Single-Nucleotide Variant Calling In Single-Cell Sequencing Data With Monopogen

Authors

Jinzhuang Dou
Yukun Tan
Kian Hong Kock
Jun Wang
Xuesen Cheng
Le Min Tan
Kyung Yeon Han
Chung-Chau Hon
Woong-Yang Park
Jay W Shin
Haijing Jin
Yujia Wang
Han Chen
Li Ding
Shyam Prabhakar
Nicholas Navin
Rui Chen
Ken Chen

Publication Date

5-1-2024

Journal

Nature Biotechnology

Abstract

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.

Keywords

Single-Cell Analysis, Humans, Polymorphism, Single Nucleotide, High-Throughput Nucleotide Sequencing, Linkage Disequilibrium, Software, Computational Biology, Genotype

Comments

Supplementary Materials

Data Availability Statement

PMID: 37592035