NanoCore: core-genome-based bacterial genomic surveillance and outbreak detection in healthcare facilities from Nanopore and Illumina data.

IF 5 2区 生物学 Q1 MICROBIOLOGY
mSystems Pub Date : 2024-11-19 Epub Date: 2024-10-07 DOI:10.1128/msystems.01080-24
Sebastian A Fuchs, Lisanna Hülse, Teresa Tamayo, Susanne Kolbe-Busch, Klaus Pfeffer, Alexander T Dilthey
{"title":"NanoCore: core-genome-based bacterial genomic surveillance and outbreak detection in healthcare facilities from Nanopore and Illumina data.","authors":"Sebastian A Fuchs, Lisanna Hülse, Teresa Tamayo, Susanne Kolbe-Busch, Klaus Pfeffer, Alexander T Dilthey","doi":"10.1128/msystems.01080-24","DOIUrl":null,"url":null,"abstract":"<p><p>Genomic surveillance enables the early detection of pathogen transmission in healthcare facilities and contributes to the reduction of substantial patient harm. Fast turnaround times, flexible multiplexing, and low capital requirements make Nanopore sequencing well suited for genomic surveillance purposes; the analysis of Nanopore data, however, can be challenging. We present NanoCore, a user-friendly method for Nanopore-based genomic surveillance in healthcare facilities, enabling the calculation and visualization of cgMLST-like (core-genome multilocus sequence typing) sample distances directly from unassembled Nanopore reads. NanoCore implements a mapping, variant calling, and multilevel filtering strategy and also supports the analysis of Illumina data. We validated NanoCore on two 24-isolate data sets of methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) and vancomycin-resistant <i>Enterococcus faecium</i> (VRE). In the Nanopore-only mode, NanoCore-based pairwise distances between closely related isolates were near-identical to Illumina-based SeqSphere<sup>+</sup> distances, a gold standard commercial method (average differences of 0.75 and 0.81 alleles for MRSA and VRE; sd = 0.98 and 1.00), and gave an identical clustering into closely related and non-closely related isolates. In the \"hybrid\" mode, in which only Nanopore data are used for some isolates and only Illumina data for others, increased average pairwise isolate distance differences were observed (average differences of 3.44 and 1.95 for MRSA and VRE, respectively; sd = 2.76 and 1.34), while clustering results remained identical. NanoCore is computationally efficient (<15 hours of wall time for the analysis of a 24-isolate data set on a workstation), available as free software, and supports installation via conda. In conclusion, NanoCore enables the effective use of the Nanopore technology for bacterial pathogen surveillance in healthcare facilities.</p><p><strong>Importance: </strong>Genomic surveillance involves sequencing the genomes and measuring the relatedness of bacteria from different patients or locations in the same healthcare facility, enabling an improved understanding of pathogen transmission pathways and the detection of \"silent\" outbreaks that would otherwise go undetected. It has become an indispensable tool for the detection and prevention of healthcare-associated infections and is routinely applied by many healthcare institutions. The earlier an outbreak or transmission chain is detected, the better; in this context, the Oxford Nanopore sequencing technology has important potential advantages over traditionally used short-read sequencing technologies, because it supports \"real-time\" data generation and the cost-effective \"on demand\" sequencing of small numbers of bacterial isolates. The analysis of Nanopore sequencing data, however, can be challenging. We present NanoCore, a user-friendly software for genomic surveillance that works directly based on Nanopore sequencing reads in FASTQ format, and demonstrate that its accuracy is equivalent to traditional gold standard short read-based analyses.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0108024"},"PeriodicalIF":5.0000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575142/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"mSystems","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1128/msystems.01080-24","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/7 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Genomic surveillance enables the early detection of pathogen transmission in healthcare facilities and contributes to the reduction of substantial patient harm. Fast turnaround times, flexible multiplexing, and low capital requirements make Nanopore sequencing well suited for genomic surveillance purposes; the analysis of Nanopore data, however, can be challenging. We present NanoCore, a user-friendly method for Nanopore-based genomic surveillance in healthcare facilities, enabling the calculation and visualization of cgMLST-like (core-genome multilocus sequence typing) sample distances directly from unassembled Nanopore reads. NanoCore implements a mapping, variant calling, and multilevel filtering strategy and also supports the analysis of Illumina data. We validated NanoCore on two 24-isolate data sets of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE). In the Nanopore-only mode, NanoCore-based pairwise distances between closely related isolates were near-identical to Illumina-based SeqSphere+ distances, a gold standard commercial method (average differences of 0.75 and 0.81 alleles for MRSA and VRE; sd = 0.98 and 1.00), and gave an identical clustering into closely related and non-closely related isolates. In the "hybrid" mode, in which only Nanopore data are used for some isolates and only Illumina data for others, increased average pairwise isolate distance differences were observed (average differences of 3.44 and 1.95 for MRSA and VRE, respectively; sd = 2.76 and 1.34), while clustering results remained identical. NanoCore is computationally efficient (<15 hours of wall time for the analysis of a 24-isolate data set on a workstation), available as free software, and supports installation via conda. In conclusion, NanoCore enables the effective use of the Nanopore technology for bacterial pathogen surveillance in healthcare facilities.

