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The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance
Charles Wang1, Binsheng Gong2, Pierre R Bushel3
11] Center for Genomics and Division of Microbiology &Molecular Genetics, School of Medicine, Loma Linda University, Loma Linda, California, USA. [2].
Nature Biotechnology
|August 25, 2014
Summary
RNA-sequencing (RNA-seq) and microarrays show similar performance for predicting modes of action (MOAs). RNA-seq offers better accuracy for low-abundance transcripts in differential gene expression analysis.
Area of Science:
- Toxicogenomics
- Transcriptomics
- Bioinformatics
Background:
- Assessing the concordance between RNA-sequencing (RNA-seq) and microarrays for genome-wide differential gene expression analysis across diverse chemical treatments is crucial.
- Understanding the factors influencing cross-platform agreement is essential for reliable toxicological and regulatory applications.
Purpose of the Study:
- To rigorously evaluate the concordance between RNA-seq and microarrays for differential gene expression analysis.
- To investigate the impact of chemical perturbation, transcript abundance, and biological complexity on cross-platform agreement.
- To compare the performance of RNA-seq and microarrays in predicting chemical modes of action (MOAs).
Main Methods:
- Generation of parallel Illumina RNA-seq and Affymetrix microarray data from rat liver samples exposed to 27 chemicals with varying modes of action.
- Analysis of cross-platform concordance for differentially expressed genes (DEGs) and enriched pathways.
- Assessment of DEG verification using quantitative PCR.
- Development and comparison of MOA prediction classifiers using data from both platforms.
Main Results:
- Cross-platform concordance linearly correlated with treatment effect size (R(2)≥0.8).
- Transcript abundance and MOA complexity significantly affected concordance.
- RNA-seq demonstrated superior performance (93%) over microarrays (75%) in DEG verification, particularly for low-abundance transcripts.
- MOA prediction classifiers performed similarly regardless of the data platform used.
Conclusions:
- While RNA-seq offers improved accuracy for low-abundance transcripts, both platforms yield comparable results for MOA prediction classifiers.
- The choice of transcriptomic platform should consider the specific endpoint, biological complexity, transcript abundance, and genomic application.
- Findings inform transcriptomic research and decision-making in clinical and regulatory settings.
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