Joint Research Unit 'Infection and Public Health', FISABIO-University of Valencia, Institute for Integrative Systems Biology (I2SysBio), Valencia, Spain.
CIBERESP, ISCIII, Spain.
Microb Genom. 2024 Jan;10(1). doi: 10.1099/mgen.0.001189.
Extensive gonococcal surveillance has been performed using molecular typing at global, regional, national and local levels. The three main genotyping schemes for this pathogen, multi-locus sequence typing (MLST), multi-antigen sequence typing (NG-MAST) and sequence typing for antimicrobial resistance (NG-STAR), allow inter-laboratory and inter-study comparability and reproducibility and provide an approximation to the gonococcal population structure. With whole-genome sequencing (WGS), we obtain a substantially higher and more accurate discrimination between strains compared to previous molecular typing schemes. However, WGS remains unavailable or not affordable in many laboratories, and thus bioinformatic tools that allow the integration of data among laboratories with and without access to WGS are imperative for a joint effort to increase our understanding of global pathogen threats. Here, we present pyngoST, a command-line Python tool for fast, simultaneous and accurate sequence typing of from WGS assemblies. pyngoST integrates MLST, NG-MAST and NG-STAR, and can also designate NG-STAR clonal complexes, NG-MAST genogroups and mosaicism, facilitating multiple sequence typing from large WGS assembly collections. Exact and closest matches for existing alleles and sequence types are reported. The implementation of a fast multi-pattern searching algorithm allows pyngoST to be rapid and report results on 500 WGS assemblies in under 1 min. The mapping of typing results on a core genome tree of 2375 gonococcal genomes revealed that NG-STAR is the scheme that best represents the population structure of this pathogen, emphasizing the role of antimicrobial use and antimicrobial resistance as a driver of gonococcal evolution. This article contains data hosted by Microreact.
已经在全球、区域、国家和地方各级使用分子分型进行了广泛的淋球菌监测。该病原体的三种主要基因分型方案，多位点序列分型（MLST）、多抗原序列分型（NG-MAST）和抗菌药物耐药性序列分型（NG-STAR），允许实验室间和研究间的可比性和可重复性，并提供淋球菌种群结构的近似值。通过全基因组测序（WGS），我们获得了与以前的分子分型方案相比，菌株之间更高和更准确的区分。然而，在许多实验室中，WGS 仍然不可用或负担不起，因此允许在有和没有 WGS 访问权限的实验室之间整合数据的生物信息学工具对于共同努力增加我们对全球病原体威胁的理解至关重要。在这里，我们介绍了 pyngoST，这是一种用于从 WGS 组装体中快速、同时和准确进行分型的命令行 Python 工具。pyngoST 集成了 MLST、NG-MAST 和 NG-STAR，还可以指定 NG-STAR 克隆复合体、NG-MAST 基因群和马赛克，从而方便从大型 WGS 组装集中进行多种序列分型。报告了现有等位基因和序列类型的精确和最接近匹配。快速多模式搜索算法的实现允许 pyngoST 快速运行，并在不到 1 分钟的时间内报告 500 个 WGS 组装的结果。将分型结果映射到 2375 个淋球菌基因组的核心基因组树上，表明 NG-STAR 是最能代表该病原体种群结构的方案，强调了抗菌药物使用和抗菌药物耐药性作为淋球菌进化驱动因素的作用。本文包含 Microreact 托管的数据。
