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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.

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Related Experiment Video

Updated: Apr 18, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

SubPatCNV: approximate subspace pattern mining for mapping copy-number variations.

Nicholas Johnson¹, Huanan Zhang², Gang Fang^3,4

¹Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota. njohnson@cs.umn.edu.

BMC Bioinformatics

|January 17, 2015

Summary

This summary is machine-generated.

SubPatCNV identifies specific DNA copy-number variation (CNV) regions within sample subsets using advanced data mining. This tool aids in discovering genetic patterns linked to diseases and population variations.

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Last Updated: Apr 18, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

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Published on: February 17, 2017

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

DNA copy-number variations (CNVs) contribute to phenotypic diversity and disease.
Identifying CNVs specific to sample subsets requires advanced data mining.

Purpose of the Study:

To develop a tool for customized identification of CNV regions within specific sample subsets.
To address the challenge of finding common CNVs in arbitrary subgroups of individuals.

Main Methods:

Introduced SubPatCNV, a tool implementing an approximate association pattern mining algorithm.
Applied spatial constraints to positional CNV probe features for pattern discovery.
Utilized high-density array data for CNV analysis.

Main Results:

SubPatCNV identified population-specific germline CNVs in HapMap samples.
Discovered large aberrant CNV events and cancer-relevant genes in TCGA ovarian cancer subgroups.
Demonstrated effective identification of consistent CNV patterns within subgroups using approximate pattern mining.

Conclusions:

SubPatCNV is a scalable, open-source software tool for identifying CNV regions in sample subgroups.
The tool offers flexibility in analyzing high-density CNV array data for various subgroup sizes.
SubPatCNV is available for download at http://sourceforge.net/projects/subpatcnv/.