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Updated: May 13, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms
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Published on: May 9, 2017

Uncovering hidden duplicated content in public transcriptomics data.

Marta Rosikiewicz1, Aurélie Comte, Anne Niknejad

  • 1Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.

Database : the Journal of Biological Databases and Curation
|March 15, 2013
PubMed
Summary
This summary is machine-generated.

Researchers developed methods to identify and filter duplicate gene expression data from databases like GEO and ArrayExpress. This quality control improves the accuracy of the Bgee database, crucial for studying gene expression evolution.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • The Bgee database curates gene expression data from diverse sources for evolutionary analysis.
  • Quality control is essential to ensure data integrity and reliability.
  • Duplicate data entries can skew analyses and reduce the accuracy of evolutionary conclusions.

Purpose of the Study:

  • To develop and present robust procedures for identifying and filtering duplicate gene expression data.
  • To enhance the quality and reliability of data within the Bgee database.
  • To establish methods for detecting potential duplicates in both Affymetrix and RNA-Seq datasets.

Main Methods:

  • Annotation and analysis of gene expression data from Affymetrix and RNA-Seq platforms.
  • Implementation of quality control procedures to detect duplicated experiments and reused experimental components.
  • Development of specific filtering strategies for Affymetrix data and identification protocols for RNA-Seq data.

Main Results:

  • Approximately 14% of analyzed data from GEO and ArrayExpress were identified as duplicates.
  • Duplicate content included fully or partially duplicated experiments and reused Affymetrix chips.
  • Established procedures for filtering Affymetrix duplicates and identifying potential RNA-Seq duplicates.

Conclusions:

  • The developed procedures effectively identify and mitigate duplicate gene expression data.
  • Filtering duplicates significantly improves the quality of curated datasets like Bgee.
  • These methods are vital for accurate comparative gene expression and evolutionary studies.