The complete set of transcribed RNAs in a cell, the transcriptome, provides a snapshot of the cell's activity. Quantitative analysis of the transcriptome during development, homeostasis, and disequilibrium is crucial for understanding cellular mechanisms. High-throughput technologies like RNA sequencing (RNA-seq) allow comprehensive transcription profiling across various physiological states, including disease, infection, environmental changes, and controlled conditions. Advancements in RNA-seq have generated extensive data for multiple organisms, stored in public databases. This data supports new research, including differential expression analysis, functional module prediction, and novel gene discovery. Comparative analysis of gene expression within or across species is also possible. However, many experimental biologists and novice researchers lack the computational expertise for such analyses. While several RNA-seq analysis protocols exist, they often focus on small-scale, in-house data rather than high-throughput public data. This chapter presents a comprehensive pipeline for acquiring, preprocessing, assembling, and quantifying public RNA-seq data in viral-infected and uninfected states, using yeast RNA-seq data as an example. Adaptable to other organisms, this resource helps researchers uncover molecular mechanisms underlying gene expression changes, identifying key genes and regulatory factors.

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Journal Article

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Muley VY

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