Identification of appropriate reference genes (RGs) is critical to accurate data interpretation in quantitative real-time PCR (qPCR) experiments. In this study, researchers from the University of Queensland have utilised next generation RNA sequencing (RNA-seq) to analyse the transcriptome of a panel of non-melanoma skin cancer lesions, identifying genes that are consistently expressed across all samples. Genes encoding ribosomal proteins were amongst the most stable in this dataset. Validation of this RNA-seq data was examined using qPCR to confirm the suitability of a set of highly stable genes for use as qPCR RGs. These genes will provide a valuable resource for the normalisation of qPCR data for the analysis of non-melanoma skin cancer.
(A) Radio buttons for users to toggle value type to be plotted on the × and y-axis. Users can choose between plotting log10 Maximum Fold Change (log10MFC), log10 Transcript Per Million (log10TPM) and Coefficient of Variation (CoV) (B) Scatterplot showing data points derived from each measured transcript. Only genes with TPM > 0 for all samples are shown. Red dots represents genes selected either by user selection of points on the chart or are present in the user input. Green dots represent the gene with information displayed in (E) and is selected by clicking on the row in. (C) Input box for users to search for genes in the datasets. (D) Table showing metrics of selected genes (coloured dots) in scatterplot (E) Gene information of specific gene in the scatterplot (Green dots).
Availability – Data visualisation of TPM, CoV and MFC metrics of the calculated genes in this study have been made available online (http://skinref-dev.dingerlab.org/)