Co-expression network analysis identifies transcriptional modules in the mouse liver

被引:0
|
作者
Wei Liu
Hua Ye
机构
[1] Institute of Aviation Medicine of Chinese PLA Air Force,Department of Pathology, Human Centrifuge Medical Training Center
[2] Lihuili Hospital,Department of Gastroenterology
来源
关键词
Co-expression network analysis; Gene module; Hub gene; Microarray; Mouse liver;
D O I
暂无
中图分类号
学科分类号
摘要
The mouse liver transcriptome has been extensively studied but little is known about the global hepatic gene network of the mouse under normal physiological conditions. Understanding this will help reveal the transcriptional organization of the liver and elucidate its functional complexity. Here, weighted gene co-expression network analysis (WGCNA) was carried out to explore gene co-expression networks using large-scale microarray data from normal mouse livers. A total of 7,203 genes were parsed into 16 gene modules associated with protein catabolism, RNA processing, muscle contraction, transcriptional regulation, oxidation reduction, sterol biosynthesis, translation, fatty acid metabolism, immune response and others. The modules were organized into higher order co-expression groups. Hub genes in each module were found to be critical for module function. In sum, the analyses revealed the gene modular map of the mouse liver under normal physiological condition. These results provide a systems-level framework to help understand the complexity of the mouse liver at the molecular level, and should be beneficial in annotating uncharacterized genes.
引用
收藏
页码:847 / 853
页数:6
相关论文
共 50 条
  • [1] Co-expression network analysis identifies transcriptional modules in the mouse liver
    Liu, Wei
    Ye, Hua
    MOLECULAR GENETICS AND GENOMICS, 2014, 289 (05) : 847 - 853
  • [2] Rice co-expression network analysis identifies gene modules associated with agronomic traits
    Zhang, Yu
    Han, Ershang
    Peng, Yuming
    Wang, Yuzhou
    Wang, Yifan
    Geng, Zhenxing
    Xu, Yupu
    Geng, Haiying
    Qian, Yangwen
    Ma, Shisong
    PLANT PHYSIOLOGY, 2022, 190 (02) : 1526 - 1542
  • [3] Gene co-expression network analysis identifies trait-related modules in Arabidopsis thaliana
    Liu, Wei
    Lin, Liping
    Zhang, Zhiyuan
    Liu, Siqi
    Gao, Kuan
    Lv, Yanbin
    Tao, Huan
    He, Huaqin
    PLANTA, 2019, 249 (05) : 1487 - 1501
  • [4] Gene co-expression network analysis identifies trait-related modules in Arabidopsis thaliana
    Wei Liu
    Liping Lin
    Zhiyuan Zhang
    Siqi Liu
    Kuan Gao
    Yanbin Lv
    Huan Tao
    Huaqin He
    Planta, 2019, 249 : 1487 - 1501
  • [5] WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS IDENTIFIES SPECIFIC GENE MODULES RELATED TO HUMAN PREECLAMPSIA
    Tang, Renqiao
    Ouyang, Shengrong
    Wu, Jianxin
    JOURNAL OF HYPERTENSION, 2016, 34 : E520 - E521
  • [6] Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Hyperlipidemia
    Miao, Liu
    Yin, Rui-Xing
    Pan, Shang-Ling
    Yang, Shuo
    Yang, De-Zhai
    Lin, Wei-Xiong
    CELLULAR PHYSIOLOGY AND BIOCHEMISTRY, 2018, 48 (03) : 1151 - 1163
  • [7] Weighted gene co-expression network analysis identifies modules and functionally enriched pathways in the lactation process
    Farhadian, Mohammad
    Rafat, Seyed Abbas
    Panahi, Bahman
    Mayack, Christopher
    SCIENTIFIC REPORTS, 2021, 11 (01)
  • [8] Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis
    Liu, Rong
    Zhang, Wei
    Liu, Zhao-Qian
    Zhou, Hong-Hao
    BMC GENOMICS, 2017, 18
  • [9] Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis
    Rong Liu
    Wei Zhang
    Zhao-Qian Liu
    Hong-Hao Zhou
    BMC Genomics, 18
  • [10] Gene co-expression analysis identifies modules related to insufficient sleep in humans
    Ye, Hua
    Huang, Shiliang
    Song, Yufei
    Liu, Huiwei
    Zhao, Xiaosu
    Zhao, Dan
    Mi, Fangxia
    Wang, Xinxue
    Zhang, Xuesong
    Du, Jinman
    Zhu, Na
    Zhang, Liangshun
    Zhao, Yibin
    SLEEP MEDICINE, 2021, 86 : 68 - 74