The functional analysis of genes has become an indispensable area, especially for the genes involved in human health and diseases. Consequently, several high-throughput techniques have been developed for functional genome analysis to determine the structure, function, and interactions of different genes. Recent advancements have been made to innovate and improve methods for the integration of bioinformatic tools and databases in the field of functional genomics. This chapter discusses the use of bioinformatic tools in some of the common approaches used in functional gene analysis, including transcriptomics, proteomics, and epigenomics, as well as the use of bioinformatic tools to understand the mechanisms underlying human diseases like neuroimmunological diseases and cancer. The significant aim of the computational methods or tools used in the functional analysis of genes or genomes is to identify biomarkers at the transcriptional or translational level. The computational tools typically involve data mining and data analysis techniques to screen differentially expressed genes (DEGs) in diseased and normal cell types and help in finding out potent biomarkers that aid in disease diagnosis as well as the development of vaccine or drug targets against diseases. Hence, a combination of wet lab high-throughput experimentation and bioinformatic analysis of genome-wide data augments the existing efforts in the area of functional genomics toward disease diagnosis as well as the development of therapeutic measures.