Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model. (December 2020)

• Record Type: Journal Article
• Authors: Zhang, Tianyue; Chang, Haowu; Zhang, Borui; Liu, Sifei; Zhao, Tianheng; Zhao, Enshuang; Zhao, Hengyi; Zhang, Hao
• Is Part Of: Computational biology and chemistry. Volume 89(2020)
• Publication Date: 2020-12
• Subjects: microRNA -- Transboundary -- Regulation -- Target Genes -- Bicluster -- Differential Expression -- Random Forest; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107401
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Propose a general analysis pipeline based on machine learning and bicluster-based function module for plant-fungal pathogenic microRNA. The feasibility has been verified through the infection process of Magnaporthe oryzae and the infection process of potato late blight. It can be extended to other related crop pathogen research, providing a reference for understanding the interaction between fungi and plants. Abstract: Plant fungal diseases have been affecting the world's agricultural production and economic levels for a long time, such as rice blast, gray tomato mold, potato late blight etc. Recent studies have shown that fungal pathogens transmit microRNA as an effector to host plants for infection. However, bioassay-based verification analysis is time-consuming and challenging, and it is difficult to analyze from a global perspective. With the accumulation of fungal and plant-related data, data analysis methods can be used to analyze pathogenic fungal microRNA further. Based on the microRNA expression data of fungal pathogens infecting plants before and after, this paper discusses the selection strategy of sample data, the extraction strategy of pathogenic fungal microRNA, the prediction strategy of a fungal pathogenic microRNA target gene, the bicluster-based fungal pathogenic microRNA functional analysis strategy and experimental verification methods. A general analysis pipeline based on machine learning and bicluster-based function module was proposed for plant-fungal pathogenic microRNA.The pipeline proposed in this paper is applied to the infection process of Magnaporthe oryzae and the infection process of potato late blight. It has been verified to prove the feasibility of the pipeline. It can be extended to other relevant crop pathogen research, providing a new idea for fungal research on plant diseases. It can be used as a reference for understanding the interaction between fungi and plants.