Importance: Genomic surveillance involves sequencing the genomes and measuring the relatedness of bacteria from different patients or locations in the same healthcare facility, enabling an improved understanding of pathogen transmission pathways and the detection of "silent" outbreaks that would otherwise go undetected. It has become an indispensable tool for the detection and prevention of healthcare-associated infections and is routinely applied by many healthcare institutions. The earlier an outbreak or transmission chain is detected, the better; in this context, the Oxford Nanopore sequencing technology has important potential advantages over traditionally used short-read sequencing technologies, because it supports "real-time" data generation and the cost-effective "on demand" sequencing of small numbers of bacterial isolates. The analysis of Nanopore sequencing data, however, can be challenging. We present NanoCore, a user-friendly software for genomic surveillance that works directly based on Nanopore sequencing reads in FASTQ format, and demonstrate that its accuracy is equivalent to traditional gold standard short read-based analyses.

NanoCore:利用 Nanopore 和 Illumina 数据,基于核心基因组的医疗机构细菌基因组监测和疫情检测。
基因组监测能及早发现医疗机构中的病原体传播,有助于减少对病人的重大伤害。快速的周转时间、灵活的多路复用和较低的资金要求使纳米孔测序技术非常适合用于基因组监控;然而,对纳米孔数据的分析可能具有挑战性。我们介绍的 NanoCore 是一种用户友好型方法,适用于医疗机构基于 Nanopore 的基因组监控,可直接从未合成的 Nanopore 读数中计算和显示类似 cgMLST(核心基因组多焦点序列分型)的样本距离。NanoCore 实现了映射、变异调用和多级过滤策略,还支持对 Illumina 数据的分析。我们在耐甲氧西林金黄色葡萄球菌(MRSA)和耐万古霉素粪肠球菌(VRE)的两个 24 个分离数据集上验证了 NanoCore。在纯 Nanopore 模式下,基于 NanoCore 的近亲分离物之间的配对距离与基于 Illumina 的 SeqSphere+ 距离(一种黄金标准的商业方法)几乎相同(MRSA 和 VRE 的等位基因平均差异分别为 0.75 和 0.81;sd = 0.98 和 1.00),并将近亲分离物和非近亲分离物进行了相同的聚类。在 "混合 "模式下,即某些分离物仅使用 Nanopore 数据,而另一些分离物仅使用 Illumina 数据时,观察到成对分离物的平均距离差异增大(MRSA 和 VRE 的平均差异分别为 3.44 和 1.95;sd = 2.76 和 1.34),而聚类结果仍然相同。NanoCore 的计算效率很高(Importance:基因组监测包括对来自不同患者或同一医疗机构不同地点的细菌进行基因组测序和相关性测量,从而加深对病原体传播途径的了解,并发现 "无声 "爆发,否则将无法发现。它已成为检测和预防医疗相关感染不可或缺的工具,并被许多医疗机构例行应用。疫情或传播链越早发现越好;在这方面,牛津纳米孔测序技术与传统使用的短读数测序技术相比具有重要的潜在优势,因为它支持 "实时 "数据生成和对少量细菌分离物进行经济高效的 "按需 "测序。然而,对纳米孔测序数据的分析可能具有挑战性。我们介绍的 NanoCore 是一种用户友好型基因组监测软件,可直接基于 FASTQ 格式的 Nanopore 测序读数进行工作,并证明其准确性等同于传统的基于短读数的金标准分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
mSystems
mSystems Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍: mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信