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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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%...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Genome Copying Errors02:46

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: Jul 10, 2026

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
09:16

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

Published on: February 21, 2015

Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic

John C Marioni1, Natalie P Thorne, Armand Valsesia

  • 1Computational Biology Group, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, UK. J.Marioni@damtp.cam.ac.uk

Genome Biology
|October 27, 2007
PubMed
Summary

Researchers identified and removed a technical artifact (

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Last Updated: Jul 10, 2026

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Published on: November 8, 2017

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Copy number variation (CNV) is common and linked to diseases like HIV-1 and Alzheimer's.
  • Current methods for CNV detection from high-throughput data are still evolving.
  • Array comparative genomic hybridization (aCGH) generates complex datasets requiring sophisticated analysis.

Purpose of the Study:

  • To address technical artifacts in genome-wide CNV analysis.
  • To improve CNV detection and biological interpretation from aCGH data.
  • To develop and validate a novel CNV calling algorithm, CNVmix.

Main Methods:

  • Identification and removal of spatial autocorrelation ('wave') artifact in aCGH data.
  • Development of CNVmix, a model-based algorithm using cross-sample information.
  • Comparison of CNVmix with existing CNV calling methods.

Main Results:

  • Removing the 'wave' artifact improved data clustering and increased CNV identification.
  • CNVmix successfully identified CNV regions using cross-sample information.
  • CNVmix demonstrated superior categorization of copy number gains/losses compared to current methods.

Conclusions:

  • Removing artifactual 'waves' is crucial for accurate aCGH data analysis.
  • Utilizing cross-sample information enhances biological insights from CNV studies.
  • The developed methods improve the extraction of genetic information from aCGH experiments in normal individuals.