Microbial natural product databases: moving forward in the multi-omics era.
Microbial natural product databases: moving forward in the multi-omics era.
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DOI:
10.1039/d0np00053a
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发表时间:
2021-01-01
影响因子:
11.9
通讯作者:
Linington RG
中科院分区:
文献类型:
--
作者:
van Santen JA;Kautsar SA;Medema MH;Linington RG
The digital revolution is driving significant changes in how people store, distribute, and use information. With the advent of new technologies around linked data, machine learning and large-scale network inference, the natural products research field is beginning to embrace real-time sharing and large-scale analysis of digitized experimental data. Databases play a key role in this, as they allow systematic annotation and storage of data for both basic and advanced applications. The quality of the content, structure, and accessibility of these databases all contribute to their usefulness for the scientific community in practice. This review covers the development of databases relevant for microbial natural product discovery during the past decade (2010-2020), including repositories of chemical structures/properties, metabolomics, and genomic data (biosynthetic gene clusters). It provides an overview of the most important databases and their functionalities, highlights some early meta-analyses using such databases, and discusses some basic principles to enable widespread interoperability between databases. Furthermore, it points out conceptual and practical challenges in the curation and usage of natural products databases. Finally, the review closes with a discussion of key action points required for the field moving forward, not only for database developers but for any scientist active in the field. Online databases are becoming key to natural product research, as publication of data is increasingly digitized. Here, we review databases of chemical structures, gene clusters and analytical data, and discuss key challenges and opportunities.
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影响因子:
14.9
作者:
Blin K;Medema MH;Kazempour D;Fischbach MA;Breitling R;Takano E;Weber T
通讯作者:
Weber T
影响因子:
14.9
作者:
Chen IA;Chu K;Palaniappan K;Pillay M;Ratner A;Huang J;Huntemann M;Varghese N;White JR;Seshadri R;Smirnova T;Kirton E;Jungbluth SP;Woyke T;Eloe-Fadrosh EA;Ivanova NN;Kyrpides NC
通讯作者:
Kyrpides NC
影响因子:
8.6
作者:
Chambers J;Davies M;Gaulton A;Hersey A;Velankar S;Petryszak R;Hastings J;Bellis L;McGlinchey S;Overington JP
通讯作者:
Overington JP
影响因子:
--
作者:
Epstein SC;Charkoudian LK;Medema MH
通讯作者:
Medema MH
影响因子:
14.9
作者:
Conway KR;Boddy CN
通讯作者:
Boddy CN