Rnaseq Data Analysis Using Shell Scripting And R

Become a master in performing RNAseq analysis on linux command-line and use R to perform DE analysis and clustering

What you'll learn

Basics of NGS data analysis and how to perform Differential gene expression analysis for RNAseq dataset

Generating Quality Control metrics and statistics

Mapping Reads to the genome

Differential gene expression

Using Conda for installation of bioinformatics tools

Processing RNA sequencing data

UNIX command-line tools for processing the data

Transcript quantification

Performing Principal Component Analysis (PCA)

Performing Clustering analysis using gene expression data

Requirements

Background knowledge of biology

Interest in working with UNIX command-line tools

Interest in bioinformatics tools installation

Interest in genomics as well as applying computational methods for processing transcriptomics datasets

No programming experience needed. You will learn everything you need to know

All downloadable resources provided

Description

In this course, you will learn how to perform RNAseq data analysis via linux command line. This course provides a comprehensive introduction to RNAseq data analysis, covering the key concepts and tools needed to perform differential expression analysis and functional annotation of RNAseq data. Students will learn how to preprocess raw sequencing data, perform quality control, and align reads to a reference genome or transcriptome. The course will also cover differential expression analysis using statistical methods and visualisation of results using popular tools such as R. You will learn how to do end-to-end RNAseq data analysis which includes pre-processing of RNAseq data, Quality Control analysis, Differential Gene Expression analysis, Clustering and Principal Component Analysis of the gene expression data. You will also learn how to download data, install the bioinformatics/IT softwares using Conda/Anaconda on Mac, Windows or Linux platforms. I will guide you through performing differential expression analysis on RStudio (graphical user interface for R language).Throughout the course, students will work with real-world datasets and gain hands-on experience with popular bioinformatics tools and software packages. By the end of the course, students will have a thorough understanding of RNAseq data analysis and will be able to perform their own analyses of gene expression data. This course is ideal for researchers, scientists, and students who are interested in understanding the molecular basis of gene expression and exploring the potential applications of RNAseq technology. No prior bioinformatics or programming experience is required, but a basic knowledge of molecular biology and genetics is recommended.

Overview

Section 1: Introduction and Installation

Lecture 1 Introduction

Lecture 2 Installing Conda

Lecture 3 Installing Bioinformatics tools using Conda

Section 2: Preparing Data for Analysis

Lecture 4 Organising the files

Lecture 5 Obtaining the Raw Data

Lecture 6 Obtaining the Genome files

Section 3: Quality Control of RNA-seq data

Lecture 7 QC analysis using FastQC for Single-End and Paired-End reads

Section 4: Read Trimming and QC analysis

Lecture 8 Adaptor and Quality Trimming using Trimmomatic

Lecture 9 QC analysis of Trimmed Reads using MultiQC

Section 5: Genome Indexing and Read Mapping

Lecture 10 Genome Indexing using STAR

Lecture 11 Read Mapping using STAR

Section 6: Duplicate Read Marking

Lecture 12 Marking Duplicate reads using Picard and indexing BAM files

Section 7: Obtaining Alignment Statistics

Lecture 13 Printing Alignment statistics using Bamtools

Section 8: Obtaining Counts (read abundance) using FeatureCounts

Lecture 14 Generating Gene Counts for DE analysis

Section 9: Differential Expression (DE) analysis using DESeq2 on RStudio

Lecture 15 Preparing the files for DE analysis

Lecture 16 Installing libraries for DE analysis on RStudio

Lecture 17 Performing the DE analysis using DESeq2 - Part1

Lecture 18 Performing the DE analysis using DESeq2 - Part2

Section 10: Principal Component Analysis and Clustering analysis of gene expression data

Lecture 19 Performing PCA analysis and generating PCA plot

Lecture 20 Performing Clustering analysis of gene expression data

People interested in learning Next Generation Sequencing data analysis methods,Beginner Bioinformatician looking to understand end-to-end pipeline for transcriptomics data analysis,People looking to understand differential gene expression analysis,People interested to carry out bioinformatics analysis with command-line tools