ArticleCommunications biology2019
Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters.
Article in Communications biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Bacterial biosynthetic superclusters: emerging drivers of synergistic natural products against antimicrobial resistance.Essays in biochemistry · 2026Review
- Neofunctionalization underlies the evolutionary origin of sclareol biosynthesis in the mint family.Nature communications · 2026Article
- Sequence modeling tools to decode the biosynthetic diversity of the human microbiome.mSystems · 2025Review
- Context matters: assessing the impacts of genomic background and ecology on microbial biosynthetic gene cluster evolution.mSystems · 2025Review
- PanBGC: a pangenome-inspired framework for comparative analysis of biosynthetic gene clusters.ISME communications · 2025Article
- iPRESTO: Automated discovery of biosynthetic sub-clusters linked to specific natural product substructures.PLoS computational biology · 2023Article
- An isotopic labeling approach linking natural products with biosynthetic gene clusters.Nature chemical biology · 2022Article
- Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.Natural product reports · 2021Review
- FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.PLoS computational biology · 2021Article
- Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery.mSystems · 2021Article
- BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters.GigaScience · 2021Article
- The Architecture of Metabolism Maximizes Biosynthetic Diversity in the Largest Class of Fungi.Molecular biology and evolution · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
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Registered trials
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