(1) Background: microbiome host classification can be used to identify sources of contamination in environmental data. However, there is no ready-to-use host classifier. Here, we aimed to build a model that would be able to discriminate between pet and human microbiomes samples. The challenge of the study was to build a classifier using data solely from publicly available studies that normally contain sequencing data for only one type of host. (2) Results: we have developed a random forest model that distinguishes human microbiota from domestic pet microbiota (cats and dogs) with 97% accuracy. In order to prevent overfitting, samples from several (at least four) different projects were necessary. Feature importance analysis revealed that the model relied on several taxa known to be key components in domestic cat and dog microbiomes (such as Fusobacteriaceae and Peptostreptococcaeae), as well as on some taxa exclusively found in humans (as Akkermansiaceae). (3) Conclusion: we have shown that it is possible to make a reliable pet/human gut microbiome classifier on the basis of the data collected from different studies.

中文翻译：

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宠物人类肠道微生物组宿主分类器，使用来自不同研究的数据

（1）背景：微生物组宿主分类可用于识别环境数据中的污染源。但是，没有现成的主机分类器。在这里，我们旨在建立一个能够区分宠物和人类微生物组样本的模型。该研究的挑战是仅使用可公开获得的研究数据建立分类器，这些数据通常仅包含一种类型宿主的测序数据。（2）结果：我们开发了一种随机森林模型，该模型可将人的微生物群与家养的宠物微生物群（猫和狗）区分开来，准确性为97％。为了防止过度拟合，需要从几个（至少四个）不同项目中抽取样本。特征重要性分析表明，该模型依赖于几种已知的分类单元，这些分类单元是家猫和狗微生物群中的关键组成部分（例如Fusobacteriaceae和Peptostreptococcae），以及某些人类专有的分类单元（如Akkermansiaceae）。（3）结论：我们已经表明，根据不同研究收集的数据，可以建立可靠的宠物/人肠道微生物组分类器。