peter J. Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.L.B. and M.A.L. conceived the project and P.J.P. supervised it. C.L.B. developed the algorithm. C.L.B., A.R.B., M.K., and A.G. generated the alignment and performed the variant calling. A.R.B., M.A.L., R.E.R., L.J.L., V.V., D.C.G., I.C.-C., M.A.S., M.K., M.E.C., and C.A.W. suggested impactful improvements to LiRA and aided in evaluating its performance. C.L.B. wrote the manuscript supervised by P.J.P., with input from all other authors.

Corresponding author

Correspondence to Peter J. Park.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bohrson, C.L., Barton, A.R., Lodato, M.A. et al. Linked-read analysis identifies mutations in single-cell DNA-sequencing data. Nat Genet 51, 749–754 (2019). https://doi.org/10.1038/s41588-019-0366-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-019-0366-2

This article is cited by

Search

Advanced 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