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

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

Author information

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.

PMID:
30886424
DOI:
10.1038/s41588-019-0366-2
[Indexed for MEDLINE]

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