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临床微生物学家使用开源Galaxy平台轻松分析细菌全基因组测序数据:血流感染中产超广谱β-内酰胺酶肠杆菌科细菌的特征分析

Easy analysis of bacterial whole-genome sequencing data for clinical microbiologists using open-source Galaxy platform: Characterization of ESBL-producing Enterobacterales from bloodstream infections.

作者信息

Berwa Aimé, Caspar Yvan

机构信息

Laboratoire de Bactériologie-Hygiène Hospitalière, CHU Grenoble Alpes, Grenoble, France; Univ. Grenoble Alpes, CNRS, CHU Grenoble Alpes, CEA, IBS, Grenoble, France.

Laboratoire de Bactériologie-Hygiène Hospitalière, CHU Grenoble Alpes, Grenoble, France; Univ. Grenoble Alpes, CNRS, CHU Grenoble Alpes, CEA, IBS, Grenoble, France.

出版信息

J Glob Antimicrob Resist. 2024 Dec;39:153-158. doi: 10.1016/j.jgar.2024.08.012. Epub 2024 Sep 13.

DOI:10.1016/j.jgar.2024.08.012
PMID:39278463
Abstract

OBJECTIVES

Clinical microbiologists require easy-to-use open access tools with graphical interfaces to perform bacterial whole-genome sequencing (WGS) in routine practice. This study aimed to build a bioinformatics pipeline on the open-source Galaxy platform, facilitating comprehensive and reproducible analysis of bacterial WGS data in a few steps. We then used it to characterize our local epidemiology of ESBL-producing Enterobacterales isolated from patients with bacteremia.

METHODS

We built a bioinformatics pipeline consisting of the following sequential tools: Fastp (input data trimming); FastQC (read quality control); SPAdes (genome assembly); Quast (quality control of genome assembly); Prokka (gene annotation); Staramr (ResFinder database) and ABRicate (CARD database) for antimicrobial resistance (AMR) gene screening and molecular strain typing. Paired-end short read WGS data from all ESBL-producing Enterobacterales strains isolated from patients with bacteremia over one year were analysed.

RESULTS

The Galaxy platform does not require command line tools. The bioinformatics pipeline was constructed within one hour. It only required uploading fastq files and facilitated systematization of de novo assembly of genomes, MLST typing, and AMR gene screening in one step. Among the 66 ESBL-producing strains analysed, the two most frequent ESBL genes were bla (62.1%) and bla (13.6%).

CONCLUSIONS

The open-access Galaxy platform provides a graphical interface and easy-to-use tools suitable for routine use in clinical microbiology laboratories without bioinformatics specialists. We believe that this platform will facilitate fast and low-cost analysis of bacterial WGS data, especially in resource-limited settings.

摘要

目的

临床微生物学家需要易于使用的具有图形界面的开放获取工具,以便在日常实践中进行细菌全基因组测序(WGS)。本研究旨在在开源Galaxy平台上构建一个生物信息学流程,通过几个步骤促进对细菌WGS数据的全面且可重复的分析。然后我们用它来描述从菌血症患者中分离出的产超广谱β-内酰胺酶(ESBL)肠杆菌科细菌的本地流行病学特征。

方法

我们构建了一个由以下顺序工具组成的生物信息学流程:Fastp(输入数据修剪);FastQC(读段质量控制);SPAdes(基因组组装);Quast(基因组组装质量控制);Prokka(基因注释);Staramr(ResFinder数据库)和ABRicate(CARD数据库)用于抗菌药物耐药性(AMR)基因筛选和分子菌株分型。对一年内从菌血症患者中分离出的所有产ESBL肠杆菌科细菌菌株的双端短读长WGS数据进行了分析。

结果

Galaxy平台不需要命令行工具。生物信息学流程在一小时内构建完成。它只需要上传fastq文件,并在一步中促进了基因组的从头组装、多位点序列分型(MLST)和AMR基因筛选的系统化。在分析的66株产ESBL菌株中,两个最常见的ESBL基因是bla(62.1%)和bla(13.6%)。

结论

开放获取的Galaxy平台提供了一个图形界面和易于使用的工具,适用于没有生物信息学专家的临床微生物实验室的日常使用。我们相信这个平台将促进细菌WGS数据的快速且低成本分析,特别是在资源有限的环境中。

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