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BGCFlow: systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets. | LitMetric

AI Article Synopsis

  • Genome mining is transforming the search for natural products, leveraging the large amount of genomic data available to uncover biosynthetic information in bacterial pangenomes.
  • BGCFlow is a new workflow tool that organizes genome mining into five stages: data selection, functional annotation, phylogenetic analysis, genome mining, and comparative analysis, making large-scale analyses more efficient.
  • The application of BGCFlow is showcased through an analysis of the Saccharopolyspora genus, enhancing the accuracy of identifying biosynthetic gene clusters (BGCs) and facilitating targeted studies of important metabolites.

Article Abstract

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as: (i) data selection, (ii) functional annotation, (iii) phylogenetic analysis, (iv) genome mining, and (v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter Notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant, and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge from the ever-growing genomic datasets of biotechnologically relevant bacterial species.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11162802PMC
http://dx.doi.org/10.1093/nar/gkae314DOI Listing

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