Objectives

Explain what RNA-seq is and describe its typical workflow.
Understand why good experimental design is crucial for RNA-seq analysis.
Identify and define key RNA-seq terminology:
• Variability
• Feature
• Biological vs technical replicates
• Covariates and confounding variables
• Statistical power
Describe strategies to minimise variability and control confounding variables.
Estimate appropriate replicate numbers and sequencing depth for an experiment.
Compare sequencing design choices:
• Poly-A enrichment vs ribo-depletion
• Single-end vs paired-end reads
• Stranded vs unstranded libraries
Understand the role of multiplexing and spike-in controls in RNA-seq.

What is RNA sequencing

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RNA-seq is a method of measuring gene expression using shotgun sequencing. The process involves reverse transcribing RNA into cDNA, then sequencing fragments on a high-throughput platform such as Illumina to obtain a large number of short reads. For each sample, the reads are then aligned to a genome, and the number of reads aligned to each gene or feature is recorded.

A typical RNA-seq experiment aims to find differentially expressed genes between two conditions (e.g. up and down-regulated genes in knock-out mice compared to wild-type mice). RNA-seq can also be used to discover new transcripts, splice variants, and fusion genes.