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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...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific primer.
Since the...

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Related Experiment Video

Updated: May 13, 2026

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

virtualArray: a R/bioconductor package to merge raw data from different microarray platforms.

Andreas Heider1, Rüdiger Alt

  • 1Translational Centre for Regenerative Medicine Leipzig, University of Leipzig, Semmelweisstr. 14, Leipzig 04103, Germany. aheider@trm.uni-leipzig.de

BMC Bioinformatics
|March 5, 2013
PubMed
Summary
This summary is machine-generated.

Researchers can now integrate diverse microarray data from public databases, even from different chip types. The virtualArray software addresses batch effects, enabling seamless data analysis and combination for broader biological insights.

Related Experiment Videos

Last Updated: May 13, 2026

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Microarrays are essential for biological research, generating vast amounts of data in public repositories like NCBI GEO and EBI ArrayExpress.
  • Existing databases contain numerous microarray samples across different species, manufacturers, and chip generations, hindering data integration.
  • Current software limitations prevent combining datasets from different microarray chip types and addressing batch effects.

Purpose of the Study:

  • To develop a user-friendly software solution for integrating diverse microarray datasets.
  • To overcome the challenge of combining data from multiple chip types and correcting for batch effects.

Main Methods:

  • The virtualArray software package facilitates the combination of raw microarray data from various chip types using current annotations.
  • It incorporates seven implemented methods for adjusting batch effects arising from differences in chip types.
  • The software allows users to customize annotation and batch effect correction parameters.

Main Results:

  • virtualArray successfully integrates raw data from almost any chip type, leveraging NCBI GEO and Bioconductor annotations.
  • The software applies robust batch effect correction methods, addressing data discrepancies between different chip types.
  • The output is a standardized Bioconductor "ExpressionSet" object, compatible with other analysis packages.

Conclusions:

  • Researchers can now seamlessly integrate their own microarray data with public repository data, regardless of chip type.
  • The virtualArray software provides a straightforward approach to handle multiple chip types and batch effects in microarray analysis.
  • This enables more comprehensive and reliable data integration for biological discovery.