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Drought Stress-Related Gene Identification in Rice by Random Walk with Restart on Multiplex Biological Networks

Author

Listed:
  • Liu Zhu

    (College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China)

  • Hongyan Zhang

    (College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China
    Hunan Engineering and Technology Research Center for Agricultural Big Data Analysis and Decision-Making, Hunan Agricultural University, Changsha 410128, China)

  • Dan Cao

    (College of Science, Central South University of Forestry and Technology, Changsha 410004, China)

  • Yalan Xu

    (College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China)

  • Lanzhi Li

    (Hunan Engineering and Technology Research Center for Agricultural Big Data Analysis and Decision-Making, Hunan Agricultural University, Changsha 410128, China)

  • Zilan Ning

    (College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China)

  • Lei Zhu

    (College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China)

Abstract

Drought stress-related gene identification is vital in revealing the drought resistance mechanisms underlying rice and for cultivating rice-resistant varieties. Traditional methods, such as Genome-Wide Association Studies (GWAS), usually identify hundreds of candidate stress genes, and further validation by biological experiements is then time-consuming and laborious. However, computational and prioritization methods can effectively reduce the number of candidate stress genes. This study introduces a random walk with restart algorithm (RWR), a state-of-the-art guilt-by-association method, to operate on rice multiplex biological networks. It explores the physical and functional interactions between biological molecules at different levels and prioritizes a set of potential genes. Firstly, we integrated a Protein–Protein Interaction (PPI) network, constructed by multiple protein interaction data, with a gene coexpression network into a multiplex network. Then, we implemented the RWR on multiplex networks (RWR-M) with known drought stress genes as seed nodes to identify potential drought stress-related genes. Finally, we conducted association analysis between the potential genes and the known drought stress genes. Thirteen genes were identified as rice drought stress-related genes, five of which have been reported in the recent literature to be involved in drought stress resistance mechanisms.

Suggested Citation

  • Liu Zhu & Hongyan Zhang & Dan Cao & Yalan Xu & Lanzhi Li & Zilan Ning & Lei Zhu, 2022. "Drought Stress-Related Gene Identification in Rice by Random Walk with Restart on Multiplex Biological Networks," Agriculture, MDPI, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:53-:d:1013620
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    References listed on IDEAS

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    1. Federico Belotti & Franco Peracchi, 2020. "Fast leave-one-out methods for inference, model selection, and diagnostic checking," Stata Journal, StataCorp LP, vol. 20(4), pages 785-804, December.
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