Description

RNA-Seq Analysis Course: From FASTQ to Differential Expression

RNA-Seq Analysis Course is a comprehensive, hands-on training program designed to teach you the complete RNA sequencing workflow used in modern genomics, transcriptomics, and computational biology. Whether you’re a student, researcher, or life science professional, this course will help you master RNA-Seq data analysis using real-world datasets and industry-standard bioinformatics tools.

RNA sequencing (RNA-Seq) has become one of the most important technologies for studying gene expression, identifying differentially expressed genes (DEGs), discovering biomarkers, and understanding biological pathways. As sequencing technologies continue to generate massive amounts of transcriptomic data, the ability to perform RNA-Seq data analysis has become an essential skill for careers in research, biotechnology, healthcare, and bioinformatics.

In this RNA-Seq Analysis Course, you’ll learn the complete workflow—from raw FASTQ files to differential gene expression and functional enrichment analysis. Through step-by-step demonstrations and practical projects, you’ll gain the confidence to analyze your own RNA-Seq datasets and build reproducible bioinformatics pipelines.

What You’ll Learn

By the end of this course, you will be able to:

• Understand the principles and applications of RNA sequencing.
• Work with raw FASTQ sequencing files.
• Perform quality assessment using FastQC.
• Preprocess sequencing reads with Fastp.
• Align reads to a reference genome using BWA.
• Process alignment files with Samtools.
• Quantify gene expression using FeatureCounts.
• Perform differential gene expression analysis using DESeq2.
• Conduct Gene Set Enrichment Analysis (GSEA) using clusterProfiler.
• Visualize and interpret transcriptomic results.
• Build a complete RNA-Seq analysis pipeline using Linux and R.

Course Structure

The course is organized into 9 comprehensive sections:

1. Course Introduction & Linux Setup
2. Linux for Bioinformatics
3. RNA-Seq Fundamentals
4. Data Acquisition & Preprocessing
5. Read Alignment to the Reference Genome
6. Gene Expression Quantification & Normalization
7. R & RStudio Setup
8. Differential Expression & Functional Enrichment Analysis
9. Final Quiz & Capstone Project

Each module combines theory with practical demonstrations, assignments, and hands-on exercises using real sequencing datasets.

Hands-On Projects

This RNA-Seq Analysis Course focuses on practical learning. Throughout the course, you’ll complete real-world projects that include:

• RNA-Seq quality control
• Read preprocessing
• Genome alignment
• Gene expression quantification
• Differential expression analysis
• Functional enrichment analysis
• Biological interpretation of transcriptomic data

These projects prepare you to confidently analyze RNA-Seq datasets used in academic research and industry.

Software and Tools You’ll Use

Gain hands-on experience with professional bioinformatics tools, including:

• FastQC
• Fastp
• BWA
• Samtools
• FeatureCounts
• R
• RStudio
• DESeq2
• clusterProfiler
• Linux

Who Should Enroll?

This course is ideal for:

• Bioinformatics students
• Biology and biotechnology students
• Master’s and PhD researchers
• Genomics and transcriptomics researchers
• Computational biologists
• Life science professionals
• Anyone who wants to learn RNA-Seq data analysis from beginner to advanced

No prior RNA-Seq experience is required. The course provides step-by-step guidance from the fundamentals to advanced transcriptomic analysis.

Why Choose This RNA-Seq Analysis Course?

• Complete RNA-Seq workflow from FASTQ to differential expression.
• Learn using real sequencing datasets.
• Hands-on training with industry-standard bioinformatics software.
• Practical Linux and R-based analysis pipelines.
• Beginner-friendly, step-by-step instruction.
• Gain job-ready skills for research, biotechnology, and computational biology.

Enroll today and master RNA-Seq data analysis by building complete bioinformatics pipelines and developing practical skills used in modern genomics and transcriptomics research.