BACKGROUND The availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacterial abundance profiles. RESULTS Here we use a transformation of the conventional bacterial strain or gene abundance profiles to functional profiles that account for bacterial metabolism and other cell functionalities. These profiles are used as features for city classification in a machine learning algorithm that allows the extraction of the most relevant features for the classification. CONCLUSIONS We demonstrate here that the use of functional profiles not only predict accurately the most likely origin of a sample but also to provide an interesting functional point of view of the biogeography of the microbiota. Interestingly, we show how cities can be classified based on the observed profile of antibiotic resistances. REVIEWERS Open peer review: Reviewed by Jin Zhuang Dou, Jing Zhou, Torsten Semmler and Eran Elhaik.

中文翻译：

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抗生素耐药性和代谢特征作为功能生物标志物，可以准确预测城市宏基因组样本的地理起源。

背景技术数百个城市微生物组概况的可用性使得能够根据样品的微生物群组成开发出越来越准确的样品来源预测因子。典型的微生物组研究涉及细菌丰度谱的分析。结果在这里，我们使用传统的细菌菌株或基因丰度图谱转换为解释细菌代谢和其他细胞功能的功能图谱。这些配置文件用作机器学习算法中城市分类的特征，该算法允许提取与分类最相关的特征。结论我们在这里证明，功能图谱的使用不仅可以准确预测样品最可能的来源，而且还可以提供微生物群生物地理学的有趣的功能观点。有趣的是，我们展示了如何根据观察到的抗生素耐药性概况对城市进行分类。审稿人 公开同行评审：由 Jin Zhuang Dou、Jing Zhou、Torsten Semmler 和 Eran Elhaik 审阅。