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

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

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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.

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

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).

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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).

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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

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  1. Craig L. Bohrson
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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.

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Correspondence to Peter J. Park.

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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

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