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<p>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 <i>Streptococcus suis</i> strains. This analysis offers new insights into the genetic diversity of <i>S. suis</i>, thereby enhancing our understanding of its genomic structure. PGAP2 is freely available at <a href="https://github.com/bucongfan/PGAP2">https://github.com/bucongfan/PGAP2</a>.</p>

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PGAP2: A comprehensive toolkit for prokaryotic pan-genome analysis based on fine-grained feature networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月10日

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  • Congfan Bu,
  • Hao Zhang,
  • Fengnian Zhang,
  • Wenhao Liang,
  • Hao Gao,
  • Jing Zhao,
  • Fangming Lv,
  • Ruikun Xue,
  • Qian Liu,
  • Zhewen Zhang,
  • Zhong Jin,
  • Jingfa Xiao

摘要

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.