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Detection of Copy Number Alterations Using Single Cell Sequencing
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Single-nucleotide variant calling in single-cell sequencing data with Monopogen.

Jinzhuang Dou1, Yukun Tan1, Kian Hong Kock2

  • 1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Nature Biotechnology
|August 17, 2023
PubMed
Summary
This summary is machine-generated.

Monopogen is a new tool that identifies genetic variations in single cells. It helps understand how genetic background influences cell behavior and enables lineage tracing for various applications.

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Area of Science:

  • Genomics and Bioinformatics
  • Single-cell Omics
  • Population Genetics

Background:

  • Single-cell omics technologies provide deep molecular insights into cellular diversity.
  • The influence of a cell's genetic background on its transcriptional and epigenetic profiles is not well understood.
  • Accurate detection of genetic variants from single-cell data is crucial for comprehensive analysis.

Purpose of the Study:

  • To introduce Monopogen, a computational tool for detecting single-nucleotide variants (SNVs) in single-cell sequencing data.
  • To enable the study of genetic determinants underlying cellular processes and variation.
  • To integrate population genetics and cell lineage tracing with single-cell omics.

Main Methods:

  • Monopogen utilizes linkage disequilibrium from external reference panels to identify germline SNVs.
  • It detects putative somatic SNVs by analyzing allele cosegregation patterns at the cell population level.
  • The tool processes single-cell sequencing data to genotype variants.

Main Results:

  • Monopogen identifies 100,000 to 3 million germline SNVs with 95% genotyping accuracy.
  • Hundreds of putative somatic SNVs are detected, facilitating clonal lineage tracing.
  • Monopogen-derived genotypes enable ancestry inference and identification of admixed samples, and reveal variants associated with cardiomyocyte metabolism and epigenomic programs.

Conclusions:

  • Monopogen effectively detects both germline and somatic single-nucleotide variants from single-cell sequencing data.
  • The tool enhances the capabilities of single-cell omics by integrating genetic background information.
  • Monopogen provides a powerful platform for uncovering genetic influences on cellular phenotypes and tracing cell lineages.