Recently, the first knowledgebase of Reference Genes for RT-qPCR normalization — ICG was developed in the BIG Data Center at Beijing Institute of Genomics (BIG), Chinese Academy of Sciences (CAS). Based on community curation, ICG has realized the effective excavation, integration and annotation for RT-qPCR internal control genes. The project was published online in Nucleic Acids Research.
Real-time quantitative PCR (RT-qPCR) is one of the most powerful molecular techniques for accurate expression profiling of targeted nucleic acid in a wide range of biological research. To reduce experimental bias and produce accurate expression levels, the most frequently used approach for RT-qPCR normalization is the use of internal control genes. It is clearly that internal control genes are condition-specific and accordingly there is no universal gene that can be used for internal control for all application scenarios, strongly indicating the necessity of proper selection of internal control gene(s) before performing any RT-qPCR experiment.
However, characterizing internal control genes is an onerous task requiring well-designed molecular experiments followed with a series of elaborate computational analyses. Therefore, it is extremely necessary to comprehensively integrate experimentally validated internal control genes from published literature and make these genes and their associated experimental conditions well-organized and public accessible to the whole scientific community.
ICG integrates >750 experimentally validated internal control genes manually curated from 283 publications, corresponding to a wide range of specific tissues, development stages and experiment treatments and covering a wide variety of species including 73 animals, 115 plants, 12 fungi and 9 bacteria. As a core resource of BIG Data Center, ICG serves as a publicly editable and open-content encyclopedia of internal control genes and thus bears broad utility for reliable RT-qPCR normalization and gene expression characterization in both model and non-model organisms.
This research is supported by Strategic Priority Research Program of CAS, National Programs for High Technology Research and Development, National Key R&D Program of China, International Partnership Program of CAS, The 100-Talent Program of CAS, National Natural Science Foundation of China.