Transcriptome comprises the total RNA present in an organism and its study is termed as transcriptomics. Transcriptomics is now a widely accepted discipline due to advances in technology since the late 1990s. The field of transcriptomics has been revolutionized by a series of technological advancements like microarrays, which measure a set of preset sequences, and RNA sequencing (RNA-Seq), which employs high-throughput sequencing to capture every sequence. RNA-Seq is the new age technology that combines a high-throughput sequencing methodology with computational techniques. The analysis of RNA-Seq data comprises several steps like quality check, trimming, alignment, assembly, expression quantification, and differential gene expression analysis. The transcriptome data can be used for the identification of differentially expressed genes that can be used as biomarkers for the diagnosis of the disease or gene target for therapeutic approaches. Transcriptome data analysis further aids in the detection of alternative splicing, chimeric transcripts, single nucleotide variants, and allele-specific expression. With advancements in sequencing technologies, analysis tools, and software, and the increasing application in healthcare and medicine, transcriptome data analysis has revolutionized healthcare research and development.