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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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Updated: May 20, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

A regression model for estimating DNA copy number applied to capture sequencing data.

Guillem J Rigaill¹, Sidney Cadot, Roelof J C Kluin

¹Department of Bioinformatics and Statistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

Bioinformatics (Oxford, England)

|July 17, 2012

Summary

This study introduces a novel regression method for DNA copy number analysis using target enrichment data. The approach accurately detects copy number alterations, outperforming traditional log-ratio methods and showing high concordance with SNP data.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Published on: August 3, 2018

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Target enrichment (DNA capture) focuses sequencing on specific genomic regions.
While effective for single-nucleotide variants, its use for copy number alterations (CNAs) in cancer is valuable.
Log-ratio analysis for CNAs discards crucial total coverage information.

Purpose of the Study:

To develop a novel method for copy number analysis using DNA capture data.
To address limitations of log-ratio methods by incorporating total coverage information.
To demonstrate the utility of the new approach in cancer cell line analysis.

Main Methods:

A regression model was developed to analyze test sample coverage/intensity as a linear function of control samples.
This method effectively handles completely deleted regions, a challenge for log-ratio approaches.
The algorithm is implemented in C and R, with code available online.

Main Results:

The new regression approach accurately determined copy number for 600 genes across nine breast cancer cell lines.
Results showed high concordance with a single-nucleotide polymorphism (SNP) genotyping platform.
The method demonstrated superior correlation with SNP data compared to existing log-ratio-based tools like ExomeCNV.

Conclusions:

The developed regression method offers an improved approach for copy number analysis from DNA capture data.
It overcomes limitations of traditional log-ratio methods, particularly for deleted regions.
This technique enhances the accuracy of CNA detection in cancer genomics studies.