ArticleNucleic acids research2024
BGCFlow: systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
- An integrative chemical and genomic similarity approach linking fungal secondary metabolites and biosynthetic gene clusters.Organic & biomolecular chemistry · 2026Article
- Structure of theMicrobiology spectrum · 2026Article
- From niche to niche: investigating microbial communities and their specialised metabolite gene clusters in human microbiomes.World journal of microbiology & biotechnology · 2026Article
- BiG-SCAPE 2.0 and BiG-SLiCE 2.0: scalable, accurate and interactive sequence clustering of metabolic gene clusters.Nature communications · 2026Article
- Phylogeny-Aware Metabologenomics Accurately Assigns Natural Products to Biosynthetic Gene Clusters.Microbial biotechnology · 2026Article
- Benchmarking methods for measuring biosynthetic gene cluster similarity and determination of gene cluster families.Bioinformatics (Oxford, England) · 2025Article
- The Role of Nucleotide Sequencing in Natural Product Drug Discovery.Journal of natural products · 2025Review
- Sequence modeling tools to decode the biosynthetic diversity of the human microbiome.mSystems · 2025Review
- Targeted genome mining with GATOR-GC maps the evolutionary landscape of biosynthetic diversity.Nucleic acids research · 2025Article
- antiSMASH 8.0: extended gene cluster detection capabilities and analyses of chemistry, enzymology, and regulation.Nucleic acids research · 2025Article
- Context matters: assessing the impacts of genomic background and ecology on microbial biosynthetic gene cluster evolution.mSystems · 2025Review
- Targeted genome mining with GATOR-GC maps the evolutionary landscape of biosynthetic diversity.bioRxiv : the preprint server for biology · 2025Article
- getphylo: rapid and automatic generation of multi-locus phylogenetic trees.BMC bioinformatics · 2025Article
- Pangenome mining of the Streptomyces genus redefines species' biosynthetic potential.Genome biology · 2025Article
- PanBGC: a pangenome-inspired framework for comparative analysis of biosynthetic gene clusters.ISME communications · 2025Article
- Expanding the genome information on Bacillales for biosynthetic gene cluster discovery.Scientific data · 2024Article
- Pan-genome-scale metabolic modeling ofmSystems · 2024Article
- HiFiBGC: an ensemble approach for improved biosynthetic gene cluster detection in PacBio HiFi-read metagenomes.BMC genomics · 2024Article
- A treasure trove of 1034 actinomycete genomes.Nucleic acids research · 2024Article
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5 authors.
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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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