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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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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Single Nucleotide Polymorphisms-SNPs01:05

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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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Genome Copying Errors02:46

Genome Copying Errors

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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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Genetic Variation01:25

Genetic Variation

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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,...
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Karyotyping01:17

Karyotyping

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Overview
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What is Variation?01:14

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
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Related Experiment Video

Updated: Jan 9, 2026

Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform
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Copy Number Variation.

Aurélien Macé1,2,3, Zoltán Kutalik1,3, Armand Valsesia4

  • 1Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland.

Methods in Molecular Biology (Clifton, N.J.)
|June 8, 2018
PubMed
Summary
This summary is machine-generated.

Copy number variants (CNVs) are crucial genomic differences linked to diseases. This chapter details computational methods for CNV analysis, essential for understanding genome plasticity and disease contributions.

Keywords:
Copy number variationDNADeletionDuplicationGenome-wide association studiesHuman diseaseHuman geneticsStructural variation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic variations include SNPs, translocations, inversions, and copy number variants (CNVs).
  • Large CNVs (>250 kb) are associated with developmental disorders and cancer.
  • CNV detection is vital for understanding genome plasticity and disease association, yet remains complex.

Purpose of the Study:

  • To explain computational methods for CNV analysis.
  • To cover the spectrum from study design to genome-wide association studies (GWAS) for clinical traits.
  • To address challenges in CNV detection, including bioinformatics workflows and calling accuracy.

Main Methods:

  • Discusses study design considerations for CNV detection.
  • Outlines data processing and quality control steps for CNV analysis.
  • Explains genome-wide association study methodologies applied to CNVs.

Main Results:

  • Highlights the importance of accurate CNV detection for disease association studies.
  • Addresses the technical challenges and potential solutions in CNV calling.
  • Provides a framework for integrating CNV data into GWAS.

Conclusions:

  • Accurate computational analysis of CNVs is essential for advancing genomic research and understanding disease.
  • Improved methods for CNV detection can overcome current limitations in GWAS.
  • This chapter serves as a guide for researchers conducting CNV analysis.