BACKGROUND Microbial communities can be location specific, and the abundance of species within locations can influence our ability to determine whether a sample belongs to one city or another. As part of the 2017 CAMDA MetaSUB Inter-City Challenge, next generation sequencing (NGS) data was generated from swipe samples collected from subway stations in Boston, New York City hereafter New York, and Sacramento. DNA was extracted and Illumina sequenced. Sequencing data was provided for all cities as part of 2017 CAMDA contest challenge dataset. RESULTS Principal component analysis (PCA) showed clear clustering of the samples for the three cities, with a substantial proportion of the variance explained by the first three components. We ran two different classifiers and results were robust for error rate (< 6%) and accuracy (> 95%). The analysis of variance (ANOVA) demonstrated that overall, bacterial composition across the three cities is significantly different. A similar conclusion was reached using a novel bootstrap based test using diversity indices. Last but not least, a co-abundance association network analyses for the taxonomic levels "order", "family", and "genus" found different patterns of bacterial networks for the three cities. CONCLUSIONS Bacterial fingerprint can be useful to predict sample provenance. In this work prediction of provenance reported with over 95% accuracy. Association based network analysis, emphasized similarities between the closest cities sharing common bacterial composition. ANOVA showed different patterns of bacterial amongst cities, and these findings strongly suggest that bacterial signature across multiple cities are different. This work advocates a data analysis pipeline which could be followed in order to get biological insight from this data. However, the biological conclusions from this analysis is just an early indication out of a pilot microbiome data provided to us through CAMDA 2017 challenge and will be subject to change as we get more complete data sets in the near future. This microbiome data can have potential applications in forensics, ecology, and other sciences. REVIEWERS This article was reviewed by Klas Udekwu, Alexandra Graf, and Rafal Mostowy.

中文翻译：

---

从微生物组16S基因谱中揭示城市地铁的细菌指纹。

背景技术微生物群落可以是位置特定的，并且位置内物种的丰富性可以影响我们确定样品是否属于一个城市或另一个城市的能力。作为2017年CAMDA MetaSUB城市间挑战赛的一部分，下一代测序（NGS）数据是从从波士顿，纽约市（其后纽约和萨克拉曼多）的地铁站收集的刷卡样本生成的。提取DNA并进行Illumina测序。作为2017 CAMDA竞赛挑战数据集的一部分，提供了所有城市的测序数据。结果主成分分析（PCA）显示了三个城市样本的清晰聚类，前三个成分解释了很大一部分方差。我们运行了两个不同的分类器，结果对于错误率（<6％）和准确性（> 95％）都是可靠的。方差分析（ANOVA）表明，三个城市的总体细菌组成明显不同。使用基于多样性指数的新型基于引导程序的测试得出了类似的结论。最后但并非最不重要的一点是，针对“等级”，“家庭”和“属”的分类水平进行的共度丰富度关联网络分析发现，这三个城市的细菌网络模式不同。结论细菌指纹可用于预测样品来源。在这项工作中，报告的出处预测准确率超过95％。基于协会的网络分析强调了共享共同细菌组成的最近城市之间的相似性。方差分析显示城市间细菌的不同模式，这些发现强烈表明，多个城市之间的细菌签名是不同的。这项工作提倡可以遵循的数据分析管道，以便从这些数据中获得生物学见解。但是，此分析的生物学结论只是通过CAMDA 2017挑战提供给我们的试验微生物组数据的早期指示，随着我们在不久的将来获得更完整的数据集，该生物学结论可能会发生变化。这种微生物组数据可能在法医，生态学和其他科学领域具有潜在的应用。审阅者本文由Klas Udekwu，Alexandra Graf和Rafal Mostowy审阅。该分析的生物学结论只是通过CAMDA 2017挑战提供给我们的试验微生物组数据的早期指示，随着我们在不久的将来获得更完整的数据集，该生物学结论可能会发生变化。这种微生物组数据可能在法医，生态学和其他科学领域具有潜在的应用。审阅者本文由Klas Udekwu，Alexandra Graf和Rafal Mostowy审阅。该分析的生物学结论只是通过CAMDA 2017挑战提供给我们的试验微生物组数据的早期指示，随着我们在不久的将来获得更完整的数据集，该生物学结论可能会发生变化。这种微生物组数据可能在法医，生态学和其他科学领域具有潜在的应用。审阅者本文由Klas Udekwu，Alexandra Graf和Rafal Mostowy审阅。