College of Life Sciences, Chongqing Normal University, Chongqing 401331, China.
Scientific Research Office, Chongqing Normal University, Chongqing 401331, China.
Cells. 2020 Mar 24;9(3):786. doi: 10.3390/cells9030786.
For accurate gene expression quantification, normalization of gene expression data against reliable reference genes is required. It is known that the expression levels of commonly used reference genes vary considerably under different experimental conditions, and therefore, their use for data normalization is limited. In this study, an unbiased identification of reference genes in was performed based on 145 microarray datasets (2296 gene array samples) covering different developmental stages, different tissues, drug treatments, lifestyle, and various stresses. As a result, thirteen housekeeping genes (, , , , , , , , , , , , and ) with enhanced stability were comprehensively identified by using six popular normalization algorithms and method. Functional enrichment analysis revealed that these genes were significantly overrepresented in GO terms or KEGG pathways related to ribosomes. Validation analysis using recently published datasets revealed that the expressions of newly identified candidate reference genes were more stable than the commonly used reference genes. Based on the results, we recommended using and as the optimal reference genes for microarray and and for RNA-sequencing data validation. More importantly, the most stable should be a promising reference gene for both data types. This study, for the first time, successfully displays a large-scale microarray data driven genome-wide identification of stable reference genes for normalizing gene expression data and provides a potential guideline on the selection of universal internal reference genes in , for quantitative gene expression analysis.
为了实现准确的基因表达定量，需要将基因表达数据与可靠的参考基因进行归一化。众所周知，在不同的实验条件下，常用的参考基因的表达水平会有很大的差异，因此，它们在数据归一化中的应用受到限制。在这项研究中，基于涵盖不同发育阶段、不同组织、药物处理、生活方式和各种应激的 145 个微阵列数据集（2296 个基因阵列样本），对 中的参考基因进行了无偏识别。结果，使用六种流行的归一化算法和 方法，综合鉴定了十三个管家基因（、、、、、、、、、、、和），它们具有增强的稳定性。功能富集分析表明，这些基因在与核糖体相关的 GO 术语或 KEGG 途径中显著过表达。使用最近发表的数据集进行验证分析表明，新鉴定的候选参考基因的表达比常用的参考基因更稳定。基于这些结果，我们建议将 和 作为微阵列数据的最佳参考基因，将 和 作为 RNA-seq 数据验证的最佳参考基因。更重要的是，最稳定的 应该是两种数据类型都有前途的参考基因。这项研究首次成功地展示了大规模微阵列数据驱动的全基因组识别稳定参考基因，用于归一化基因表达数据，并为定量基因表达分析中 的通用内部参考基因选择提供了潜在的指导。
