Hands-On Rna-Seq Analysis Crash Course: From Fastq To Degs

Master RNA-Seq Data Analysis from Scratch: Learn Linux, FASTQ Processing, Alignment, and Differential Gene Expression

What you'll learn

Understand the basics of Linux and command-line operations essential for bioinformatics.

Set up your bioinformatics environment on Windows using Linux (WSL) or VirtualBox.

Retrieve RNA-Seq datasets from public databases like NCBI SRA or ENA.

Perform quality control and preprocessing on FASTQ files.

Learn how to align RNA-Seq reads to a reference genome using tools like HISAT2, STAR, or BWA.

Quantify gene expression levels from alignment files (BAM).

Use R and RStudio to perform downstream analyses including: Differential gene expression analysis (DGEA) and Gene set enrichment analysis (GSEA)

Work on real-life assignments and a capstone project that solidify your skills and build your portfolio.

Hands-on approach: Real datasets, practical workflows, and terminal-based commands.

No prior experience required: Beginner-friendly and fully guided.

Portfolio-ready assignments and a capstone project for showcasing your skills.

Requirements

A basic understanding of biology and gene expression

Curiosity to explore computational tools in biology

A computer or laptop with internet access

Willingness to install open-source software (guidance is provided)

Enthusiasm to learn both theory and hands-on bioinformatics skills

No programming experience? No problem. We’ll teach you how to use the Linux terminal and R for RNA-Seq analysis, even if you're starting from zero.

Description

This RNA-Seq Data Analysis course is going to be a game changer for you. In the modern era of genomics and transcriptomics, we are witnessing an explosion of RNA sequencing data. If you want to survive and grow in research, academia, or the bioinformatics industry, learning RNA-Seq is no longer optional — it’s essential. Traditional biology is no longer sufficient to handle this scale of data. This is where computational biology and bioinformatics come into play, helping researchers make sense of massive datasets through efficient pipelines and analysis tools.RNA-Seq (RNA sequencing) is one of the most powerful technologies used to study gene expression and discover differentially expressed genes (DEGs). It helps uncover the molecular mechanisms behind diseases, responses to treatments, and regulatory pathways in all living organisms.Keeping this demand in view, we have brought you a complete hands-on crash course on RNA-Seq analysis that takes you from raw FASTQ files all the way to DEGs and gene enrichment results. This course will help you master the complete pipeline of RNA-Seq analysis using a blend of command-line tools and R programming.This course is divided into 9 comprehensive sections:(1) Course & Linux Introduction(2) Basic Linux for Bioinformatics(3) Foundations of RNA-Seq(4) Data Acquisition & Preprocessing(5) Mapping to the Reference Genome(6) Quantification & Normalization(7) R and RStudio Setup(8) Downstream Analysis: DEGs & GSEA(9) Final Quiz & Capstone ProjectThis course is a unique blend of theory and hands-on practice. First, you will learn the basics of RNA-Seq and Linux. Then, you will perform real-time preprocessing, alignment, quantification, and downstream analysis using publicly available RNA-Seq data. You’ll also be completing assignments and a capstone project, giving you the practical experience needed to confidently handle real-world datasets.You’ll work with some of the most widely used bioinformatics tools such as:FastQC for quality checkBWA for alignmentSamtools and FeatureCounts for BAM file handling and quantificationR and DESeq2 for DEG analysisclusterProfiler for enrichment and pathway analysisWe assure you that by the end of this course, you will be able to build your own RNA-Seq analysis pipeline from scratch using command-line tools and R. This will not only add a valuable skill to your CV but also transform the way you look at transcriptomics and biological data analysis.We hope this course will be worth your time and investment — and it will open up new opportunities for you in the ever-evolving field of bioinformatics.

Overview

Section 1: Course Introduction

Lecture 1 Introduction of Course

Section 2: Basic Linux For Bioinformatics

Lecture 2 Introduction to Linux

Lecture 3 Environment Setup On Windows

Lecture 4 Navigating The Linux File System

Lecture 5 Basic Linux Commands For Bioinformatics

Lecture 6 Working With Text Files And Data Processing In Linux For Bioinformatics

Lecture 7 File Compression And Archiving In Bioinformatics

Section 3: Foundations of RNA-Seq

Lecture 8 Introduction To RNA-Seq

Section 4: Data Acquisition & Preprocessing

Lecture 9 Retrieving RNA-Seq Data

Lecture 10 Preprocessing Of RNA-Seq Data

Section 5: Mapping to the Reference Genome

Lecture 11 Indexing And Alignment To Reference Genome

Section 6: Quantification & Normalization

Lecture 12 Quantifications Of Bam Files

Section 7: R and R Studio

Lecture 13 Introduction of R

Lecture 14 Setting Up R And R Studio For Bioinformatics

Lecture 15 Package Installation In R

Section 8: Downstream Analyses

Lecture 16 Differential Expression Analysis Using R

Lecture 17 Gene Set Enrichment Analysis In R

Section 9: Final Project & Quiz

Biology, biotechnology, or computer science students looking to learn RNA-Seq data analysis.,Researchers in genomics or molecular biology aiming to analyze their own sequencing data.,Professionals transitioning to the field of bioinformatics.,Anyone interested in learning how computational tools are used in transcriptomics research.