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

Craig L Bohrson; Alison R Barton; Michael A Lodato; Rachel E Rodin; Lovelace J Luquette; Vinay V Viswanadham; Doga C Gulhan; Isidro Cortés-Ciriano; Maxwell A Sherman; Minseok Kwon; Michael E Coulter; Alon Galor; Christopher A Walsh; Peter J Park

doi:10.1038/s41588-019-0366-2

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

Nat Genet. 2019 Apr;51(4):749-754. doi: 10.1038/s41588-019-0366-2. Epub 2019 Mar 18.

Authors

Craig L Bohrson^{1

2}, Alison R Barton^{1

2}, Michael A Lodato^{3

4

5}, Rachel E Rodin^{3

4

5

6}, Lovelace J Luquette^{1

2}, Vinay V Viswanadham^{1

2}, Doga C Gulhan¹, Isidro Cortés-Ciriano^{1

7}, Maxwell A Sherman¹, Minseok Kwon¹, Michael E Coulter^{3

4

5

6}, Alon Galor¹, Christopher A Walsh^{3

4

5}, Peter J Park⁸

Affiliations

¹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
² Bioinformatics and Integrative Genomics PhD Program, Harvard Medical School, Boston, MA, USA.
³ Division of Genetics and Genomics, Manton Center for Orphan Disease, and Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, USA.
⁴ Departments of Neurology and Pediatrics, Harvard Medical School, Boston, MA, USA.
⁵ Broad Institute, Cambridge, MA, USA.
⁶ Program in Neuroscience and Harvard/MIT MD-PHD Program, Harvard Medical School, Boston, MA, USA.
⁷ Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.
⁸ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. peter_park@hms.harvard.edu.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

DNA Mutational Analysis / methods
Heterozygote
Humans
Mutation / genetics*
Mutation Rate
Polymorphism, Single Nucleotide / genetics
Sequence Analysis, DNA / methods
Single-Cell Analysis / methods
Whole Genome Sequencing / methods

Abstract

Publication types

MeSH terms

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