Stable reference genes selected from RNAseq data do not offer any significant advantage over conventional reference genes for normalizing qPCR assays. Stable reference genes selected from RNAseq data do not offer any significant advantage over conventional reference genes for normalizing qPCR assays

Using robust reference genes which are stably expressed across the samples to normalized qPCR data in a study is crucial. The choice of such reference genes are made based on different approaches. Additionally, a growing body of literature suggests the use of RNA-seq as a prerequisite for choosing optimal reference genes. In this study, we raised two major questions. Firstly, is RNA-seq necessary to find stably expressing genes that could be used as reference genes to normalize qPCR data? In order to address this question we have devised a previously described statistical pipeline of ours to best select the reference genes. Interestingly, we found that RNA-seq is not necessarily needed to find optimal reference genes. Addressing this question raised the second question, is this pipeline is effective for cross-sectional studies and longitudinal studies and in different models of various species. We report that our statistical pipeline that combines two different approaches works robustly in both of the setups we have tried. Overall design: Comparison of two approaches (stably expressed genes from RNA-seq or conventional genes) to find the optimal reference genes for qPCR analyses.

在研究中，选用在各样本间表达稳定的可靠参考基因以对定量聚合酶链式反应（quantitative Polymerase Chain Reaction, qPCR）数据进行标准化，是一项关键步骤。此类参考基因的筛选需基于多种不同的分析策略。此外，日益增多的相关研究文献表明，RNA测序（RNA-sequencing, RNA-seq）可作为筛选最优参考基因的必要前提步骤。本研究提出了两个核心研究问题：其一，是否必须借助RNA-seq来筛选可用于qPCR数据标准化的稳定表达参考基因？为解答该问题，我们设计了一套此前已发表的统计分析流程，用于精准筛选最优参考基因。有趣的是，我们的研究发现，筛选最优参考基因并非必须依赖RNA-seq技术。在解答第一个研究问题的过程中，我们进一步提出了第二个核心问题：该统计分析流程是否可适用于横断面研究、纵向研究，以及不同物种的各类实验模型？本研究结果显示，我们这套结合两种不同分析策略的统计流程，在我们测试的两类实验设置中均展现出优异的稳健性与适用性。整体实验设计：对比两种筛选策略——基于RNA-seq的稳定表达基因法与传统候选基因法，以筛选适用于qPCR分析的最优参考基因。