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

Bioinformatics: RNA-Seq/Differential expression in Bash & R!, available at $39.99, has an average rating of 3.8, with 19 lectures, based on 10 reviews, and has 74 subscribers.

You will learn about 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. This course is ideal for individuals who are People who want to preform RNA-seq themselves through tools such as R and the command line. or People generally interested in new research methologies and would like to try them themselves! or People looking to learn about RNA-seq and differential gene expression. or Become independent from bioinformatic tools such as Galaxy. It is particularly useful for People who want to preform RNA-seq themselves through tools such as R and the command line. or People generally interested in new research methologies and would like to try them themselves! or People looking to learn about RNA-seq and differential gene expression. or Become independent from bioinformatic tools such as Galaxy.

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

Summary

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

Price: $39.99

Average Rating: 3.8

Number of Lectures: 19

Number of Published Lectures: 19

Number of Curriculum Items: 19

Number of Published Curriculum Objects: 19

Original Price: £34.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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.

Target Audiences

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.

Ever wonder which technologies allow researchers to discovernew markers of cancer or to get a greater understandingof genetic diseases? Or even just what genes are importantfor 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 understandbut 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 Colabto 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Using Google Colabs bash shell function to write a RNA-Seq pipeline.

Lecture 1: Getting Started with Google Collab

Lecture 2: Downloading the raw sequence reads

Lecture 3: Installing the tools we need for RNA-seq in Google Colab

Lecture 4: Creating a transcriptome index using Salmon

Lecture 5: Aligning the genome with Salmon quant.

Lecture 6: Running Fastqc and collating reports using MultiQC

Lecture 7: Talking through the MultiQC Report

Lecture 8: A example of a Bash Script and workflow managers.

Chapter 3: Differential Expression and Gene Ontology in R

Lecture 1: Installing R and Rstudio

Lecture 2: Installing the required packages for the exercise

Lecture 3: Importing the Salmon abundance estimations into R using txiimport

Lecture 4: Creating a annotated dataset using BiomartR

Lecture 5: Counts and Quality Control (Part 1)

Lecture 6: Count and Quality Control (Part 2)

Lecture 7: Starting Differential Expression using DESeq2

Lecture 8: Visulising the DESeq2 results

Lecture 9: Fast Gene Set Enrichment Analysis (fgsea)

Lecture 10: KEGG Pathviews and clusterprofiler overrepresentation analysis

Instructors

Adrian Bourke
PhD Student in Synthetic Biology

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 2 votes
4 stars: 3 votes
5 stars: 3 votes

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