Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single‐cell RNA sequencing analyses

Abstract
The transcriptome, the complete set of RNA molecules within a cell, plays a critical role in regulating physiological processes. The advent of RNA sequencing (RNA‐seq) facilitated by Next Generation Sequencing (NGS) technologies, has revolutionized transcriptome research, providing unique insights into gene expression dynamics. This powerful strategy can be applied at both bulk tissue and single‐cell levels. Bulk RNA‐seq provides a gene expression profile within a tissue sample. Conversely, single‐cell RNA sequencing (scRNA‐seq) offers resolution at the cellular level, allowing the uncovering of cellular heterogeneity, identification of rare cell types, and distinction between distinct cell populations. As computational tools, machine learning techniques, and NGS sequencing platforms continue to evolve, the field of transcriptome research is poised for significant advancements. Therefore, to fully harness this potential, a comprehensive understanding of bulk RNA‐seq and scRNA‐seq technologies, including their advantages, limitations, and computational considerations, is crucial. This review provides a systematic comparison of the computational processes involved in both RNA‐seq and scRNA‐seq, highlighting their fundamental principles, applications, strengths, and limitations, while outlining future directions in transcriptome research.