Molecular analyses of normal and diseased cells give insight into changes in gene expression and help in understanding the background of pathophysiological processes. Years after cDNA microarrays were established in research, RNA sequencing (RNA-seq) became a key method of quantitatively measuring the transcriptome. In this study, researchers from the Friedrich-Alexander University compared the detection of genes by each of the transcriptome analysis methods: cDNA array, quantitative RT-PCR, and RNA-seq. As expected, the researchers found differences in the gene expression profiles of the aforementioned techniques. Here, they present selected genes that exemplarily demonstrate the observed differences and calculations to reveal that a strong RNA secondary structure, as well as sample preparation, can affect RNA-seq. In summary, this study addresses an important issue with a strong impact on gene expression analysis in general. Therefore, the researchers suggest that these findings need to be considered when dealing with data from transcriptome analyses.
Scatter plots depicting the detected genes measured with
two different methods for transcriptome analysis
Shown for the melanoma cell line Mel Im in (A) and Mel Juso in (B). The measured signals with log2 are depicted as plots for each gene. The light gray color shows genes that were measured with both methods, the dark gray color shows genes that were not detected (n.d.) with microarray analysis, and the black dots depict genes that were not detected with RNA-seq. The blue line represents the Spearman correlation between genes that were detected with both methods. The genes named within the plots (labeled in red) are further discussed within the following text. Statistical analysis was performed using R.