Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis

Abstract

Background: RNA sequencing allows the measuring of gene expression at a resolution unmet by expression arrays or RT-qPCR. It is however necessary to normalize sequencing data by library size, transcript size and composition, among other factors, before comparing expression levels. The use of internal control genes or spike-ins is advocated in the literature for scaling read counts, but the methods for choosing reference genes are mostly targeted at RT-qPCR studies and require a set of pre-selected candidate controls or pre-selected target genes. Results: Here, we report an R-based script to select internal control genes based solely on read counts and gene sizes. This novel method first normalizes the read counts to Transcripts per Million (TPM) and then excludes weakly expressed genes using the DAFS script to calculate the cut-off. It then selects as references the genes with lowest TPM covariance. We used this method to pick custom reference genes for the differential expression analysis of three transcriptome sets from transgenic Arabidopsis plants expressing heterologous fungal effector proteins tagged with GFP (using GFP alone as the control). The custom reference genes showed lower covariance and fold change as well as a broader range of expression levels than commonly used reference genes. When analyzed with NormFinder, both typical and custom reference genes were considered suitable internal controls, but the expression of custom selected genes was more stable. geNorm produced a similar result in which most custom selected genes ranked higher (i.e. expression more stable) than commonly used reference genes. Conclusions: The proposed method is innovative, rapid and simple. Since it does not depend on genome annotation, it can be used with any organism, and does not require pre-selected reference candidates or target genes that are not always available.

High Enhancer Activity is an Epigenetic Feature of HPV Negative Atypical Head and Neck Squamous Cell Carcinoma

“…Counts were generated using featureCounts from subread v1.6.3 80 [64]. Downstream normalization and differential expression analysis were performed using DESeq2, with size factors being calculated using data-driven housekeeping gene method as implemented in the CustomSelection R package [65,66]. Pathway enrichment analyses were performed using GSEA's pre-ranked list option [67].…”

Transcriptome-based selection and validation of optimal house-keeping genes for skin research in goats (Capra hircus)

“…However, due to the limited number of animals used and testing only of commonly used HKGs, the previously published study [ 17 ] resulted in a limited impact. The development of high-throughput RNA-seq technology provides a method of determining spatiotemporal expression at the transcriptome level, and provides a novel approach for the identification of HKGs [ 18 , 19 ]. This strategy was successfully used to identify candidate HKGs for Artemisia sphaerocephala [ 7 ], Pyropia yezoensis [ 20 ], Euscaphis [ 21 ], Arabidopsis pumila [ 22 ], fish [ 23 ], tomato leaves [ 24 ], and holstein cows [ 25 ].…”

Transcriptome-based Selection and Validation of Optimal House-keeping Genes for Skin Research in Goats (Capra hircus)

“…However, due to lack of experiments used and testing only of commonly used HKGs, that study resulted in a limited impact. The development of high-throughput RNA-seq technology provides a means of determining spatio-temporal expression at the transcriptome level, and provides a novel approach for the identi cation of HKGs [18,19]. This strategy was successfully used to identify candidate HKGs for Artemisia sphaerocephala [7], Pyropia yezoensis [22], Euscaphis [23], Arabidopsis pumila [24], sh [20], tomato leaves [21], and holstein cows [25], all from transcriptome datasets.…”