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How consistent are we? Interlaboratory comparison study in fathead minnows using the model estrogen

April Feswick1, Meghan Isaacs1, Adam Biales2

  • 1Department of Biology, University of New Brunswick, Saint John, New Brunswick, Canada.

Environmental Toxicology and Chemistry
|March 20, 2017
PubMed
Summary

Omics data consistency across labs is crucial for environmental risk assessment. Standardizing bioinformatics pipelines improved transcriptomic data overlap, enabling reliable identification of chemical-responsive genes.

Keywords:
Endocrine disruptorEstrogenic compoundInterlaboratory comparisonRisk assessmentTranscriptomics

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Area of Science:

  • Environmental toxicology
  • Transcriptomics
  • Ecotoxicogenomics

Background:

  • Omics technologies are increasingly used in environmental risk assessment.
  • Data consistency across different laboratories remains a significant challenge.
  • Standardization is needed to ensure the reliability of omics data in regulatory science.

Purpose of the Study:

  • To evaluate the congruence of transcriptomic data generated by independent laboratories.
  • To assess the impact of different bioinformatics pipelines on data consistency.
  • To determine the reliability of omics data for environmental risk assessment.

Main Methods:

  • Male fathead minnows were exposed to 17α-ethinylestradiol (EE2).
  • Liver tissues were analyzed using microarrays across six independent laboratories.
  • Participants used their own bioinformatics pipelines, followed by an analysis using a standardized pipeline.

Main Results:

  • Only 4.7% of identified responsive transcripts were detected by all laboratories.
  • A standardized pipeline improved the overlap of differentially expressed genes from 50% to 59%.
  • Ranking transcripts by fold change showed high consistency (R² > 0.9) across laboratories.

Conclusions:

  • Standardized bioinformatics pipelines enhance transcriptomic data consistency in environmental risk assessments.
  • Ranking genes by fold change, not p-value, improves inter-laboratory comparisons.
  • Adoption of standardized methods and focused arrays will advance the regulatory application of omics data.