Discovery of thermophilic Bacillales using reduced-representation genotyping for identification

Metagenomics and artificial intelligence in the context of human health

Human microbiome is ubiquitous, dynamic, and site-specific consortia of microbial communities. The pathogenic nature of microorganisms within human tissues has led to an increase in microbial studies. Characterization of genera, like Streptococcus, Cutibacterium, Staphylococcus, Bifidobacterium, Lactococcus and Lactobacillus through culture-dependent and culture-independent techniques has been reported. However, due to the unique environment within human tissues, it is difficult to culture these microorganisms making their molecular studies strenuous. MGs offer a gateway to explore and characterize hidden microbial communities through a culture-independent mode by direct DNA isolation. By function and sequence-based MGs, Scientists can explore the mechanistic details of numerous microbes and their interaction with the niche. Since the data generated from MGs studies is highly complex and multi-dimensional, it requires accurate analytical tools to evaluate and interpret the data. Artificial intelligence (AI) provides the luxury to automatically learn the data dimensionality and ease its complexity that makes the disease diagnosis and disease response easy, accurate and timely. This review provides insight into the human microbiota and its exploration and expansion through MG studies. The review elucidates the significance of MGs in studying the changing microbiota during disease conditions besides highlighting the role of AI in computational analysis of MG data.