RNA-seq: Glossary

RNA-seq is a commonly used technology for profiling the transcriptome.

There are a number of different applications for RNA-seq data - we’ll be looking at detecetign differentially expressed genes using data from an experiment involving RNA-seq data from yeast.

Quality assessmet is a key initial step in the analysis of RNA-seq data

The FastQC and MultiQC applications are useful tools for quality assessent of RNA-seq data.

Adapter removal and trimming (optional) are important steps in processign RNA-seq data.

Alignment and feature counting are used to generate read counts for each genomic feature (e.g., genes) of interest, per sample.

The count data can then be used for stataistical analysis (e.g., to identify differentially expressed genes).

Statistical analysis is required to identify genes exhibiting altered expression between experimental conditions.

The limma processing pipeline is a fairly standard (and robust) way to do this.

DESeq2 and edgeR offer alternative methods for identifying differetially expressed genes.

Coordinated changes in groups of functionally related genes can tell us about the underlying biological mechanisms that changing between exprimental conditions.

The characteristics of RNA-seq experiments mean that gene-length correction is required, to avoid standard approaches to over-representation analysis givign erroneous results.