Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions (original) (raw)
Hjorleifsson Eldjarn, Grimur, Ramsay, Andrew 
ORCID: https://orcid.org/0000-0002-1451-8973, Van Der Hooft, Justin ORCID: https://orcid.org/0000-0002-9340-5511, Duncan, Katherine R., Soldatou, Sylvia, Rousu, Juho, Daly, Ronan ORCID: https://orcid.org/0000-0002-1275-6820, Wandy, Joe ORCID: https://orcid.org/0000-0002-3068-4664 and Rogers, Simon ORCID: https://orcid.org/0000-0003-3578-4477(2021) Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions.PLoS Computational Biology, 17(5), e1008920. (doi: 10.1371/journal.pcbi.1008920) (PMID:33945539) (PMCID:PMC8130963)
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.
| Item Type: | Articles |
|---|---|
| Additional Information: | JJJvdH acknowledges an ASDI grant from the Netherlands eScience Center - NLeSC (grant no. ASDI.2017.030, https://www.esciencecenter.nl/). AR, KRD and SR acknowledge funding from the Biotechnology and Biological Sciences Research Council (BB/R022054/1, https://bbsrc.ukri.org/). KRD, SR and SS are supported by a Carnegie Trust Collaborative Research Grant (https://www.carnegie-trust.org/). JR acknowledges funding from the Academy of Finland (grants 310107 and 334790, https://www.aka.fi/) and Scottish Informatics and Computing Science Alliance (SICSA) distinguished visiting fellow scheme (https://www.sicsa.ac.uk/). |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Hjorleifsson Eldjarn, Mr Grimur and Ramsay, Mr Andrew and Wandy, Dr Joe and Daly, Dr Ronan and Van Der Hooft, Mr Justin and Rogers, Dr Simon |
| Creator Roles: | Hjorleifsson Eldjarn, G.Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review and editingRamsay, A.Data curation, Software, VisualizationVan Der Hooft, J.Conceptualization, Validation, Writing – review and editingDaly, R.SoftwareWandy, J.SoftwareRogers, S.Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Writing – review and editing |
| Authors: | Hjorleifsson Eldjarn, G., Ramsay, A., Van Der Hooft, J., Duncan, K. R., Soldatou, S., Rousu, J., Daly, R., Wandy, J., and Rogers, S. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Infection & ImmunityCollege of Science and Engineering > School of Computing Science |
| Journal Name: | PLoS Computational Biology |
| Publisher: | Public Library of Science |
| ISSN: | 1553-734X |
| ISSN (Online): | 1553-7358 |
| Published Online: | 04 May 2021 |
| Copyright Holders: | Copyright © 2021 Hjörleifsson Eldjárn et al. |
| First Published: | First published in PLoS Computational Biology 17(5):e1008920 |
| Publisher Policy: | Reproduced under a Creative Commons Licence |
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Funder and Project Information
Combatting antimicrobial resistance through new software for natural product discovery
Simon Rogers
BB/R022054/1
Computing Science
Deposit and Record Details
| ID Code: | 241348 |
|---|---|
| Depositing User: | Mr Matt Mahon |
| Datestamp: | 12 May 2021 14:06 |
| Last Modified: | 23 Sep 2022 14:51 |
| Date of acceptance: | 22 April 2021 |
| Date of first online publication: | 4 May 2021 |
| Date Deposited: | 12 May 2021 |
| Data Availability Statement: | Yes |