Bioinformatics: RNA-Seq/Differential expression in Bash & R!

About Bioinformatics: RNA-Seq/Differential expression in Bash & R!

Ever wonder which technologies allow researchers to

discover

new markers of cancer or to get a greater

understanding

of genetic diseases? Or even just what genes are

important

for cellular growth? This is usually carried out using an application of Next Generation Sequencing Technology called RNA sequencing. RNA sequencing allows you to interpret the gene expression pattern of cells. Throughout this course, you will be equipped with the tools and knowledge to not only

understand

but

perform

RNA sequencing using

bash scripting

and

R

.

bash scripting

in

Google Colab

to understand how to run the important RNA-sequencing tools. I will then explain what a bash script that you may upload to an HPC server would look like. We will then take the data outputted from the pipeline and move into

Rstudio

where you will learn how to code with the

basics of R

! Here you will also learn how to

quality control your counts

,

perform differential expression analysis

and perform

gene ontology analysis

. As an added bonus I will also show you how to map differential expression results onto Kegg pathways!

Once you've completed this course you will know how to:

1. Download publically available data from a FTP site directly to a HPC cluster. 2. Obtain the needed raw files for genome alignment. 3. Perform genome alignment using a tool called

Salmon

. 4. Analyse the quality of your RNA-seq data using

FastQC

and

MultiQC

, while also doing a custom analysis in

R

. 5. Carry out a differential expression using

DESeq2

to find out what changes between a cell on day 4 Vs day 7 of growth. 6. Carry out gene ontology analysis to understand what pathways are up and down-regulated using

fgsea

and

clusterprofiler

. 7. Use Pathview to create annotated

KEGG

maps that can be used to look at specific pathways in more detail.

Practical Based

The course has one initial lecture explaining some of the basics of sequencing and what RNA sequencing can be used for. Then it's straight into the practical! Throughout the 19 lectures, you are guided step by step through the process from downloading the data to how you could potentially interpret the data at the final stages. Unlike most courses, the process is not simplistic. The project has real-world issues, such as dealing with code errors, using a non-model organism and how you can get around them with some initiative!

This course is made for anyone that has an interest in Next-Generation Sequencing and the technologies currently being used to make breakthroughs in genetic and medical research! The course is also meant for beginners in RNA-seq to learn the general process and complete a full walkthrough that is applicable to their own data!