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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%...
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.
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...
Single Nucleotide Polymorphisms-SNPs01:05

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: Jun 16, 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

Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays.

Derek A Oldridge1, Samprit Banerjee, Sunita R Setlur

  • 1Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, NY 10065, USA.

Nucleic Acids Research
|February 17, 2010
PubMed
Summary

Array platforms detect copy number variants (CNVs) for human diversity studies. Study design and data processing significantly impact CNV accuracy, especially within segmental duplications, requiring careful optimization for reliable results.

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Area of Science:

  • Genomics
  • Human Genetics
  • Bioinformatics

Background:

  • Array-based platforms are crucial for detecting copy number variants (CNVs), contributing to understanding human diversity.
  • Suboptimal study design and data processing can negatively impact the accuracy of CNV assessment.

Purpose of the Study:

  • To quantitatively evaluate the impact of study design and data processing on CNV detection using Affymetrix SNP Array 6.0.
  • To compare CNV detection results with a high-resolution array comparative genomic hybridization (CGH) platform (Agilent).

Main Methods:

  • Evaluation of 42 HapMap samples using Affymetrix Genome-Wide Human SNP Array 6.0.
  • Implementation and comparison of various processing and segmentation strategies.
  • Comparison of results against Agilent oligonucleotide array CGH platform data.

Main Results:

  • Different reference models (single vs. pooled) cause significant inter-platform discrepancies (up to 30%) in CNV detection.
  • CNVs within segmental duplication regions are substantially harder to detect (P < 0.0001).
  • Adjusting for reference sample effects improved sensitivity and specificity, though performance varied for segmental duplications.

Conclusions:

  • Reference sample choice is a major factor influencing CNV detection discrepancies between array platforms.
  • Segmental duplication regions present unique challenges for accurate CNV identification.
  • Guidelines are provided to optimize array-based CNV studies based on specific research objectives.