Microbial Dark Matter: from Discovery to Applications

With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment.

Automated summary

With the rapid growth of microbiome samples and sequencing data, there has been an increase in knowledge about microbial communities. However, there is still much to learn, including novel species and genes, as well as dynamic patterns within microbial communities, which make up the microbial dark matter. This study summarizes the role of microbial dark matter in microbiome research, reviews current data mining methods, particularly artificial intelligence (AI) methods, and provides case studies on using AI for microbiome data mining and knowledge discovery.