In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 5 ( 2021-5-4), p. e1008920-
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.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1008920
DOI:
10.1371/journal.pcbi.1008920.g001
DOI:
10.1371/journal.pcbi.1008920.g002
DOI:
10.1371/journal.pcbi.1008920.g003
DOI:
10.1371/journal.pcbi.1008920.g004
DOI:
10.1371/journal.pcbi.1008920.g005
DOI:
10.1371/journal.pcbi.1008920.g006
DOI:
10.1371/journal.pcbi.1008920.g007
DOI:
10.1371/journal.pcbi.1008920.g008
DOI:
10.1371/journal.pcbi.1008920.t001
DOI:
10.1371/journal.pcbi.1008920.t002
DOI:
10.1371/journal.pcbi.1008920.t003
DOI:
10.1371/journal.pcbi.1008920.t004
DOI:
10.1371/journal.pcbi.1008920.s001
DOI:
10.1371/journal.pcbi.1008920.s002
DOI:
10.1371/journal.pcbi.1008920.s003
DOI:
10.1371/journal.pcbi.1008920.s004
DOI:
10.1371/journal.pcbi.1008920.s005
DOI:
10.1371/journal.pcbi.1008920.s006
DOI:
10.1371/journal.pcbi.1008920.s007
DOI:
10.1371/journal.pcbi.1008920.s008
DOI:
10.1371/journal.pcbi.1008920.s009
DOI:
10.1371/journal.pcbi.1008920.s010
DOI:
10.1371/journal.pcbi.1008920.s011
DOI:
10.1371/journal.pcbi.1008920.r001
DOI:
10.1371/journal.pcbi.1008920.r002
DOI:
10.1371/journal.pcbi.1008920.r003
DOI:
10.1371/journal.pcbi.1008920.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2193340-6
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