Designing RNA sequencing experiments: A practical guide to reproducible gene expression analysis

RNA sequencing (RNA-seq) has become a cornerstone of modern biotechnology, offering a comprehensive and high-resolution view of gene expression that enables the discovery of novel transcripts across diverse biological systems. Its applications extend beyond basic transcriptomics, providing powerful tools for uncovering molecular mechanisms underlying disease, environmental responses, and chemical toxicity. In biotechnology and biomedical research, RNA-seq facilitates the identification of regulatory networks and biomarkers that inform therapeutic development, risk assessment, and precision medicine. However, reproducibility and data interpretation challenges persist, often stemming from suboptimal experimental design or inconsistent analytical pipelines. This review critically examines key methodological aspects required for robust and biologically meaningful RNA-seq studies including experimental design, sample preparation, sequencing strategies, and data quality control focuses specifically on eukaryotic cell RNA-seq workflows. We also compare leading sequencing platforms and discuss emerging trends that enhance the scalability and reproducibility of transcriptomic analyses. By integrating best practices with recent technological advances, this review provides a practical framework for designing high-quality RNA-seq experiments that support innovation in biotechnology, systems biology, and translational research.