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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...
What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

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Updated: May 14, 2026

Bacterial Gene Expression Analysis Using Microarrays
29:41

Bacterial Gene Expression Analysis Using Microarrays

Published on: May 28, 2007

What statisticians should know about microarray gene expression technology.

Stephen Welle1

  • 1Functional Genomics Center, University of Rochester, Rochester, NY, USA. Stephen_Welle@urmc.rochester.edu

Methods in Molecular Biology (Clifton, N.J.)
|February 7, 2013
PubMed
Summary
This summary is machine-generated.

This chapter explains how microarray data is generated in labs. Understanding these technical sources of variability is crucial for data analysts to minimize errors using normalization or array exclusion.

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Last Updated: May 14, 2026

Bacterial Gene Expression Analysis Using Microarrays
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07:04

Performing Custom MicroRNA Microarray Experiments

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Area of Science:

  • Biotechnology
  • Genomics
  • Data Science

Background:

  • Microarray technology is a key tool in genomic research.
  • Understanding data generation processes is essential for accurate analysis.
  • Technical variability can significantly impact experimental outcomes.

Purpose of the Study:

  • To review the laboratory generation of microarray data.
  • To enhance data analysts' understanding of technical variability.
  • To highlight methods for minimizing data variability.

Main Methods:

  • Review of laboratory protocols for microarray data generation.
  • Discussion of potential sources of technical variation.
  • Explanation of data normalization techniques.
  • Description of aberrant array exclusion criteria.

Main Results:

  • Identification of key technical steps in microarray data production.
  • Elucidation of common sources of experimental variability.
  • Demonstration of the impact of variability on data quality.

Conclusions:

  • Awareness of data generation processes improves microarray data analysis.
  • Normalization and array exclusion are vital for robust results.
  • Minimizing technical variability enhances the reliability of genomic studies.