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Updated: Sep 6, 2025

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

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

Published on: October 18, 2013

Somatic variant calling from single-cell DNA sequencing data.

Monica Valecha^1,2, David Posada^1,2,3

¹CINBIO, Universidade de Vigo, 36310 Vigo, Spain.

Computational and Structural Biotechnology Journal

|July 5, 2022

Summary

This summary is machine-generated.

Single-cell DNA sequencing is prone to errors, making variant identification difficult. This review examines current single-cell variant calling tools, highlighting their pros and cons for researchers.

Keywords:

ADO, allelic dropout Allele dropout Amplification error CNV, copy number variant Indel, short insertion or deletion LDO, locus dropout SNV, single nucleotide variant SV, structural variant Single-cell genomics Somatic variants VAF, variant allele frequency Variant calling hSNP, heterozygous single-nucleotide polymorphism scATAC-seq, single-cell sequencing assay for transposase-accessible chromatin scDNA-seq, single-cell DNA sequencing scHi-C, single-cell Hi-C sequencing scMethyl-seq, single-cell Methylation sequencing scRNA-seq, single-cell RNA sequencing scWGA, single-cell whole-genome amplification

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Last Updated: Sep 6, 2025

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

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Published on: October 18, 2013

Detection of Copy Number Alterations Using Single Cell Sequencing

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Published on: February 17, 2017

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

Genomics
Bioinformatics

Background:

Single-cell sequencing is increasingly used but generates error-prone DNA data.
Technical biases like uneven coverage and allelic dropout complicate variant identification.

Purpose of the Study:

To review existing methods for single-cell variant calling, focusing on single nucleotide variants.
To provide guidance for selecting appropriate tools based on data characteristics.

Main Methods:

Literature review of single-cell variant calling algorithms.
Analysis of strategies and data utilization by different callers.

Main Results:

Single-cell variant callers employ diverse strategies, leading to discordant results on real data.
Existing methods have distinct benefits and limitations.

Conclusions:

Choosing the right variant caller is crucial for accurate analysis of single-cell DNA sequencing data.
Understanding tool performance is essential for effective genomic variant identification.