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2000
Volume 20, Issue 7
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Background

Due to infection by the rice blast fungus, rice, a major global staple, faces yield challenges. While chemical control methods are common, their environmental and economic costs are growing concerns. Traditional biological experiments are also inefficient for exploring resistance genes. Therefore, understanding the interaction between rice and the rice blast fungus is urgent and important.

Objective

This study aims to use multi-omics data to uncover key elements in rice's defense against rice blast fungus . We built a detailed, multi-layered heterogeneous interaction network, employing an innovative graph embedding feature with a cross-layer random walk algorithm to identify crucial crucial resistance factors. This could inform strategies for enhancing disease resistance in rice.

Methods

We integrated genomics, transcriptomics, and proteomics data on infecting rice. This multi-omics data was used to construct a multi-layer heterogeneous network. An advanced graph embedding algorithm (BINE) provided rich vector representations of network nodes. A multi-layer network walking algorithm was then used to analyze the network and identify key regulatory small RNA (sRNAs) in rice.

Results

Node similarity rankings allowed us to identify significant regulatory sRNAs in rice that are integral to disease resistance. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses further revealed their roles in biological processes and key metabolic pathways. Our integrative method precisely and efficiently identified these crucial elements, offering a valuable systems biology tool.

Conclusion

By integrating multi-omics data with computational analysis, this study reveals key regulatory sRNAs in rice's disease resistance mechanism. These findings enhance our understanding of rice disease resistance and provide genetic resources for breeding disease-resistant rice. Despite limitations in sRNA functional interpretation, this research demonstrates the power of applying multi-omics data to address complex biological problems.

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2024-07-22
2025-08-17

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A Novel Method for Mining Regulatory sRNAs Related to Rice Resistance Against Blast Fungus from Multi-Omics Data
Curr Bioinform 20, 581 (2025); https://doi.org/10.2174/0115748936305102240705052723
/content/journals/cbio/10.2174/0115748936305102240705052723
/content/journals/cbio/10.2174/0115748936305102240705052723
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  • Article Type:     Research Article
Keyword(s): Deep learning; Magnaporthe oryzae; Magnaporthe oryzae-rice interaction; multi-omics; random walk; rice blast fungus; sRNA

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