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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,...

Genome Copying Errors

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their survival. Therefore, the copying errors are checked and repaired at three levels.

Genetic Variation

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

Updated: Dec 10, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

A machine learning framework for genotyping the structural variations with copy number variant.

Tian Zheng¹, Xiaoyan Zhu², Xuanping Zhang¹

¹School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.

BMC Medical Genomics

|August 29, 2020

Summary

This summary is machine-generated.

This study introduces a new machine learning method for accurate genotyping of structural variations in cancer genomes, even with copy number variants (CNVs). The developed approach outperforms existing tools in accuracy and efficiency for next-generation sequencing data analysis.

Keywords:

Cancer genomics Copy number variant Genotyping structural variation Multiclass relevance vector machine NGS data analysis

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genotyping structural variations in next-generation sequencing data is computationally challenging.
Copy number variants (CNVs) in cancer genomes reduce the accuracy of existing genotyping methods by causing biases in sequencing coverage and variant allelic frequency.
CNVs can lead to misinterpretation of heterozygotes as homozygotes and misjudgment of other data signals like split-mapped reads.

Purpose of the Study:

To develop a computational method for accurate genotyping of indels within CNV regions.
To address the challenge of coexisting structural variations and CNVs in cancer genomes.
To improve the accuracy and reliability of structural variation genotyping in complex genomic regions.

Main Methods:

Proposed a machine learning framework to incorporate multiple data features and their interactions for genotyping.
Extracted fifteen types of classification features as input, considering five variant types: normal homozygote, homozygous variant, heterozygous variant without CNV, heterozygous variant with CNV on mutated haplotype, and heterozygous variant with CNV on wild haplotype.
Utilized a Multiclass Relevance Vector Machine (M-RVM) combined with feature distribution characteristics.

Main Results:

The proposed method was applied to simulated and real data, outperforming existing software (Gindel, Facets, GATK) and other machine learning algorithms (SVM, Naïve Bayes, BP Neural Network).
Demonstrated superior accuracy, stability, and efficiency compared to current popular genotyping tools.
The method effectively handles the complexity introduced by coexisting CNVs.

Conclusions:

Genotyping structural variations in CNV regions requires a multi-feature approach beyond traditional methods.
The proposed method is a practical algorithm for correcting genotype structural variations with CNVs in next-generation sequencing data.
The developed computational method offers improved accuracy for complex genomic analyses.