aalto1 untyped-item.component.html
Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
24
Series
PLoS Computational Biology, Volume 17, issue 5, pp. 1-24
Abstract
Specialised metabolites from microbial sources are well-known for their wide range of biomedical applications, particularly as antibiotics. When mining paired genomic and metabolomic data sets for novel specialised metabolites, establishing links between Biosynthetic Gene Clusters (BGCs) and metabolites represents a promising way of finding such novel chemistry. However, due to the lack of detailed biosynthetic knowledge for the majority of predicted BGCs, and the large number of possible combinations, this is not a simple task. This problem is becoming ever more pressing with the increased availability of paired omics data sets. Current tools are not effective at identifying valid links automatically, and manual verification is a considerable bottleneck in natural product research. We demonstrate that using multiple link-scoring functions together makes it easier to prioritise true links relative to others. Based on standardising a commonly used score, we introduce a new, more effective score, and introduce a novel score using an Input-Output Kernel Regression approach. Finally, we present NPLinker, a software framework to link genomic and metabolomic data. Results are verified using publicly available data sets that include validated links.
Description
Publisher Copyright: © 2021 Public Library of Science. All rights reserved.
Keywords
Other note
Citation
Eldjarn, G H, Ramsay, A, Van Der Hooft, J J J, Duncan, K R, Soldatou, S, Rousu, J, Daly, R, Wandy, J & Rogers, S 2021, 'Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions', PLoS Computational Biology, vol. 17, no. 5, e1008920, pp. 1-24. https://doi.org/10.1371/journal.pcbi.1008920