ArticleNature communications2025
PGAP2: A comprehensive toolkit for prokaryotic pan-genome analysis based on fine-grained feature networks.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Complete Genome Sequence Analysis, Probiotic Properties, and Safety Assessment of Healthy Human-Derived Pediococcus pentosaceus.Probiotics and antimicrobial proteins · 2026Article
- Article
- LSGFA: domain-based infraspecific large-scale prokaryotic genomic orthologous gene inference.BMC bioinformatics · 2026Article
- Taxonomic Description and Genomic Characterization ofMicroorganisms · 2026Article
- Screening and Evaluation In Vitro ofMicroorganisms · 2026Article
- Genomic Evidence Supporting a One Health Perspective onAntibiotics (Basel, Switzerland) · 2026Article
- Comparative pangenomics of theFrontiers in microbiology · 2026Article
- Genome architecture reveals hidden strain-level diversity in the highly conserved fish pathogen Nocardia seriolae.Access microbiology · 2026Article
- Genome-wide investigation of outbreak-associatedFrontiers in microbiology · 2026Article
- Probiotic Properties In Vitro ofMicroorganisms · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Pan-genome analysis is a crucial method for studying genomic dynamics. By creating pan-genome maps for prokaryotic organisms, we can gain valuable insights into their genetic diversity and ecological adaptability. However, current analytical methods often struggle to balance accuracy and computational efficiency, and they tend to provide primarily qualitative results. This study introduces PGAP2, an integrated software package that simplifies various processes, including data quality control, pan-genome analysis, and result visualization. PGAP2 facilitates the rapid and accurate identification of orthologous and paralogous genes by employing fine-grained feature analysis within constrained regions. Our systematic evaluation with simulated and gold-standard datasets demonstrates that PGAP2 is more precise, robust, and scalable than state-of-the-art tools for large-scale pan-genome data. Furthermore, PGAP2 introduces four quantitative parameters derived from the distances between or within clusters, enabling detailed characterization of homology clusters. Finally, we validate our quantitative findings by applying PGAP2 to construct a pan-genomic profile of 2794 zoonotic Streptococcus suis strains. This analysis offers new insights into the genetic diversity of S. suis, thereby enhancing our understanding of its genomic structure. PGAP2 is freely available at https://github.com/bucongfan/PGAP2 .
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