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 Project

This 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 check

• BWA for alignment

• Samtools and FeatureCounts for BAM file handling and quantification

• R and DESeq2 for DEG analysis

• clusterProfiler for enrichment and pathway analysis

We 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.