Robotics & Machine Learning Daily News2024,Issue(Jun.4) :85-86.

Chinese Academy of Agricultural Sciences Reports Findings in Machine Learning (E xploring salt tolerance mechanisms using machine learning for transcriptomic ins ights: case study in Spartina alterniflora)

中国农业科学院报告了机器学习的研究结果(利用机器学习研究转录组信息的耐盐性机制:互花米草的案例研究)

Robotics & Machine Learning Daily News2024,Issue(Jun.4) :85-86.

Chinese Academy of Agricultural Sciences Reports Findings in Machine Learning (E xploring salt tolerance mechanisms using machine learning for transcriptomic ins ights: case study in Spartina alterniflora)

中国农业科学院报告了机器学习的研究结果(利用机器学习研究转录组信息的耐盐性机制:互花米草的案例研究)

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摘要

机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx记者从中华人民共和国北京发回的新闻报道,研究表明:“盐胁迫对全球谷类作物生产构成了重大威胁,强调了全面了解耐盐机制的必要性。准确的差异表达基因功能注释对于深入了解耐盐机制至关重要。”我们的新闻编辑从中国农业科学院的一篇研究中获得了一句话,“预测欠种基因功能的挑战仍然存在,特别是排除不常见的GO项时,我们建议使用NetGo3.0,这是一种基于机器学习的注释方法,不依赖于种间同源信息,来预测盐胁迫下差异表达基因的功能。”摘要:盐生植物具有盐腺,表现出极强的耐盐性,是深入研究盐胁迫下转录体的理想候选基因。然而,目前对盐胁迫下转录体的研究还很有限。本文以盐生植物为例,研究了盐生植物在不同盐浓度下的转录反应,重点探讨了盐生植物的耐盐性机制。转录体分析揭示了影响基因转录、转录和转录等关键途径的重大变化。离子转运和ROS代谢。值得注意的是,我们在中鉴定了基因家族的一个成员,显示了与水稻同源物的趋同选择。此外,我们的全基因组分析探索了对盐胁迫的选择性剪接反应,为选择性剪接和转录调节在提高耐盐性方面的平行功能提供了见解。令人惊讶的是,盐暴露后差异表达和差异剪接基因之间的重叠最小。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Beijing, Peo ple’s Republic of China, by NewsRx correspondents, research stated, “Salt stress poses a significant threat to global cereal crop production, emphasizing the ne ed for a comprehensive understanding of salt tolerance mechanisms. Accurate func tional annotations of differentially expressed genes are crucial for gaining ins ights into the salt tolerance mechanism.” Our news editors obtained a quote from the research from the Chinese Academy of Agricultural Sciences, “The challenge of predicting gene functions in under-stud ied species, especially when excluding infrequent GO terms, persists. Therefore, we proposed the use of NetGO 3.0, a machine learning-based annotation method th at does not rely on homology information between species, to predict the functio ns of differentially expressed genes under salt stress. , a halophyte with salt glands, exhibits remarkable salt tolerance, making it an excellent candidate for in-depth transcriptomic analysis. However, current research on the transcriptom e under salt stress is limited. In this study we used as an example to investiga te its transcriptional responses to various salt concentrations, with a focus on understanding its salt tolerance mechanisms. Transcriptomic analysis revealed s ubstantial changes impacting key pathways, such as gene transcription, ion trans port, and ROS metabolism. Notably, we identified a member of the gene family in , showing convergent selection with the rice ortholog. Additionally, our genome- wide analyses explored alternative splicing responses to salt stress, providing insights into the parallel functions of alternative splicing and transcriptional regulation in enhancing salt tolerance in. Surprisingly, there was minimal over lap between differentially expressed and differentially spliced genes following salt exposure.”

Key words

Beijing/People’s Republic of China/Asi a/Cyborgs/Emerging Technologies/Genetics/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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