Glossary

fastqc is the first step in any sequencing data analysis when working with fastq files

Mapping RNA-seq data requires using splicing-aware mappers.

The first step of mapping sequencing data is to build a genome index.

This involves figuring out which reference file and annotation you need, and making sure the chromosome names in them match

STAR is used to map the reads on Artemis

We have loaded our count table into R, and set up an RStudio project

We have identified that the data was generated in a strand-specific manner

We must filter out lowly expressed genes prior to DGEA

Prior to any DGEA, we must take advantage of unsupervised learning techniques to ensure that no batch effects or other confounding issues affect our experiment as a whole

PCA and heirarchical clustering can be used to achieve this

If there are issues with the PCA and/or clustering, while a list of genes will still be reported in the differential expression, it will most NOT be reliable or accurate, as these issues need to be taken into account

There are tools in R to take into account both understood (different time points) and inexplicable batch effects

In this section, we have carried out differential gene expression analysis

We can use both built-in visualisations with limma, as well as external R packages

Exploratory pathway analysis can be performed using a wide range of online tools

ToppGene allows us to quickly assess what’s going on in our data

If a formal pathway analysis needs to be carried out, tools like goana and camera nicely fit within the limma ecosystem