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在地铁中避免疫情恐慌并战胜鸭嘴兽。

Avoiding Pandemic Fears in the Subway and Conquering the Platypus.

作者信息

Gonzalez A, Vázquez-Baeza Y, Pettengill J B, Ottesen A, McDonald D, Knight R

机构信息

Department of Pediatrics, University of California San Diego, San Diego, California, USA.

Department of Computer Science and Engineering, University of California San Diego, San Diego, California, USA.

出版信息

mSystems. 2016 Jun 28;1(3). doi: 10.1128/mSystems.00050-16. eCollection 2016 May-Jun.


DOI:10.1128/mSystems.00050-16
PMID:27832215
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5069772/
Abstract

Metagenomics is increasingly used not just to show patterns of microbial diversity but also as a culture-independent method to detect individual organisms of intense clinical, epidemiological, conservation, forensic, or regulatory interest. A widely reported metagenomic study of the New York subway suggested that the pathogens and were part of the "normal subway microbiome." In their article in mSystems, Hsu and collaborators (mSystems 1(3):e00018-16, 2016, http://dx.doi.org/10.1128/mSystems.00018-16) showed that microbial communities on transit surfaces in the Boston subway system are maintained from a metapopulation of human skin commensals and environmental generalists and that reanalysis of the New York subway data with appropriate methods did not detect the pathogens. We note that commonly used software pipelines can produce results that lack validity (e.g., reporting widespread distribution of notorious endemic species such as the platypus or the presence of pathogens) but that appropriate use of inclusion and exclusion sets can avoid this issue.

摘要

宏基因组学不仅越来越多地用于展示微生物多样性模式,还作为一种不依赖培养的方法,用于检测具有强烈临床、流行病学、保护、法医或监管意义的个体生物。一项关于纽约地铁的广泛报道的宏基因组学研究表明,某些病原体是“正常地铁微生物群”的一部分。在他们发表于《mSystems》的文章中,许及其合作者(《mSystems》1(3):e00018 - 16, 2016, http://dx.doi.org/10.1128/mSystems.00018 - 16)表明,波士顿地铁系统中运输表面的微生物群落是由人类皮肤共生菌和环境泛化菌的集合种群维持的,并且用适当方法对纽约地铁数据进行重新分析未检测到这些病原体。我们注意到,常用的软件流程可能会产生缺乏有效性的结果(例如,报告鸭嘴兽等臭名昭著的特有物种的广泛分布或病原体的存在),但适当使用包含和排除集可以避免这个问题。

相似文献

[1]
Avoiding Pandemic Fears in the Subway and Conquering the Platypus.

mSystems. 2016-6-28

[2]
Urban Transit System Microbial Communities Differ by Surface Type and Interaction with Humans and the Environment.

mSystems. 2016-6-28

[3]
Geospatial Resolution of Human and Bacterial Diversity with City-Scale Metagenomics.

Cell Syst. 2015-7-29

[4]
Co-occurrence patterns of bacteria within microbiome of Moscow subway.

Comput Struct Biotechnol J. 2020-2-1

[5]
Fingerprinting cities: differentiating subway microbiome functionality.

Biol Direct. 2019-10-30

[6]
A tandem repeats database for bacterial genomes: application to the genotyping of Yersinia pestis and Bacillus anthracis.

BMC Microbiol. 2001

[7]
The Built Environment Is a Microbial Wasteland.

mSystems. 2016-4-19

[8]
Prediction of microbial communities for urban metagenomics using neural network approach.

Hum Genomics. 2019-10-22

[9]
Microbiological Characteristics of Some Stations of Moscow Subway.

Biology (Basel). 2022-1-21

[10]
Sizing Up the Uncultured Microbial Majority.

mSystems. 2018-9-25

引用本文的文献

[1]
Microbiome diversity of low biomass skin sites is captured by metagenomics but not 16S amplicon sequencing.

bioRxiv. 2025-6-24

[2]
Faecal metagenomes of great tits and blue tits provide insights into host, diet, pathogens and microbial biodiversity.

Access Microbiol. 2025-4-28

[3]
Metagenome-validated combined amplicon sequencing and text mining-based annotations for simultaneous profiling of bacteria and fungi: vaginal microbiota and mycobiota in healthy women.

Microbiome. 2024-12-28

[4]
YACHT: an ANI-based statistical test to detect microbial presence/absence in a metagenomic sample.

Bioinformatics. 2024-2-1

[5]
A Large-Scale Study into Protist-Animal Interactions Based on Public Genomic Data Using DNA Barcodes.

Animals (Basel). 2023-7-8

[6]
An initial genomic blueprint of the healthy human oesophageal microbiome.

Access Microbiol. 2023-6-26

[7]
YACHT: an ANI-based statistical test to detect microbial presence/absence in a metagenomic sample.

bioRxiv. 2023-4-20

[8]
Drastic reduction of false positive species in samples of insects by intersecting the default output of two popular metagenomic classifiers.

PLoS One. 2022

[9]
HAYSTAC: A Bayesian framework for robust and rapid species identification in high-throughput sequencing data.

PLoS Comput Biol. 2022-9

[10]
Zebra: Static and Dynamic Genome Cover Thresholds with Overlapping References.

mSystems. 2022-10-26

本文引用的文献

[1]
Urban Transit System Microbial Communities Differ by Surface Type and Interaction with Humans and the Environment.

mSystems. 2016-6-28

[2]
Geospatial Resolution of Human and Bacterial Diversity with City-Scale Metagenomics.

Cell Syst. 2015-7-29

[3]
Co-enriching microflora associated with culture based methods to detect Salmonella from tomato phyllosphere.

PLoS One. 2013-9-9

[4]
Baseline survey of the anatomical microbial ecology of an important food plant: Solanum lycopersicum (tomato).

BMC Microbiol. 2013-5-24

[5]
SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data.

Bioinformatics. 2012-10-15

[6]
IMG: the Integrated Microbial Genomes database and comparative analysis system.

Nucleic Acids Res. 2012-1

[7]
Basic local alignment search tool.

J Mol Biol. 1990-10-5

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