Gene Expression

The vast majority of gene expression car ried out nowadays is RNAseq. It involves the quantification of transcript abundance, detection of differentially expressed genes across conditions, and exploration of underlying biological pathways and functions.

Once the data has been preprocessed using command line tools, one typically will use downstream analysis tools, mainly from Bioconductor, for subsequent analysis which can usually be run on a local machine.

We highly recommend that you use the Bioconductor DESeq2 package to analyse RNAseq data. It has become recognised as a standard, and the DESeq2 manual is not only one of the best guides available but it's constantly updated.

TO DO

The "DESeq2 manual" by Michael Love explains the use of the package and demonstrates typical workflows. Please note that in the manual, they also mention that the "RNA-seq workflow" on the Bioconductor website covers similar material but at a slower pace, including the generation of count matrices from FASTQ files. So you might want to check that workflow too.

TAKE the course

After this section you should be able to

Understand the theory behind DESeq2
Use DESeq2 to perform differential gene expression analysis