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

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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Publication Details

Journal
Nature Communications
Published
2025-11-10
DOI
https://doi.org/10.1038/s41467-025-64846-5
Citations
1
Primary Topic
Streptococcal Infections and Treatments
Type
article
Field-Weighted Citation Impact
1.88

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article

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

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1 citations
Nature Communications
Streptococcal Infections and Treatments
1.88
article

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

Jingfa Xiao, Jing Zhao, Fengnian Zhang, Congfan Bu, Hao Zhang, Hao Gao, Zhong Jin, Zhewen Zhang, Qian Liu, Ruikun Xue, Wenhao Liang, F. Lv
article en
1 citations

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 .

Nature CommunicationsVol. 16(1)
Chinese Academy of Sciences (CN), Beijing Institute of Genomics (CN), Computer Network Information Center (CN), China National Biotechnology (China) (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences
Openalex Percentile: Top 11%
Streptococcal Infections and Treatments
1.88
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