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Related Concept Videos

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Related Experiment Video

Updated: Jun 23, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Somatic variant detection in normal tissues from single-cell sequencing data.

Rui Luo, Ziyi Wang, Jinzhuang Dou

    Biorxiv : the Preprint Server for Biology
    |June 22, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study shows that single-cell sequencing can reliably detect rare somatic mutations in normal cells using computational tools like Monopogen. Single-nucleus ATAC-seq data proved particularly effective for variant calling and understanding cellular evolution.

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    Detection of Copy Number Alterations Using Single Cell Sequencing
    09:45

    Detection of Copy Number Alterations Using Single Cell Sequencing

    Published on: February 17, 2017

    Related Experiment Videos

    Last Updated: Jun 23, 2026

    Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
    11:02

    Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

    Published on: October 18, 2013

    Detection of Copy Number Alterations Using Single Cell Sequencing
    09:45

    Detection of Copy Number Alterations Using Single Cell Sequencing

    Published on: February 17, 2017

    Area of Science:

    • Genomics
    • Computational Biology
    • Molecular Biology

    Background:

    • Single-cell sequencing (SCS) is vital for identifying somatic variants in individual cells, aiding phylogenetic analysis of cellular populations.
    • Detecting somatic variants in normal tissues using SCS is challenging due to their rarity, unlike in tumor tissues.
    • Evaluating the feasibility of somatic variant calling from single-nucleus RNA-seq (snRNA-seq) and single-nucleus ATAC-seq (snATAC-seq) is crucial.

    Purpose of the Study:

    • To assess the capability of snRNA-seq and snATAC-seq data for somatic variant calling.
    • To compare the performance of computational tools Monopogen and SComatic for single-cell somatic variant detection.
    • To establish the feasibility of reliable single-cell somatic mutation calling in normal tissues.

    Main Methods:

    • Profiling a cell-line mix of six HapMap samples using 10x Genomics 5' snRNA-seq and snATAC-seq.
    • Utilizing PacBio long-read whole genome sequencing (WGS) data from individual cell lines as ground truth.
    • Applying computational tools Monopogen and SComatic for somatic variant calling and evaluating other methods (DeepVariant, Cellsnp-lite, Mutect2).

    Main Results:

    • Monopogen achieved high SNV detection accuracies (93.30% in snRNA-seq, 99.64% in snATAC-seq), outperforming SComatic.
    • Monopogen detected somatic SNVs at low cellular fractions (as low as 0.5%) and assigned variants to cells of origin with >80% accuracy.
    • snATAC-seq demonstrated broader genomic coverage and detected more variants compared to snRNA-seq.

    Conclusions:

    • Single-cell somatic mutation calling is feasible and reliable using snRNA-seq and snATAC-seq data.
    • Monopogen is a highly accurate tool for single-cell somatic variant calling, especially with snATAC-seq data.
    • This approach facilitates studies on clonal evolution and cell-type-specific mutagenesis in normal tissues.