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Related Experiment Video

Updated: Jul 12, 2025

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A Relationship Prediction Method for Magnaporthe oryzae-Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder.

Enshuang Zhao1, Liyan Dong1,2, Hengyi Zhao1

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China.

Journal of Fungi (Basel, Switzerland)
|October 27, 2023
PubMed
Summary

A new method, Weighted Gene Autoencoder Multi-Omics Relationship Prediction (WGAEMRP), integrates rice and Magnaporthe oryzae Oryzae (MoO) multi-omics data. This approach enhances understanding of rice-fungal interactions and disease resistance mechanisms.

Keywords:
Magnaporthe oryzae Oryzae (MoO) pathotypeWGCNAcross-kingdom regulationgraph autoencodermulti-omicsrice

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Area of Science:

  • Plant Pathology
  • Bioinformatics
  • Genomics

Background:

  • Magnaporthe oryzae Oryzae (MoO) is a major rice pathogen, but its infection mechanisms are not fully understood.
  • Single-omics data is insufficient to elucidate complex cross-kingdom interactions between MoO and rice.
  • Existing methods lack the capacity for integrated multi-omics analysis.

Purpose of the Study:

  • To develop a novel computational method for integrating multi-omics data from MoO and rice.
  • To construct a comprehensive interaction network revealing key biomolecules involved in rice-fungus pathogenesis.
  • To provide a foundation for understanding MoO infection and developing disease-resistant rice.

Main Methods:

  • Proposed Weighted Gene Autoencoder Multi-Omics Relationship Prediction (WGAEMRP).
  • Combined weighted gene co-expression network analysis (WGCNA) with graph autoencoder.
  • Applied WGAEMRP to MoO-rice multi-omics data to build a heterogeneous interaction network.

Main Results:

  • Constructed a MoO-rice multi-omics heterogeneous interaction network.
  • Identified key biomolecules: 18 MoO small RNAs (sRNAs), 17 rice genes, 26 rice mRNAs, and 28 rice proteins.
  • Discovered functional modules and pathways related to gene expression, protein dynamics, and metabolism during infection.

Conclusions:

  • WGAEMRP significantly improves multi-omics data integration efficiency and accuracy.
  • The study provides a robust data foundation for investigating MoO pathogenesis.
  • Findings offer new insights for developing novel strategies for disease-resistant rice varieties.