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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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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.
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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform
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An end to end workflow for differential gene expression using Affymetrix microarrays.

Bernd Klaus1, Stefanie Reisenauer1

  • 1EMBL Heidelberg, Heidelberg, 69117, Germany.

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Summary

This study presents a Bioconductor workflow for Affymetrix microarray analysis, detailing steps from raw data to differential gene expression and enrichment analysis for clinical data.

Keywords:
gene expressionmicroarray

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Affymetrix microarrays are widely used for gene expression profiling.
  • Analyzing clinical microarray data requires robust bioinformatics workflows.
  • Differential gene expression analysis identifies genes with altered activity between conditions.

Purpose of the Study:

  • To provide an end-to-end workflow for Affymetrix microarray differential gene expression analysis using Bioconductor.
  • To demonstrate the application of this workflow on a clinical dataset comparing inflamed and non-inflamed colon tissue.
  • To facilitate the adaptation of the workflow for similar microarray platforms.

Main Methods:

  • Utilized Bioconductor packages for Affymetrix microarray analysis.
  • Processed raw data (CEL files) through import into ExpressionSet.
  • Performed quality control, normalization, and differential gene expression (DE) analysis.
  • Conducted enrichment analysis on DE results.

Main Results:

  • Successfully implemented a complete bioinformatics workflow for Affymetrix microarrays.
  • Identified differentially expressed genes between inflamed and non-inflamed colon tissues across two disease subtypes.
  • Demonstrated the utility of Bioconductor for complex clinical genomic data analysis.

Conclusions:

  • The presented Bioconductor workflow is effective for Affymetrix microarray differential gene expression analysis.
  • The workflow is adaptable to various Gene-type arrays and similar platforms.
  • This approach enables robust analysis of clinical genomic data for disease subtype comparisons.