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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%...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

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Related Experiment Video

Updated: Apr 19, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Copy number variation analysis based on AluScan sequences.

Jian-Feng Yang¹, Xiao-Fan Ding¹, Lei Chen²

¹Division of Life Science and Applied Genomics Centre, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.

Journal of Clinical Bioinformatics

|January 6, 2015

Summary

This summary is machine-generated.

A new AluScanCNV package efficiently identifies copy number variations (CNVs) from AluScan sequencing data. This method aids in distinguishing cancer types and analyzing constitutional CNVs in individuals.

Keywords:

AluScan sequencing CNV calling Cancer classification Machine learning

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Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

AluScan technology enables large-scale genomic investigation using minimal DNA.
Existing copy number variation (CNV) calling algorithms are incompatible with AluScan data.
Specialized methods are needed for CNV analysis from AluScan sequencing.

Purpose of the Study:

To develop an efficient computational package for calling CNVs from AluScan data.
To validate the utility of AluScan-derived CNVs in cancer genomics.
To explore the potential of CNV-features for cancer subtyping.

Main Methods:

Developed the AluScanCNV package utilizing Geary-Hinkley transformation (GHT) for read-depth ratios.
Employed GISTIC-like and Circular Binary Segmentation (CBS) algorithms for CNV identification.
Analyzed AluScan data from non-cancer and cancer genomes, including glioma and liver cancer.

Main Results:

The AluScanCNV package successfully identified localized, recurrent, and extended CNVs.
CNV patterns in glioma and liver cancer samples showed consistency with previous studies.
Machine learning on CNV-features achieved accurate discrimination between liver and non-liver cancers.

Conclusions:

The AluScanCNV package is effective for comprehensive CNV calling from AluScan sequences.
Identified CNVs can be used with machine learning to develop cancer-specific biomarkers.
The method is versatile for analyzing constitutional and somatic CNVs in various human DNA samples.