Artificial intelligence in microbial metagenomics

Rapid advancements in genomic sequencing technologies and similar technological advancements in the area of accessing, isolating, extracting and functional probing of microbes residing in diverse environments has resulted in a deluge of microbiome sequencing and microbial genomic sequencing data. Concomitant developments in the area of data science, specifically in the domains of advanced statistics, and artificial intelligence (AI) can facilitate mining this data to answer complex biological questions and developing translational applications in diverse areas, ranging from health-care to industrial microbiology. For most researchers, information on which AI tools address specific biological questions is scattered across disparate sources. In this chapter, we explore the various applications of AI-based methodologies (using case-studies) in answering different biological questions using microbial genomics and metagenomic data. We also discuss different AI and machine-learning (ML) based approaches to integrate metagenomic data with other "omics" data. Finally, we highlight both challenges and possibilities with this rapidly progressing field.