(CS-21-1) Controlling for total RNA abundance affects the identification of differentially expressed
genes revealing bias toward morning-expressed responses
Body of Abstract: RNA-Sequencing (RNA-Seq) is widely used to investigate changes in gene expression at the transcription level in plants. Most plant RNA-Seq analysis pipelines base the normalization approaches on the assumption that total transcript levels do not vary between samples. However, this assumption has yet to be demonstrated. Several common experimental treatments and genetic alterations affect transcription efficiency or RNA stability, resulting in unequal transcript abundance. The addition of synthetic RNA controls is a simple correction that controls for variation in total mRNA levels. In this work, with the synthetic RNA-Seq dataset, the addition of external RNA spike-ins as a normalization control reveals true mRNA abundance and corrects a bias toward downregulated genes due to transcriptional amplification. We evaluate the RNA spike-in normalization method using RNA-Seq data from sorghum leaves grown under chilling stress and collected at dawn and dusk. Regardless of temperature conditions, we observe increased transcript abundance in the evening by normalizing with spike-ins, resulting in identifying novel evening-upregulated genes in normal and chilling stress conditions. Our study highlights the importance of considering the time of day in plant gene expression studies and demonstrates that external RNA spike-ins are essential for accurate RNA-Seq analysis.