Abstract
BackgroundDose-dependent differential gene expression provides critical information required for regulatory decision-making. The lower costs associated with RNA-Seq have made it the preferred technology for transcriptomic analysis. However, concordance between RNA-Seq and microarray analyses in dose response studies has not been adequately vetted.ResultsWe compared the hepatic transcriptome of C57BL/6 mice following gavage with sesame oil vehicle, 0.01, 0.03, 0.1, 0.3, 1, 3, 10, or 30 μg/kg TCDD every 4 days for 28 days using Illumina HiSeq RNA-Sequencing (RNA-Seq) and Agilent 4×44 K microarrays using the same normalization and analysis approach. RNA-Seq and microarray analysis identified a total of 18,063 and 16,403 genes, respectively, that were expressed in the liver. RNA-Seq analysis for differentially expressed genes (DEGs) varied dramatically depending on the P1(t) cut-off while microarray results varied more based on the fold change criteria, although responses strongly correlated. Verification by WaferGen SmartChip QRTPCR revealed that RNA-Seq had a false discovery rate of 24% compared to 54% for microarray analysis. Dose–response modeling of RNA-Seq and microarray data demonstrated similar point of departure (POD) and ED50 estimates for common DEGs.ConclusionsThere was a strong correspondence between RNA-Seq and Agilent array transcriptome profiling when using the same samples and analysis strategy. However, RNA-Seq provided superior quantitative data, identifying more genes and DEGs, as well as qualitative information regarding identity and annotation for dose response modeling in support of regulatory decision-making.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1527-z) contains supplementary material, which is available to authorized users.
Highlights
Dose-dependent differential gene expression provides critical information required for regulatory decision-making
17,794 genes were found to be expressed in the mouse liver with a sample size of 3
In comparison to WaferGen SmartChip QRTPCR, we demonstrate that some differences between RNA-Seq and Agilent differentially expressed genes (DEGs) identification may not be as significant as previously reported
Summary
Dose-dependent differential gene expression provides critical information required for regulatory decision-making. The emergence of next-generation-sequencing (NGS) with its direct transcript identification, lower cost, larger dynamic range and superior detection of low abundance genes [10,11,12], is making microarrays obsolete. Despite these advantages, some studies have raised concerns regarding RNA-Seq and microarray comparability [13,14,15]. Semi-parametric normalization accounts for multiple sources of variation including random effects [22] while empirical Bayes analysis has the advantage of considering continuous variables such as the correlation between doses that can improve DEG detection [23,24], Nault et al BMC Genomics (2015) 16:373 an important consideration in regulatory decisionmaking
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