Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
2019
2019
Historique:
received:
13
11
2018
accepted:
22
01
2019
entrez:
12
3
2019
pubmed:
12
3
2019
medline:
12
3
2019
Statut:
epublish
Résumé
The biosynthetic machinery responsible for the production of bacterial specialised metabolites is encoded by physically clustered group of genes called biosynthetic gene clusters (BGCs). The experimental characterisation of numerous BGCs has led to the elucidation of subclusters of genes within BGCs, jointly responsible for the same biosynthetic function in different genetic contexts. We developed an unsupervised statistical method able to successfully detect a large number of modules (putative functional subclusters) within an extensive set of predicted BGCs in a systematic and automated manner. Multiple already known subclusters were confirmed by our method, proving its efficiency and sensitivity. In addition, the resulting large collection of newly defined modules provides new insights into the prevalence and putative biosynthetic role of these modular genetic entities. The automated and unbiased identification of hundreds of co-evolving group of genes is an essential breakthrough for the discovery and biosynthetic engineering of high-value compounds.
Identifiants
pubmed: 30854475
doi: 10.1038/s42003-019-0333-6
pii: 10.1038/s42003-019-0333-6
pmc: PMC6395733
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Pagination
83Subventions
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/J014478/1
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/M017702/1
Pays : United Kingdom
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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