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

Name: Bioinformatics: RNA-Seq/Differential expression in Bash & R!
Rating: 3.8 (16 reviews)

Carry out a RNA-seq pipeline in the command line in Colab and use R to carry out differential expression & GO analysis!

Created byAdrian Bourke

Last updated 11/2022

English

What you'll learn

The basics of Next Generation Sequencing and how it can be used for Differential gene expression analysis via RNA sequencing.
How to use RNA-seq tools from the command line. Examples done in Google Colab so everyone can follow.
Preprocessing RNA sequencing data.
Aligning the reads to a genome using Salmon.
Transcript quantification.
Differential Expression in R.
Gene ontology and Pathway analysis in R.
Ultimately understand how technologies like RNA sequencing could be used to identify specific genes that can cause certain conditions.
An explanation of how you would upload your data to a server to run a big job.

Course content

3 sections • 19 lectures • 4h 31m total length

Introduction10:23
An introduction to what you will learn on the course :) The complete google collab and R scripts are below for use during the course if you don't want to follow along or get stuck!

Getting Started with Google Collab5:24
This lecture gives you a very basic introduction to google colab and installing some packages we will require.
Downloading the raw sequence reads9:16
After this lecture, you will have downloaded the raw RNA-seq files that we will be working on for the rest of the course.
Installing the tools we need for RNA-seq in Google Colab3:48
Installing the tools we will use in the Google Colab environment using conda.
Creating a transcriptome index using Salmon24:32
Creating an index for salmon which we need to align the genome.
Aligning the genome with Salmon quant.13:00
Learning how to write a loop in bash and align all the samples with the salmon quant function.
Running Fastqc and collating reports using MultiQC13:26
This lecture describes how to run multiqc on all the outputs from your tools and how to interpret basic salmon and FastQC outputs. This tool is extremely useful for looking at the broad scope of everything you have done.
Talking through the MultiQC Report8:53
This lecture will walk you through a MultiQC report which contains information from the Salmon and FastQC runs.
A example of a Bash Script and workflow managers.7:29
A quick example of a bash script file that you may upload to an HPC cluster to initiate a job using. The resources in this video have more information about bash scripts and HPC clusters.

Installing R and Rstudio2:19
Learn how to install the coding language R and the IDE Rstudio!
Installing the required packages for the exercise8:13
Install the required packages required for this course. Please remember all the code is supplied in the resources of the first Lecture.
Importing the Salmon abundance estimations into R using txiimport16:12
I did not do all the quality control in one lecture! There is quite a bit to it :). This lecture covers how to import the salmon quant files into R in a useable format for DESeq2.
Creating a annotated dataset using BiomartR21:17
Learn how to use BiomartR to change your ENSEMBL IDs to gene names.
Counts and Quality Control (Part 1)8:28
This lecture will show you how to analyse your count data to make sure you have enough annotated read to continue.
Count and Quality Control (Part 2)25:38
After this lesson, you will know how to look at the results of the count's analysis graphically.
Starting Differential Expression using DESeq218:18
Learn how to use DESeq2 to carry out differential expression.
Visulising the DESeq2 results20:42
Learn how to visualise DEsSeq2 data!
Fast Gene Set Enrichment Analysis (fgsea)19:01
Learn how to preform fgsea on your DESeq2 results!
KEGG Pathviews and clusterprofiler overrepresentation analysis35:27

Requirements

Background knowlege of Biology and genetics
Knowledge of the composition and function of DNA & RNA.
An interest in the bioinformatics side of genetics and how it applies to understanding gene expression in different conditions.
No background knowledge in coding needed but an interest is required.

Description

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. Discover how the transcriptome of cell changes throughout its growth cycle. To ensure that you have a full understanding of how to perform RNA sequencing yourself every step of the process will be explained! You will first learn how to use 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:

Download publically available data from a FTP site directly to a HPC cluster.
Obtain the needed raw files for genome alignment.
Perform genome alignment using a tool called Salmon.
Analyse the quality of your RNA-seq data using FastQC and MultiQC, while also doing a custom analysis in R.
Carry out a differential expression using DESeq2 to find out what changes between a cell on day 4 Vs day 7 of growth.
Carry out gene ontology analysis to understand what pathways are up and down-regulated using fgsea and clusterprofiler.
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!

Who this course is for:

People who want to preform RNA-seq themselves through tools such as R and the command line.
People generally interested in new research methologies and would like to try them themselves!
People looking to learn about RNA-seq and differential gene expression.
Become independent from bioinformatic tools such as Galaxy.

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

What you'll learn

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Course content

Introduction1 lecture • 10min

Using Google Colabs bash shell function to write a RNA-Seq pipeline.8 lectures • 1hr 26min

Differential Expression and Gene Ontology in R10 lectures • 2hr 56min

Requirements

Description

Who this course is for: