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
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.
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.
Keywords:
Endocrine disruptorEstrogenic compoundInterlaboratory comparisonRisk assessmentTranscriptomics
