BACKGROUND Research has found that human associated microbial communities play a role in homeostasis and the disruption of these communities may be important in an array of medical conditions. However outside of the human body many of these communities remain poorly studied. The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium is characterizing the microbiomes of urban environments with the aim to improve design of mass transit systems. As part of the CAMDA 2018 MetaSUB Forensics Challenge 311 city microbiome samples were provided to create urban microbial fingerprints, as well as a further 3 mystery datasets for validation. RESULTS MetaSUB samples were clustered using t-SNE in an unsupervised fashion to almost discrete groups, which upon inspection represented city of origin. Based on this clustering, geographically close metropolitan areas appear to display similar microbial profiles such as those of Auckland and Hamilton. Mystery unlabeled samples were provided part of the challenge. A random forest classifier built on the initial dataset of 311 samples was capable of correctly classifying 83.3% of the mystery samples to their city of origin. Random Forest analyses also identified features with the highest discriminatory power, ranking bacterial species such as Campylobacter jejuni and Staphylococcus argenteus as highly predictive of city of origin. The surface from which the sample was collected displayed little detectable impact on the microbial profiles in the data generated here. The proportion of reads classified per sample varied greatly and so de-novo assembly was applied to recover genomic fragments representing organisms not captured in reference databases. CONCLUSIONS Current methods can differentiate urban microbiome profiles from each other with relative ease. De-novo assembly indicated that the MetaSUB metagenomic data contains adequate depth to recover metagenomic assembled genomes and that current databases are not sufficient to fully characterize urban microbiomes. Profiles found here indicate there may be a relationship between geographical distance between areas and the urban microbiome composition although this will need further research. The impact of these different profiles on public health is currently unknown but the MetaSUB consortium is uniquely suited to evaluate these and provide a roadmap for the inclusion of urban microbiome information for city planning and public health policy. REVIEWERS This article was reviewed by Dimitar Vassilev, Eran Elhaik and Chengsheng Zhu.

中文翻译：

---

机器学习技术在创建城市微生物指纹中的应用。

背景技术研究发现，与人类相关的微生物群落在体内稳态中起作用，这些群落的破坏在一系列医学状况中可能很重要。但是，在人体之外，这些社区中的许多社区仍然研究不足。地铁和城市生物群落（MetaSUB）国际联盟的元基因组学和元设计旨在表征城市环境的微生物群落，以改善公共交通系统的设计。作为CAMDA 2018 MetaSUB取证挑战赛的一部分，提供了311个城市微生物组样本以创建城市微生物指纹图谱，以及另外3个用于验证的神秘数据集。结果使用t-SNE以无监督的方式将MetaSUB样本聚类为几乎离散的组，这些组经检查代表原产城市。基于此群集，地理上相近的大都市地区似乎显示出相似的微生物特征，例如奥克兰和汉密尔顿。未标记的神秘样品是挑战的一部分。建立在311个样本的初始数据集上的随机森林分类器能够将83.3％的神秘样本正确分类到其来源城市。随机森林分析还确定了具有最高辨别力的特征，将空肠弯曲杆菌和阿根廷葡萄球菌等细菌物种列为可高度预测起源城市的细菌。在此处生成的数据中，从中收集样品的表面对微生物的分布几乎没有可检测到的影响。每个样品分类的读物的比例差异很大，因此应用了从头组装方法来回收代表未在参考数据库中捕获的生物的基因组片段。结论当前的方法可以相对容易地区分城市微生物组概况。De-novo大会表明MetaSUB宏基因组学数据包含足够的深度来恢复宏基因组学组装的基因组，并且当前的数据库不足以全面表征城市微生物群。在此找到的资料表明，区域之间的地理距离与城市微生物组组成之间可能存在某种关系，尽管这需要进一步研究。目前尚不清楚这些不同概况对公共卫生的影响，但MetaSUB财团非常适合评估这些情况，并为将城市微生物组信息纳入城市规划和公共卫生政策提供了路线图。审阅者本文由Dimitar Vassilev，Eran Elhaik和Zhu Chengsheng审阅。