Benefiting from big data in natural products: importance of preserving foundational skills and prioritizing data quality. Issue 11 (4th November 2021)

• Record Type: Journal Article
• Authors: Cech, Nadja B.; Medema, Marnix H.; Clardy, Jon
• Is Part Of: Natural product reports. Volume 38:Issue 11(2021)
• Page Start: 1947
• Page End: 1953
• Publication Date: 2021-11-04
• Subjects: Natural products -- Periodicals; Produits naturels -- Périodiques; 547.7
• DOI: 10.1039/d1np00061f
• Languages: English
• ISSNs: 0265-0568

Abstract

Abstract : Big data is changing how we do natural products research and creating exciting new possibilities. Continued attention to enhancing data quality, increasing access, and preserving foundational skills is needed. Abstract : Systematic, large-scale, studies at the genomic, metabolomic, and functional level have transformed the natural product sciences. Improvements in technology and reduction in cost for obtaining spectroscopic, chromatographic, and genomic data coupled with the creation of readily accessible curated and functionally annotated data sets have altered the practices of virtually all natural product research laboratories. Gone are the days when the natural products researchers were expected to devote themselves exclusively to the isolation, purification, and structure elucidation of small molecules. We now also engage with big data in taxonomic, genomic, proteomic, and/or metabolomic collections, and use these data to generate and test hypotheses. While the oft stated aim for the use of large-scale -omics data in the natural products sciences is to achieve a rapid increase in the rate of discovery of new drugs, this has not yet come to pass. At the same time, new technologies have provided unexpected opportunities for natural products chemists to ask and answer new and different questions. With this viewpoint, we discuss the evolution of big data as a part of natural products research and provide a few examples of how discoveries have been enabled by access to big data. We also draw attention to some of the limitations in our existing engagement with large datasets and consider what would be necessary to overcome them.