In recent years, advancements in molecular techniques have revolutionized our understanding of gene expression, cellular diversity, and epigenetic modifications. The rapid advancement of high-throughput sequencing technologies has led to an explosion of transcriptomic data, necessitating robust computational tools for effective analysis and interpretation. The present chapter provides an overview of key bioinformatics tools and pipelines used in transcriptomics, from raw data processing to downstream functional analysis. It covers essential steps such as quality control, read alignment, transcript assembly, differential gene expression analysis, and pathway enrichment studies. Widely used tools, including FastQC for quality assessment, STAR and HISAT2 for alignment, StringTie for transcript assembly, and DESeq2 and edgeR for differential expression analysis, are discussed in detail. Additionally, emerging machine learning approaches and visualization techniques for single-cell transcriptomics and RNA modifications are explored. The integration of these tools with cloud computing and artificial intelligence is also highlighted, enabling large-scale data processing and enhancing reproducibility. Challenges such as data complexity, batch effects, and computational resource limitations are addressed, along with strategies for overcoming them. By providing a comprehensive guide to transcriptomic data analysis, this chapter serves as a valuable resource for researchers seeking to extract meaningful biological insights from large-scale RNA sequencing datasets, ultimately advancing our understanding of gene regulation and cellular function in various biological systems.