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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis.

Karen Cristine Gonçalves Dos Santos1,2, Isabel Desgagné-Penix1,2, Hugo Germain3,4

  • 1Department of Chemistry, Biochemistry and Physics, Université du Québec à Trois-Rivières, Trois-Rivières, QC, G9A 5H7, Canada.

BMC Genomics
|January 12, 2020
PubMed
Summary

This study introduces a new R-based pipeline for selecting internal control genes in RNA sequencing data, improving gene expression analysis accuracy. The method identifies stably expressed genes without needing pre-selected candidates, offering a rapid and universal solution.

Keywords:
Housekeeping genes for qPCRNext-generation sequencingR script

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Area of Science:

  • Bioinformatics
  • Molecular Biology
  • Genomics

Background:

  • RNA sequencing offers high-resolution gene expression measurement.
  • Normalization of RNA sequencing data is crucial for accurate expression level comparison.
  • Existing methods for selecting reference genes are often limited to specific experimental setups or require pre-selected candidates.

Purpose of the Study:

  • To develop and validate an R-based pipeline for selecting internal control genes for RNA sequencing data.
  • To provide a method that relies solely on read counts and gene sizes, independent of genome annotation.
  • To identify more stably expressed reference genes compared to commonly used ones.

Main Methods:

  • Normalization of read counts to Transcripts per Million (TPM).
  • Exclusion of weakly expressed genes using the DAFS script to determine expression cut-offs.
  • Selection of reference genes based on the lowest TPM covariance.
  • Application of the method to Arabidopsis transcriptome datasets.

Main Results:

  • The custom reference genes selected by the pipeline exhibited lower covariance and fold change compared to commonly used reference genes.
  • Both NormFinder and geNorm analyses indicated that the custom reference genes were more stably expressed.
  • The proposed method successfully identified suitable internal control genes for differential gene expression analysis.

Conclusions:

  • The developed R-based pipeline is an innovative, rapid, and simple method for selecting internal control genes.
  • The method's independence from genome annotation makes it applicable to any organism.
  • It eliminates the need for pre-selected reference candidates or target genes, simplifying experimental design.