Cargando…
Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions
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 metabol...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130963/ https://www.ncbi.nlm.nih.gov/pubmed/33945539 http://dx.doi.org/10.1371/journal.pcbi.1008920 |
_version_ | 1783694620187688960 |
---|---|
author | Hjörleifsson Eldjárn, Grímur Ramsay, Andrew van der Hooft, Justin J. J. Duncan, Katherine R. Soldatou, Sylvia Rousu, Juho Daly, Rónán Wandy, Joe Rogers, Simon |
author_facet | Hjörleifsson Eldjárn, Grímur Ramsay, Andrew van der Hooft, Justin J. J. Duncan, Katherine R. Soldatou, Sylvia Rousu, Juho Daly, Rónán Wandy, Joe Rogers, Simon |
author_sort | Hjörleifsson Eldjárn, Grímur |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8130963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81309632021-05-27 Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions Hjörleifsson Eldjárn, Grímur Ramsay, Andrew van der Hooft, Justin J. J. Duncan, Katherine R. Soldatou, Sylvia Rousu, Juho Daly, Rónán Wandy, Joe Rogers, Simon PLoS Comput Biol Research Article 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. Public Library of Science 2021-05-04 /pmc/articles/PMC8130963/ /pubmed/33945539 http://dx.doi.org/10.1371/journal.pcbi.1008920 Text en © 2021 Hjörleifsson Eldjárn et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hjörleifsson Eldjárn, Grímur Ramsay, Andrew van der Hooft, Justin J. J. Duncan, Katherine R. Soldatou, Sylvia Rousu, Juho Daly, Rónán Wandy, Joe Rogers, Simon Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions |
title | Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions |
title_full | Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions |
title_fullStr | Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions |
title_full_unstemmed | Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions |
title_short | Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions |
title_sort | ranking microbial metabolomic and genomic links in the nplinker framework using complementary scoring functions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130963/ https://www.ncbi.nlm.nih.gov/pubmed/33945539 http://dx.doi.org/10.1371/journal.pcbi.1008920 |
work_keys_str_mv | AT hjorleifssoneldjarngrimur rankingmicrobialmetabolomicandgenomiclinksinthenplinkerframeworkusingcomplementaryscoringfunctions AT ramsayandrew rankingmicrobialmetabolomicandgenomiclinksinthenplinkerframeworkusingcomplementaryscoringfunctions AT vanderhooftjustinjj rankingmicrobialmetabolomicandgenomiclinksinthenplinkerframeworkusingcomplementaryscoringfunctions AT duncankatheriner rankingmicrobialmetabolomicandgenomiclinksinthenplinkerframeworkusingcomplementaryscoringfunctions AT soldatousylvia rankingmicrobialmetabolomicandgenomiclinksinthenplinkerframeworkusingcomplementaryscoringfunctions AT rousujuho rankingmicrobialmetabolomicandgenomiclinksinthenplinkerframeworkusingcomplementaryscoringfunctions AT dalyronan rankingmicrobialmetabolomicandgenomiclinksinthenplinkerframeworkusingcomplementaryscoringfunctions AT wandyjoe rankingmicrobialmetabolomicandgenomiclinksinthenplinkerframeworkusingcomplementaryscoringfunctions AT rogerssimon rankingmicrobialmetabolomicandgenomiclinksinthenplinkerframeworkusingcomplementaryscoringfunctions |