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A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder
Magnaporthe oryzae Oryzae (MoO) pathotype is a devastating fungal pathogen of rice; however, its pathogenic mechanism remains poorly understood. The current research is primarily focused on single-omics data, which is insufficient to capture the complex cross-kingdom regulatory interactions between...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10607591/ https://www.ncbi.nlm.nih.gov/pubmed/37888263 http://dx.doi.org/10.3390/jof9101007 |
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author | Zhao, Enshuang Dong, Liyan Zhao, Hengyi Zhang, Hao Zhang, Tianyue Yuan, Shuai Jiao, Jiao Chen, Kang Sheng, Jianhua Yang, Hongbo Wang, Pengyu Li, Guihua Qin, Qingming |
author_facet | Zhao, Enshuang Dong, Liyan Zhao, Hengyi Zhang, Hao Zhang, Tianyue Yuan, Shuai Jiao, Jiao Chen, Kang Sheng, Jianhua Yang, Hongbo Wang, Pengyu Li, Guihua Qin, Qingming |
author_sort | Zhao, Enshuang |
collection | PubMed |
description | Magnaporthe oryzae Oryzae (MoO) pathotype is a devastating fungal pathogen of rice; however, its pathogenic mechanism remains poorly understood. The current research is primarily focused on single-omics data, which is insufficient to capture the complex cross-kingdom regulatory interactions between MoO and rice. To address this limitation, we proposed a novel method called Weighted Gene Autoencoder Multi-Omics Relationship Prediction (WGAEMRP), which combines weighted gene co-expression network analysis (WGCNA) and graph autoencoder to predict the relationship between MoO–rice multi-omics data. We applied WGAEMRP to construct a MoO–rice multi-omics heterogeneous interaction network, which identified 18 MoO small RNAs (sRNAs), 17 rice genes, 26 rice mRNAs, and 28 rice proteins among the key biomolecules. Most of the mined functional modules and enriched pathways were related to gene expression, protein composition, transportation, and metabolic processes, reflecting the infection mechanism of MoO. Compared to previous studies, WGAEMRP significantly improves the efficiency and accuracy of multi-omics data integration and analysis. This approach lays out a solid data foundation for studying the biological process of MoO infecting rice, refining the regulatory network of pathogenic markers, and providing new insights for developing disease-resistant rice varieties. |
format | Online Article Text |
id | pubmed-10607591 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106075912023-10-28 A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder Zhao, Enshuang Dong, Liyan Zhao, Hengyi Zhang, Hao Zhang, Tianyue Yuan, Shuai Jiao, Jiao Chen, Kang Sheng, Jianhua Yang, Hongbo Wang, Pengyu Li, Guihua Qin, Qingming J Fungi (Basel) Article Magnaporthe oryzae Oryzae (MoO) pathotype is a devastating fungal pathogen of rice; however, its pathogenic mechanism remains poorly understood. The current research is primarily focused on single-omics data, which is insufficient to capture the complex cross-kingdom regulatory interactions between MoO and rice. To address this limitation, we proposed a novel method called Weighted Gene Autoencoder Multi-Omics Relationship Prediction (WGAEMRP), which combines weighted gene co-expression network analysis (WGCNA) and graph autoencoder to predict the relationship between MoO–rice multi-omics data. We applied WGAEMRP to construct a MoO–rice multi-omics heterogeneous interaction network, which identified 18 MoO small RNAs (sRNAs), 17 rice genes, 26 rice mRNAs, and 28 rice proteins among the key biomolecules. Most of the mined functional modules and enriched pathways were related to gene expression, protein composition, transportation, and metabolic processes, reflecting the infection mechanism of MoO. Compared to previous studies, WGAEMRP significantly improves the efficiency and accuracy of multi-omics data integration and analysis. This approach lays out a solid data foundation for studying the biological process of MoO infecting rice, refining the regulatory network of pathogenic markers, and providing new insights for developing disease-resistant rice varieties. MDPI 2023-10-12 /pmc/articles/PMC10607591/ /pubmed/37888263 http://dx.doi.org/10.3390/jof9101007 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhao, Enshuang Dong, Liyan Zhao, Hengyi Zhang, Hao Zhang, Tianyue Yuan, Shuai Jiao, Jiao Chen, Kang Sheng, Jianhua Yang, Hongbo Wang, Pengyu Li, Guihua Qin, Qingming A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder |
title | A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder |
title_full | A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder |
title_fullStr | A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder |
title_full_unstemmed | A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder |
title_short | A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder |
title_sort | relationship prediction method for magnaporthe oryzae–rice multi-omics data based on wgcna and graph autoencoder |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10607591/ https://www.ncbi.nlm.nih.gov/pubmed/37888263 http://dx.doi.org/10.3390/jof9101007 |
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