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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: Sep 9, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Visual Inspection of Sequencing Data for Diagnosis: Practical Guide to Structural Variant Analysis Using Integrative

Benjamin Ganne^1,2, Clément Hersent³, Vincent Gatinois^3,4

¹Unit of Chromosomal Genetics and Research Platform Chromostem, Department of Molecular Genetics and Cytogenomics, Site Unique de Biologie (SUB), Montpellier CHU, Montpellier, France. benjamin.ganne@chu-montpellier.fr.

Methods in Molecular Biology (Clifton, N.J.)

|August 30, 2025

Summary

This summary is machine-generated.

This guide explains how to interpret structural variants (SVs) from next-generation sequencing (NGS) data using IGV. It helps distinguish true SVs from artifacts to improve diagnostic accuracy in clinical genomics.

Keywords:

Bioinformatics Chromothripsis Copy number variation IGV Structural variation

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Published on: August 15, 2019

Area of Science:

Genomics
Bioinformatics
Clinical Diagnostics

Background:

Next-generation sequencing (NGS) has transformed clinical genomics.
Structural variants (SVs) are crucial in diseases like chromothripsis.
Accurate SV interpretation is vital for patient care.

Purpose of the Study:

To provide a guide for interpreting SVs from NGS data.
To detail methods for assessing SV veracity using IGV.
To differentiate true SVs from sequencing artifacts.

Main Methods:

Utilizing Integrative Genomics Viewer (IGV) for SV analysis.
Implementing specific steps for SV interpretation.
Applying techniques to validate SV calls.

Main Results:

Established methods for assessing SV veracity.
Provided criteria for distinguishing true SVs from artifacts.
Demonstrated effective SV interpretation using IGV.

Conclusions:

Effective interpretation of NGS-derived SV data enhances diagnostic accuracy.
This guide aids clinicians and researchers in SV analysis.
Improved SV interpretation leads to better patient care outcomes.