Abstract
RNA sequencing (RNA-Seq) is a powerful technique used to study gene expression and uncover the complexities of transcriptional regulation. Advances in computational methods for RNA-Seq data analysis have significantly improved the accuracy, sensitivity, and scalability of transcriptome profiling. This article reviews recent developments in computational tools and algorithms for RNA-Seq analysis, focusing on read alignment, quantification, differential expression analysis, and transcript assembly. We also discuss the challenges and future directions in RNA-Seq data analysis, including the integration of multi-omics data and the application of machine learning techniques.
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