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Comparing Copy Number Variations and SNPs

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: Feb 23, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Evaluation of three read-depth based CNV detection tools using whole-exome sequencing data.

Ruen Yao¹, Cheng Zhang², Tingting Yu¹

¹Department of Medical Genetics and Molecular Diagnostic Laboratory, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai, 200127 China.

Molecular Cytogenetics

|August 31, 2017

Summary

Whole exome sequencing (WES) shows promise for detecting copy number variants (CNVs), but current read-depth algorithms have low sensitivity and specificity compared to chromosomal microarray analysis (CMA). Further development is needed for reliable WES-based CNV detection in clinical settings.

Keywords:

Clinical sequencing Copy number variants Structural variation Whole exome sequencing

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Published on: October 18, 2013

Area of Science:

Genomics
Bioinformatics
Clinical Genetics

Background:

Whole exome sequencing (WES) is a cost-effective method for small variant detection.
Copy number variant (CNV) detection from WES data is evolving using read-depth algorithms.
Chromosomal microarray analysis (CMA) is the gold standard for CNV detection.

Purpose of the Study:

To evaluate three WES read-depth based CNV detection programs.
To compare WES-based CNV detection with high-resolution CMA.
To assess the clinical utility of WES for CNV identification.

Main Methods:

Acquired paired CMA and WES data from 45 samples.
Utilized 219 known CNVs (2.3 kb - 35 Mb) from three CMA platforms as standards.
Applied XHMM, CoNIFER, and CNVnator software to WES data for CNV calling.

Main Results:

All WES tools detected more small variants (< 20 kb) than CMA.
XHMM and CoNIFER showed low sensitivity (22.2% and 14.6%).
CNVnator had higher sensitivity (87.7%) but over-detected small CNVs and inaccurately estimated variant sizes.

Conclusions:

WES-based CNV detection shows low concordance and immaturity.
CMA remains crucial for clinical-grade CNV detection in the next-generation sequencing (NGS) era.
WES requires further algorithm refinement for reliable CNV analysis.