Abstract

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is extensively applied technique to investigate the transcript abundance of target genes in various organisms. Selection of appropriate reference genes (RGs) for qRT-PCR normalization is a crucial prerequisite for accurately quantifying gene expression level. RGs should exhibit minimal variation in gene expression. However, the actual expression stability of RGs fluctuates greatly in different species or under different experimental conditions. Due to rapid advancements in next-generation sequencing (NGS) technology, it is no longer difficult to get massive transcriptome data, which has greatly promoted the development of RGs. In this review, we elaborate on the strategies for developing RGs using Northern blotting, expressed sequence tags (ESTs), qRT-PCR, and high-throughput technologies such as microarray and RNA-sequencing (RNA-Seq). The process for developing RGs based on RNA-Seq is further addressed, including processing and normalization of RNA-Seq data, evaluation of gene expression stability, and screening and validation of RGs. The most frequently used RGs in horticultural plants are summarized, and the strategies for developing these RGs are introduced in detail. The information provided here will help to design effective strategies for the development of RGs in horticultural plants, with a focus on using big data generated by RNA-Seq.

摘要

定量逆转录聚合酶链反应 (qRT-PCR) 是广泛应用的技术，用于研究各种生物中靶基因的转录丰度。为 qRT-PCR 标准化选择合适的参考基因 (RG) 是准确量化基因表达水平的关键先决条件。RGs 应该表现出基因表达的最小变化。然而，RGs的实际表达稳定性在不同物种或不同实验条件下波动很大。由于下一代测序（NGS）技术的飞速发展，获取海量转录组数据不再困难，极大地促进了RGs的发展。在这篇综述中，我们详细阐述了使用 Northern 印迹、表达序列标签 (ESTs)、qRT-PCR、以及高通量技术，例如微阵列和 RNA 测序 (RNA-Seq)。进一步阐述了基于 RNA-Seq 开发 RGs 的过程，包括 RNA-Seq 数据的处理和标准化、基因表达稳定性的评估以及 RGs 的筛选和验证。总结了园艺植物中最常用的 RGs，并详细介绍了这些 RGs 的开发策略。此处提供的信息将有助于设计开发园艺植物 RGs 的有效策略，重点是使用 RNA-Seq 生成的大数据。以及 RG 的筛选和验证。总结了园艺植物中最常用的 RGs，并详细介绍了这些 RGs 的开发策略。此处提供的信息将有助于设计开发园艺植物 RGs 的有效策略，重点是使用 RNA-Seq 生成的大数据。以及 RG 的筛选和验证。总结了园艺植物中最常用的 RGs，并详细介绍了这些 RGs 的开发策略。此处提供的信息将有助于设计开发园艺植物 RGs 的有效策略，重点是使用 RNA-Seq 生成的大数据。