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Related Concept Videos

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

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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

Analysis of array-CGH data using the R and Bioconductor software suite.

Winfried A Hofmann1, Anja Weigmann, Marcel Tauscher

  • 1Hannover Medical School, Institute of Cell and Molecular Pathology, Carl-Neuberg-Str. 1, 30625 Hannover, Germany.

Comparative and Functional Genomics
|August 22, 2009
PubMed
Summary

A new tool accurately detects DNA copy number alterations (CNAs) from array-based comparative genomic hybridization (array-CGH) data. This aids in identifying minimal altered regions (MARs) for discovering cancer-related genes.

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Area of Science:

  • Genomics
  • Molecular Genetics
  • Bioinformatics

Background:

  • Array-based comparative genomic hybridization (array-CGH) is a high-resolution technique for genome-wide screening of chromosome alterations.
  • DNA copy number alterations (CNAs) are crucial in tumor genomes and congenital abnormalities.
  • Accurate analysis of array-CGH microarray data requires sophisticated computational methods.

Purpose of the Study:

  • To develop a versatile tool for comprehensive array-CGH data analysis.
  • To enable accurate and sensitive detection of CNAs.
  • To facilitate the identification of minimal altered regions (MARs).

Main Methods:

  • Development of a user-friendly software tool.
  • Implementation of normalization and visualization functionalities.
  • Integration of breakpoint detection and comparative analysis algorithms.

Main Results:

  • A versatile tool for array-CGH data analysis was successfully developed.
  • The tool provides accurate and sensitive detection of CNAs.
  • The software enables efficient normalization, visualization, and comparative analysis.

Conclusions:

  • The developed tool represents a significant advancement in CNA detection.
  • The minimal altered regions (MARs) determination feature aids in identifying potential tumor suppressor genes and oncogenes.
  • This approach enhances the discovery of novel genes implicated in diseases.