Pairing omics to decode the diversity of plant specialized metabolism

被引:0
|
作者
Wolters, Felicia C. [1 ,2 ]
Del Pup, Elena [1 ]
Singh, Kumar Saurabh [1 ,3 ,4 ,5 ]
Bouwmeester, Klaas [2 ]
Schranz, M. Eric [2 ]
van der Hooft, Justin J. J. [1 ]
Medema, Marnix H. [1 ]
机构
[1] Wageningen Univ & Res, Bioinformat Grp, Wageningen, Netherlands
[2] Wageningen Univ & Res, Biosystemat Grp, Wageningen, Netherlands
[3] Univ Utrecht, Inst Environm Biol, Plant Microbe Interact, Utrecht, Netherlands
[4] Univ Exeter, Fac Environm Sci & Econ, Engn Dept, Penryn TR10 9FE, Cornwall, England
[5] Maastricht Univ, Brightlands Future Farming Inst, Fac Sci & Engn, Plant Funct Genom Grp, NL-5928 SX Venlo, Netherlands
关键词
Multi-omics; Plant science; Specialized metabolism; Paired omics; Multi-omics integration; GENE-CLUSTER; METABOLOMICS; PATHWAY; IDENTIFICATION; PREDICTION; NETWORKS; DATABASE; DEFENSE;
D O I
10.1016/j.pbi.2024.102657
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Plants have evolved complex bouquets of specialized natural products that are utilized in medicine, agriculture, and industry. Untargeted natural product discovery has benefitted from growing plant omics data resources. Yet, plant genome complexity limits the identification and curation of biosynthetic pathways via single omics. Pairing multi-omics types within experiments provides multiple layers of evidence for biosynthetic pathway mining. The extraction of paired biological information facilitates connecting genes to transcripts and metabolites, especially when captured across time points, conditions and chemotypes. Experimental design requires specific adaptations to enable effective paired-omics analysis. Ultimately, metadata standards are required to support the integration of paired and unpaired public datasets and to accelerate collaborative efforts for natural product discovery in the plant research community.
引用
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页数:9
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