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解锁植物生物活性通路:利用omics数据和助力机器学习

Mickael Durand¹, Sébastien Besseau¹, Nicolas Papon²

¹Biomolécules et Biotechnologies Végétales, EA2106, Université de Tours, 37200 Tours, France.

Current opinion in biotechnology

|May 10, 2024

概括

利用奥米克数据和机器学习,我们对植物特种代谢途径的理解得到了进步. 这使得发现植物天然产品及其生物合成途径的新策略成为可能.

科学领域:

植物生物化学和分子生物学
代谢学和转录学.
生物信息学和计算生物学

背景情况:

植物生物活性对医药和食品工业至关重要.
高质量的奥米克数据 (代谢学,转录学) 对于研究植物代谢途径至关重要.
之前的方法成功地使用omics数据识别了植物天然产品 (PNP) 生物合成途径.

研究的目的:

审查最近在破译植物专用生物合成途径方面的进展.
突出OMIC数据集成和机器学习在这个领域的影响.
探索发现和破坏路径的新机会.

主要方法:

关于植物科学中omics数据应用的当前文献的综述.
分析机器学习技术应用于代谢学和转录学数据的分析.
讨论用于路径阐明的综合数据方法.

主要成果:

当Omics数据得到有效利用时,有助于揭示PNP生物合成途径.
机器学习使生物数据分析民主化,为路径探索提供了新的途径.
最近的突破证明了这些技术在破坏专门的生物合成途径方面的潜力.

结论:

奥米克数据和机器学习之间的协同作用显著增强了对植物特种代谢途径的探索.
这些综合方法是释放植物生物活性的全部潜力的关键.
未来的研究很可能会专注于进一步完善这些计算策略,用于更广泛的应用.

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