Liu Xin, Tang Siyuan, Gao Yingbo, Zhang Xiaoxiang, Dong Guichun, Zhou Juan, Zhou Yong, Yang Zefeng, Huang Jianye, Yao Youli
Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, 48 East WenHui Rd, Yangzhou, Jiangsu 225009, China.
Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, 48 East WenHui Rd, Yangzhou, Jiangsu 225009, China.
Plant Cell Physiol. 2025 Jan 29;66(1):120-132. doi: 10.1093/pcp/pcae138.
Reverse transcription quantitative real-time PCR (RT-qPCR) is esteemed for its precision and reliability, positioning it as the standard for evaluating gene expression. Selecting suitable reference genes is crucial for acquiring accurate data on target gene expression. However, identifying appropriate reference genes for specific rice tissues or growth conditions has been a challenge. To overcome this, we introduce the Rice Reference Genes (RRG) tool ( https://www.rrgenes.com/ ), which assists researchers in selecting reference genes for diverse experimental conditions in rice. This tool utilizes 4404 rice-derived RNA-seq datasets, categorized by five tissue types-leaf, root, seedling, panicle, and seed-and seven stress conditions (cold, disease, drought, heat, hormone, metal, and salt), along with corresponding control groups (mock). In this research, we employed the RRG web-based tool to identify candidate reference genes in rice leaves, roots, and seedlings exposed to salt and drought stress. These candidates were rigorously tested against conventionally established reference genes, confirming their accuracy and reliability. The RRG tool is designed to be user-friendly, allowing even those with limited experience to efficiently select optimal reference genes in rice with ease.
逆转录定量实时PCR（RT-qPCR）因其精确性和可靠性而备受推崇，使其成为评估基因表达的标准方法。选择合适的内参基因对于获取目标基因表达的准确数据至关重要。然而，为特定水稻组织或生长条件鉴定合适的内参基因一直是一项挑战。为了克服这一问题，我们推出了水稻内参基因（RRG）工具（https://www.rrgenes.com/），该工具可帮助研究人员为水稻不同实验条件选择内参基因。此工具利用4404个源自水稻的RNA测序数据集，这些数据集按叶、根、幼苗、穗和种子五种组织类型以及冷、病害、干旱、热、激素、金属和盐七种胁迫条件分类，同时还有相应对照组（模拟）。在本研究中，我们使用基于RRG的网络工具来鉴定遭受盐胁迫和干旱胁迫的水稻叶片、根和幼苗中的候选内参基因。这些候选基因与传统确定的内参基因进行了严格测试，证实了它们的准确性和可靠性。RRG工具设计得用户友好，即使经验有限的人也能轻松高效地为水稻选择最佳内参基因。
