Bioinformatics: Guide To Rna-Seq With No Coding Required!

Learn to process & analyse RNA-seq data without code: Transcriptomics, Differential expression, STAR, Pathway analysis

What you'll learn
The basics of Next Generation Sequencing and how it can be used for Differential gene expression analysis via RNA sequencing.
Preprocessing RNA sequencing data.
Aligning the reads to a genome.
Transcript quantification.
Differential Expression.
Gene ontology and Pathway analysis
Ultimately understand how technologies like RNA sequencing could be used to identify specific genes that can cause certain conditions.

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.

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. Throughout this course, you will be equipped with the tools and knowledge to not only understand but perform RNA sequencing and discover how the transcriptome of a cell changes throughout its growth cycle. To avoid the need for complex software installations, coding experience and in some cases a Linux operating system we will be using a free bioinformatics tool called Galaxy for the whole analysis! Not only that, but we will also be using the STAR pipeline which is currently supported by the ENCODE project!Once you've completed this course you will know how to:Download publically available data from papers straight onto Galaxy.Obtain the needed raw files for genome alignment. Perform genome alignment using a tool called STAR.Create count tables from your alignment using FeatureCounts.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. Use Pathview to create annotated KEGG maps that can be used to look at specific pathways in more detail.Use a web browser-based tool called DEGUST as an alternative to using DESeq2.Practical BasedThe 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 14 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 galaxies limitations 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 there own data!

Overview
Section 1: Introduction and Getting Started

Lecture 1 Who Am I?

Lecture 2 Obtaining the Raw Data

Lecture 3 Downloading the Data on Galaxy

Section 2: Quality Control and Genome Alignment

Lecture 4 Note on Lecture 5

Lecture 5 Quality Control of Sequencing Data using FastQC

Lecture 6 Dealing with MultiQC Report Issues

Lecture 7 Obtaining the Reference Genome Files Needed for Alignment

Lecture 8 Aligning the RNA Sequencing Reads to the Reference Genome.

Section 3: Getting Transcript Abundance uisng FeatureCounts

Lecture 9 Using Infer Experiment to Determine FeatureCounts Input Settings

Lecture 10 Using FeatureCounts to Create a Counts Table based on Gene and Exon methologies

Section 4: Carrying out Differential Expression and Gene Ontology Analysis

Lecture 11 Carrying Out Differential Expression Using DESeq2

Lecture 12 Biomart has Updated

Lecture 13 Looking At The Annotated DESeq2 Results and Preparing Files For GOseq

Lecture 14 Analyzing the GOseq Results

Lecture 15 Preparing the Needed Files For Pathview Analysis

Lecture 16 Discussing the KEGG Pathview Results

Section 5: Optional: Using DEGUST Instead of DESEQ2

Lecture 17 DEGUST Walkthrough

People generally interested in new research methologies and would like to try them themselves!,Beginner Bioinformaticians looking to understand the process of RNA sequencing,People interested in researching the effects of different pathologies on gene expression or even how gene expression changes over the course of a cell's growth curve.,People looking to carry out differential gene expression and gene ontology analysis.,People who want to carry out bioinformatic analysis without the need for complex code.

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