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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LiRA is available at https://github.com/parklab/LiRA.
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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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).
The authors declare no competing interests.
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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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