Differential gene expression analysis in galaxy
Master web-based transcriptomics and RNA-seq analytics without writing a single line of code. Learn to analyze, visualize, and interpret high-throughput differential gene expression data in Galaxy.
Course Description
This intensive online bootcamp by Dr. Omics Edu simplifies the complexities of transcriptomics data analysis using the open-source, web-based Galaxy platform. Designed for life scientists, this course removes the barrier of command-line programming, allowing you to focus entirely on biological discoveries. Participants will learn how to navigate RNA-seq pipelines, evaluate sequencing quality control, and perform robust differential gene expression analysis. The training emphasizes key visual interpretation methods, including generating publication-ready heatmaps and volcano plots directly within the software. By understanding how to transition from raw sequencing reads to clear fold-change insights, you will gain a strong grasp of data-driven molecular biology. This project-centric format ensures that researchers can confidently handle transcriptomic workflows independently. Ultimately, this bootcamp provides the core analytic foundations required to feed clean expression data into downstream functional annotation and AI modeling pipelines.
What You'll Learn
The core principles of transcriptomics, RNA-seq experimental design, and data processing logic.
How to import, manage, and process large-scale transcriptomic datasets within the Galaxy platform interface.
Advanced quality control methods using tools to filter and trim raw sequencing reads.
Standard pipelines to calculate differential gene expression metrics and identify statistically significant targets.
How to construct and interpret key data visualizations such as heatmaps, PCA, and volcano plots.
Curriculum
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Introduction to Transcriptomics: Understanding RNA-seq principles, workflow architectures, and the Galaxy ecosystem.
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Quality Control & Preprocessing: Running read-quality assessments, adapter trimming, and data filtering protocols.
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Sequence Alignment & Quantification: Overview of mapping reads to reference genomes and quantifying gene expression levels.
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Differential Expression Analysis: Running standard bioinformatic statistical packages within Galaxy to find up- and down-regulated genes.
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Data Visualization Masterclass: Generating interactive volcano plots, cluster heatmaps, and plotting expression trends.
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