Hong Eunju, Shin Youngjin, Kim Hyunseong, Cho Woo Young, Song Woo-Hyun, Jung Seung-Hyun, Lee Minho
Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea.
Basic Medical Science Facilitation Program, Catholic Medical Center, The Catholic University of Korea, Seoul 06591, Republic of Korea.
J Microbiol. 2025 Jan;63(1):e.2409020. doi: 10.71150/jm.2409020. Epub 2025 Jan 24.
With the advent of whole-genome sequencing, opportunities to investigate the population structure, transmission patterns, antimicrobial resistance profiles, and virulence determinants of Streptococcus pneumoniae at high resolution have been increasingly expanding. Consequently, a user-friendly bioinformatics tool is needed to automate the analysis of Streptococcus pneumoniae whole-genome sequencing data, summarize clinically relevant genomic features, and further guide treatment options. Here, we developed PneusPage, a web-based tool that integrates functions for species prediction, molecular typing, drug resistance determination, and data visualization of Streptococcus pneumoniae. To evaluate the performance of PneusPage, we analyzed 80 pneumococcal genomes with different serotypes from the Global Pneumococcal Sequencing Project and compared the results with those from another platform, PathogenWatch. We observed a high concordance between the two platforms in terms of serotypes (100% concordance rate), multilocus sequence typing (100% concordance rate), penicillin-binding protein typing (88.8% concordance rate), and the Global Pneumococcal Sequencing Clusters (98.8% concordance rate). In addition, PneusPage offers integrated analysis functions for the detection of virulence and mobile genetic elements that are not provided by previous platforms. By automating the analysis pipeline, PneusPage makes whole-genome sequencing data more accessible to non-specialist users, including microbiologists, epidemiologists, and clinicians, thereby enhancing the utility of whole-genome sequencing in both research and clinical settings. PneusPage is available at https://pneuspage.minholee.net/.
随着全基因组测序的出现，以高分辨率研究肺炎链球菌的种群结构、传播模式、抗菌药物耐药性谱和毒力决定因素的机会日益增加。因此，需要一种用户友好的生物信息学工具来自动化分析肺炎链球菌全基因组测序数据，总结临床相关的基因组特征，并进一步指导治疗方案。在此，我们开发了PneusPage，这是一种基于网络的工具，集成了肺炎链球菌的物种预测、分子分型、耐药性测定和数据可视化功能。为了评估PneusPage的性能，我们分析了来自全球肺炎链球菌测序项目的80个不同血清型的肺炎链球菌基因组，并将结果与另一个平台PathogenWatch的结果进行比较。我们观察到两个平台在血清型（一致率100%）、多位点序列分型（一致率100%）、青霉素结合蛋白分型（一致率88.8%）和全球肺炎链球菌测序簇（一致率98.8%）方面具有高度一致性。此外，PneusPage提供了用于检测毒力和移动遗传元件的综合分析功能，而以前的平台并未提供这些功能。通过自动化分析流程，PneusPage使非专业用户（包括微生物学家、流行病学家和临床医生）更容易获取全基因组测序数据，从而提高了全基因组测序在研究和临床环境中的实用性。可通过https://pneuspage.minholee.net/访问PneusPage。
