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Metabolites 2017, 7(3), 34; https://doi.org/10.3390/metabo7030034

Natural Product Discovery Using Planes of Principal Component Analysis in R (PoPCAR)

1
Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI 53705, USA
2
Exploratory Science Center, Merck & Co., 320 Bent St., Cambridge, MA 02141, USA
*
Author to whom correspondence should be addressed.
Academic Editor: RuAngelie Edrada-Ebel
Received: 30 April 2017 / Revised: 20 June 2017 / Accepted: 11 July 2017 / Published: 13 July 2017
(This article belongs to the Special Issue Marine Metabolomics)
View Full-Text   |   Download PDF [2771 KB, uploaded 13 July 2017]   |  

Abstract

Rediscovery of known natural products hinders the discovery of new, unique scaffolds. Efforts have mostly focused on streamlining the determination of what compounds are known vs. unknown (dereplication), but an alternative strategy is to focus on what is different. Utilizing statistics and assuming that common actinobacterial metabolites are likely known, focus can be shifted away from dereplication and towards discovery. LC-MS-based principal component analysis (PCA) provides a perfect tool to distinguish unique vs. common metabolites, but the variability inherent within natural products leads to datasets that do not fit ideal standards. To simplify the analysis of PCA models, we developed a script that identifies only those masses or molecules that are unique to each strain within a group, thereby greatly reducing the number of data points to be inspected manually. Since the script is written in R, it facilitates integration with other metabolomics workflows and supports automated mass matching to databases such as Antibase. View Full-Text
Keywords: metabolomics; principal component analysis; actinobacteria; marine actinomycetes; mass spectrometry metabolomics; principal component analysis; actinobacteria; marine actinomycetes; mass spectrometry
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Chanana, S.; Thomas, C.S.; Braun, D.R.; Hou, Y.; Wyche, T.P.; Bugni, T.S. Natural Product Discovery Using Planes of Principal Component Analysis in R (PoPCAR). Metabolites 2017, 7, 34.

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