Linked-read analysis identifies mutations in single-cell DNA-sequencing data
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Updated: Jan 27, 2026

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
Published on: March 15, 2019
Craig L Bohrson1,2, Alison R Barton1,2, Michael A Lodato3,4,5
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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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