A protocol for identifying universal reference genes within a genus based on RNA-Seq data: a case study of poplar stem gene expression

• Published in: Forestry research (Fayetteville, Ga.) Ga.), 2024, Vol.4 (1), p.e021-13
• Main Authors: Xie, Qi, Ahmed, Umair, Qi, Cheng, Du, Kebing, Luo, Jie, Wang, Pengcheng, Zheng, Bo, Shi, Xueping
• Format: Article
• Language: English
• Subjects: abiotic stress; gene expression; populus; reference genes; rt-qpcr; stem development; transcriptome
• ISSN: 2767-3812; 2767-3812
• DOI: 10.48130/forres-0024-0017

Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) plays a crucial role in relative gene expression analysis, and accurate normalization relies on suitable reference genes (RGs). In this study, a pipeline for identifying candidate RGs from publicly available stem-related RNA-Seq data of different species under various developmental and abiotic stress conditions is presented. DESeq2's median of ratios yielded the smallest coefficient of variance (CV) values in a total of 292 RNA-Seq samples and was therefore chosen as the method for sample normalization. A total of 541 stably expressed genes were retrieved based on the CV values with a cutoff of 0.3. Universal gene-specific primer pairs were designed based on the consensus sequences of the orthologous genes of each RG candidate. The expression levels of 12 candidate RGs and six reported RGs in stems under different abiotic stress conditions or in different species were assessed by RT-qPCR. The expression stability of selected genes was further evaluated using ΔCt, geNorm, NormFinder, and BestKeeper. All candidate RGs were stably expressed in different experiments and conditions in . A test dataset containing 117 RNA-Seq samples was then used to confirm the expression stability, six candidate RGs and three reported RGs met the requirement of CV ≤ 0.3. In summary, this study was to propose a systematic and optimized protocol for the identification of constitutively and stably expressed genes based on RNA-Seq data, and Potri.001G349400 ( ) was identified as the best candidate RG suitable for gene expression studies in poplar stems.