Abstract
(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.
Highlights
A microbiome is a complex ecological structure that is unique to each environment
(2) Results: we have developed a random forest model that distinguishes human microbiota from domestic pet microbiota with 97% accuracy
(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
Summary
A microbiome is a complex ecological structure that is unique to each environment. Microbiota inhabiting living organism sites, such as the human gut, are of particular interest. The composition of the gut microbiome predicts some non-intestinal illnesses, such as coronary artery disease [7], liver fibrosis [8], metabolic diseases/obesity [9], insomnia [10], and bipolar depression [11]. Another classification setting is the detection of contamination in samples. This task mostly arises in the case of water contamination with sewage or animal faeces. This task arose from our need to filter out faecal samples of pets mistakenly or intentionally sent for commercial microbiome analysis in guise of human ones
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