Abstract
The quantity of physiological data has grown exponentially, yielding insights into mechanisms of phenotypic and disease pathways. Among the powerful tools for physiological omics is the study of RNA, where broad sequencing of RNA leads to hypothesis generation and testing while providing observational discovery. Emphasis has been placed on RNA molecules that code for proteins, even though they represent a minority of total RNA. Diverse sequencing methods have rapidly expanded the identification of non-protein-coding molecules, including nonsense-mediated decay and long non-coding RNAs (lncRNA), which now represent the most diverse class of RNA. Increasing attention needs to be paid to the data processing of RNA sequencing to interpret transcript-level mapping data in the context of protein biology, as many protein-coding genes have diverse noncoding transcripts. Over the past several years, single-cell and spatial transcriptomics have yielded unprecedented insights into cellular, tissue, and organ physiology. Building on these advancements, bulk RNA sequencing tools have begun producing robust deconvolution methods that enhance the analysis of human genes, the detection of foreign RNA from bacteria and viruses, and provide deep insights into complex immunological events, such as B- and T-cell recombination. Over a million RNA-sequencing datasets have been generated, providing resources for data scientists to reprocess data and expand larger databases. From model organisms to complex human diseases, RNA sequencing resources continue to transform our knowledge of the complexity of personalized disease insights. Observational science is at the core of physiology, and growth of RNA sequencing represents a significant tool for physiologists.