Single-nucleotide variant calling in single-cell sequencing data with Monopogen

Monopogen identifies single-nucleotide variants in single-cell sequencing data. Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

Automated summary

Monopogen is a computational tool for identifying single-nucleotide variants (SNVs) from single-cell sequencing data. It can accurately identify 100K to 3M germline SNVs with a genotyping accuracy of 95%, as well as hundreds of putative somatic SNVs. Monopogen enables global and local ancestry inference, identification of admixed samples, and detection of variants associated with cellular processes such as cardiomyocyte metabolism and epigenomic programs. It also improves the detection of putative somatic SNVs and enables clonal lineage tracing in primary human clonal hematopoiesis.