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DNA Microarrays

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...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

Updated: Jun 20, 2026

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Published on: August 5, 2008

Using Free and Open-Source Bioconductor Packages to Analyze Array Comparative Genomics Hybridization (aCGH) Data.

Simon Lin¹, Pan Du, Nadereh Jafari

¹Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.

Current Genomics

|September 2, 2009

Summary

This summary is machine-generated.

Array comparative genomic hybridization (aCGH) helps cancer researchers analyze genomic abnormalities. This review covers open-source Bioconductor tools for analyzing large aCGH datasets to understand cancer development and outcomes.

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Last Updated: Jun 20, 2026

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

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Area of Science:

Genomics
Cancer Research
Bioinformatics

Background:

Whole-genome array Comparative Genomic Hybridization (aCGH) is a powerful technique for detecting chromosomal deletions and amplifications.
Its accessibility has led to widespread use in cancer research for studying tumorigenesis and predicting clinical outcomes.
aCGH datasets are large, containing millions of hybridization measurements, necessitating robust statistical analysis.

Purpose of the Study:

To review available free and open-source statistical analysis packages for aCGH data within the Bioconductor environment.
To provide example R codes for performing aCGH data analysis.

Main Methods:

Review of Bioconductor packages relevant to aCGH data analysis.
Demonstration of analysis workflows using provided example R code.

Main Results:

Identification and overview of key Bioconductor packages for aCGH data analysis.
Practical examples illustrating the application of these tools.

Conclusions:

Statistical analysis of aCGH data is crucial for uncovering genomic abnormalities in cancers.
Bioconductor offers valuable, accessible resources for researchers analyzing complex aCGH datasets.