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

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. Linked-read analysis is a method for analyzing single-cell DNA-sequencing data that accurately identifies somatic single-nucleotide variants by using read-level phasing with nearby germline variants, enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.