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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%...
Karyotyping01:17

Karyotyping

Describing the number and physical features of chromosomes can reveal abnormalities that underlie genetic diseases. This description is facilitated by special staining techniques that produce a particular banding pattern on each chromosome. State-of-the-art techniques make this approach even more powerful, enabling the detection of individual genes that cause disease.A Simple Chromosome Staining Technique Provides Valuable Scientific InsightSome genetic diseases can be detected by looking at...
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.
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,...
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...

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Related Experiment Video

Updated: Jun 24, 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

Computational methods for identification of recurrent copy number alteration patterns by array CGH.

S P Shah1

  • 1Department of Computer Science, University of British Columbia, British Columbia Cancer Agency, Vancouver, BC, Canada. sshah@bccrc.ca

Cytogenetic and Genome Research
|March 17, 2009
PubMed
Summary
This summary is machine-generated.

This study reviews computational methods for finding recurrent DNA copy number alterations (CNA) in cancer using array comparative genomic hybridization (aCGH) data. Identifying these common CNAs is crucial for cancer diagnostics and predicting patient outcomes.

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Area of Science:

  • Genomics
  • Cancer Research
  • Bioinformatics

Background:

  • Recurrent DNA copy number alterations (CNA) are critical in cancer research, influencing gene dosage and expression.
  • Array comparative genomic hybridization (aCGH) is a key technology for genome-wide analysis of CNAs in tumor samples.

Purpose of the Study:

  • To review and evaluate computational approaches for detecting recurrent CNAs from aCGH data.
  • To identify common CNAs across patient cohorts for potential diagnostic and prognostic applications.

Main Methods:

  • Qualitative evaluation of existing computational methods for recurrent CNA detection.
  • Analysis of strengths and limitations of various detection algorithms.
  • Discussion of challenges posed by data noise in CNA prediction.

Main Results:

  • CNAs are significant in cancer diagnostics and outcome prediction.
  • aCGH data analysis for recurrent CNAs presents computational challenges due to noise.
  • Various computational methods exist, each with specific analytical strengths and weaknesses.

Conclusions:

  • Accurate detection of recurrent CNAs from aCGH data is essential for advancing cancer research.
  • Further development of computational methods is needed to overcome data noise and improve prediction accuracy.
  • Future research should focus on refining algorithms and exploring new directions in CNA detection.