Advances in transcriptomics data analysis

Many diseases result in changes in mRNA expression, prompting the development of various methods to sequence and analyze the upregulation and downregulation of mRNA. This has led to novel approaches facilitated by RNA sequencing (RNA-Seq), which has emerged as the preferred method for high-throughput studies. Advances in transcriptomics have greatly enhanced our understanding of cellular gene expression in multicellular organisms, contributing to more effective disease diagnosis. The advent of single-cell RNA sequencing (scRNA-seq) has further revolutionized the field, enabling the analysis of gene expression profiles at the single-cell level. This technique has provided valuable insights into cellular heterogeneity, the identification of new cell subtypes, and other critical aspects of gene expression dynamics. This chapter provides an in-depth exploration of current methods and tools employed at various stages of transcriptomics data analysis, highlighting recent advancements in both bulk RNA-Seq and single-cell RNA-Seq. It presents the material in a clear and comprehensive manner, incorporating detailed workflows for analyzing bulk and single-cell RNA-Seq data. Additionally, it includes commands and examples of widely-used programs in transcriptomics for a thorough understanding of the methodologies.