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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: Apr 24, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Performance evaluation of DNA copy number segmentation methods.

Morgane Pierre-Jean, Guillem Rigaill, Pierre Neuvial

Briefings in Bioinformatics

|September 10, 2014

Summary

This summary is machine-generated.

This study introduces a framework for generating realistic DNA copy number profiles for cancer samples. The framework enables objective assessment of bioinformatic methods, revealing that no single method excels across all scenarios.

Keywords:

DNA copy number performance evaluation realistic data generation segmentation

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Last Updated: Apr 24, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

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Published on: February 17, 2017

Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

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Measuring Single-Cell Mitochondrial DNA Copy Number and Heteroplasmy Using Digital Droplet Polymerase Chain Reaction

Measuring Single-Cell Mitochondrial DNA Copy Number and Heteroplasmy Using Digital Droplet Polymerase Chain Reaction

Published on: July 12, 2022

Area of Science:

Bioinformatics
Biostatistics
Cancer Genomics

Background:

Analyzing DNA copy number profiles from microarrays and sequencing is crucial in cancer research.
Current performance assessments of analytical methods often rely on unrealistic simulations or small datasets.
A need exists for objective and reproducible evaluation of bioinformatic and biostatistical tools.

Purpose of the Study:

To develop and implement a framework for generating realistic DNA copy number profiles with known ground truth.
To enable objective and reproducible performance assessment of DNA copy number profile analysis methods.
To compare the performance of segmentation methods for SNP microarray data.

Main Methods:

Generated realistic DNA copy number profiles by resampling SNP microarray data from known copy-number regions.
Controlled signal-to-noise ratio by adjusting the percentage of tumor cells in simulated samples.
Applied the framework to a comparative study of DNA copy number profile segmentation methods.

Main Results:

No single segmentation method demonstrated superior performance across all tested conditions.
Identified method-specific advantages and disadvantages based on tumor cell fraction and heterozygous marker proportion.
The study provides a reproducible comparison using the open-source R package jointseg.

Conclusions:

The developed framework facilitates robust evaluation of DNA copy number analysis tools.
Method selection for DNA copy number profiling should consider sample characteristics like tumor purity.
The jointseg R package offers a valuable resource for reproducible bioinformatics research in cancer genomics.