Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions

Grimur Hjorleifsson Eldjarn, Andrew Ramsay, Justin J.J. Van Der Hooft, Katherine R. Duncan, Sylvia Soldatou, Juho Rousu, Ronan Daly, Joe Wandy, Simon Rogers*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)
114 Downloads (Pure)


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.

Original languageEnglish
Article numbere1008920
Pages (from-to)1-24
Number of pages24
JournalPLoS computational biology
Issue number5
Publication statusPublished - May 2021
MoE publication typeA1 Journal article-refereed


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