从多组学数据中挖掘与水稻抗瘟性相关的sRNA调控因子的新方法

IF 2.4 3区 生物学 Q3 BIOCHEMICAL RESEARCH METHODS
Jianhua Sheng, Enshuang Zhao, Yuheng Zhu, Yinfei Dai, Borui Zhang, Qingming Qin, Hao Zhang
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引用次数: 0

摘要

背景:由于稻瘟病真菌的感染,作为全球主要主食的水稻面临产量挑战。虽然化学防治方法很普遍,但其环境和经济成本越来越令人担忧。传统的生物学实验在探索抗性基因方面也效率低下。因此,了解水稻与稻瘟病菌之间的相互作用显得尤为迫切和重要。研究目的本研究旨在利用多组学数据揭示水稻防御稻瘟病真菌 Magnaporthe oryzae 的关键因素。我们建立了一个详细的多层异质相互作用网络,利用创新的图嵌入特征和跨层随机行走算法来识别关键的重要抗性因子:本研究旨在利用多组学数据揭示水稻防御稻瘟病真菌 Magnaporthe oryzae 的关键因素。我们利用创新的图嵌入特征和跨层随机行走算法,构建了一个详细的多层异质相互作用网络,以确定关键的重要抗病因子。这可以为提高水稻抗病性的策略提供参考。研究方法我们整合了感染水稻的 Magnaporthe oryzae 的基因组学、转录组学和蛋白质组学数据。先进的图嵌入算法(BINE)为网络节点提供了丰富的向量表示。先进的图嵌入算法(BINE)提供了丰富的网络节点向量表示,然后使用多层网络行走算法对网络进行分析,找出水稻中的关键调控小 RNA(sRNA)。结果通过节点相似性排名,我们确定了水稻中与抗病性密不可分的重要调控 sRNA。基因本体(GO)和京都基因组百科全书(KEGG)分析进一步揭示了它们在生物过程和关键代谢途径中的作用。结论通过将多组学数据与计算分析相结合,本研究揭示了水稻抗病机制中的关键调控 sRNA。这些发现加深了我们对水稻抗病性的理解,并为培育抗病水稻提供了遗传资源。尽管在 sRNA 功能解释方面存在局限性,但这项研究展示了应用多组学数据解决复杂生物学问题的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Method for Mining Regulatory sRNAs Related to Rice Resistance Against Blast Fungus from Multi-Omics Data
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 Magnaporthe oryzae. 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. objective: This study aims to use multi-omics data to uncover key elements in rice's defense against rice blast fungus Magnaporthe oryzae. 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 Magnaporthe oryzae 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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来源期刊
Current Bioinformatics
Current Bioinformatics 生物-生化研究方法
CiteScore
6.60
自引率
2.50%
发文量
77
审稿时长
>12 weeks
期刊介绍: Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth/mini-reviews, research papers and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science. The journal focuses on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.
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