Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
Chem Soc Rev. 2020 Jun 7;49(11):3297-3314. doi: 10.1039/d0cs00162g. Epub 2020 May 12.
Microbial and plant specialized metabolites constitute an immense chemical diversity, and play key roles in mediating ecological interactions between organisms. Also referred to as natural products, they have been widely applied in medicine, agriculture, cosmetic and food industries. Traditionally, the main discovery strategies have centered around the use of activity-guided fractionation of metabolite extracts. Increasingly, omics data is being used to complement this, as it has the potential to reduce rediscovery rates, guide experimental work towards the most promising metabolites, and identify enzymatic pathways that enable their biosynthetic production. In recent years, genomic and metabolomic analyses of specialized metabolic diversity have been scaled up to study thousands of samples simultaneously. Here, we survey data analysis technologies that facilitate the effective exploration of large genomic and metabolomic datasets, and discuss various emerging strategies to integrate these two types of omics data in order to further accelerate discovery.
微生物和植物的特殊代谢物构成了巨大的化学多样性，并在调节生物之间的生态相互作用方面发挥着关键作用。也被称为天然产物，它们已广泛应用于医学、农业、化妆品和食品工业。传统上，主要的发现策略集中在使用基于活性的代谢物提取物的分离。越来越多的是，组学数据也被用来补充这一点，因为它有可能降低重新发现的速度，指导实验工作朝着最有前途的代谢物方向发展，并识别出使它们生物合成生产成为可能的酶途径。近年来，对特殊代谢多样性的基因组和代谢组学分析已经扩大规模，以同时研究数千个样本。在这里，我们调查了数据分析技术，这些技术有助于有效探索大型基因组和代谢组数据集，并讨论了各种新兴的策略来整合这两种类型的组学数据，以进一步加速发现。
