Because changes in the structure of a microbial community can have a notable impact on its environment, it is important to delineate the global diversity of microbial community structures as a basis for predicting such changes. In the past decade, microbiomes from various natural and human symbiotic environments have been thoroughly studied. However, our knowledge is limited as to what types of environments affect the structure of a microbial community. In addition, there is no easy way to evaluate typicality and heterogeneity of newly obtained microbiome samples by comparing them with a large number of past samples.
We applied several different machine-learning techniques to a dataset containing >30,000 sequenced 16S rRNA gene amplicons from a public database MicrobeDB.jp, and created the tool to explore the microbiome universe and to place your metagenomic data on the universe like a Global Positioning System.
Our system enables researchers to do the following: 1) clarify the relationship between environments and patterns of microbial community structures. 2) predict the "latent environments" of new samples from, for example, the ocean, a diseased gut, or another unexpected environment, and quickly compare new samples with tens of thousands of existing samples based on their environmental similarity, which makes it easy to detect dysbiosis of the microbiome in the human gut or contaminants in natural environments. 3) search for samples in the >30,000-sample dataset based on an environmental perspective, without depending on exact word matching of queries and sample descriptions.
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Contact: khigashi_at_nig.ac.jp (please replace _at_ with @)
Kurokawa Lab, National Institute of Genetics.