ICG: a wiki-driven knowledgebase of internal control genes for RT-qPCR normalization.

Journal: Nucleic acids research
Published Date:

Abstract

Real-time quantitative PCR (RT-qPCR) has become a widely used method for accurate expression profiling of targeted mRNA and ncRNA. Selection of appropriate internal control genes for RT-qPCR normalization is an elementary prerequisite for reliable expression measurement. Here, we present ICG (http://icg.big.ac.cn), a wiki-driven knowledgebase for community curation of experimentally validated internal control genes as well as their associated experimental conditions. Unlike extant related databases that focus on qPCR primers in model organisms (mainly human and mouse), ICG features harnessing collective intelligence in community integration of internal control genes for a variety of species. Specifically, it integrates a comprehensive collection of more than 750 internal control genes for 73 animals, 115 plants, 12 fungi and 9 bacteria, and incorporates detailed information on recommended application scenarios corresponding to specific experimental conditions, which, collectively, are of great help for researchers to adopt appropriate internal control genes for their own experiments. Taken together, ICG serves as a publicly editable and open-content encyclopaedia of internal control genes and accordingly bears broad utility for reliable RT-qPCR normalization and gene expression characterization in both model and non-model organisms.

Authors

  • Jian Sang
    BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
  • Zhennan Wang
    School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, China.
  • Man Li
    Department of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, China.
  • Jiabao Cao
    BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
  • Guangyi Niu
    BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
  • Lin Xia
    Department of Pharmacy, Shanghai Changhai Hospital, Naval Medical University, Shanghai, People's Republic of China.
  • Dong Zou
    BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
  • Fan Wang
    Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
  • Xingjian Xu
    BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
  • Xiaojiao Han
    Key Laboratory of Tree Genomics, The Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400, China.
  • Jinqi Fan
    School of Ocean, Yantai University, Yantai 264005, China.
  • Ye Yang
    Department of Rehabilitation Medicine, Guilin People's Hospital, Guilin, Guangxi Zhuang Autonomous Region, China.
  • Wanzhu Zuo
    School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China.
  • Yang Zhang
    Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Wenming Zhao
    BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
  • Yiming Bao
    BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
  • Jingfa Xiao
    BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
  • Songnian Hu
    CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
  • Lili Hao
    BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
  • Zhang Zhang
    c BIG Data Center, Beijing Institute of Genomics (BIG) , Chinese Academy of Sciences , Beijing , China.