Bioinformatics for Beginners 2026: Differential Gene Expression and Pathway Analysis in Bulk RNA-Seq
When: August 11, 2026 - August 20, 2026Share
About this Course
This module is the third and final installment in Bioinformatics for Beginners: 2026, a training series that introduces bioinformatics concepts through a practical bulk RNA-seq analysis workflow.
In this four-lesson module, participants will learn the foundational concepts underlying differential expression and functional enrichment analysis while gaining hands-on experience using iDEP to perform these analyses. This course is designed for researchers seeking practical skills for interpreting gene expression data and generating biologically meaningful insights.
Description
This lesson introduces the principles of differential gene expression (DEG) analysis. Participants will learn about common normalization strategies and gain a conceptual understanding of the statistical frameworks used by widely adopted DEG tools, including limma, edgeR, and DESeq2, with an emphasis on their assumptions, strengths, and appropriate use cases. This is not a hands-on lesson.
This lesson introduces the principles of differential gene expression (DEG) analysis. Participants will learn about common normalization strategies and gain a conceptual understanding of the statistical frameworks used by widely adopted DEG tools, including limma, edgeR, and DESeq2, with an emphasis on their assumptions, strengths, and appropriate use cases. This is not a hands-on lesson.
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Where
Online WebinarDescription
This hands-on lesson demonstrates how to perform DEG analysis using the iDEP web platform on Biowulf. Participants will learn how to upload data, configure analysis settings, and interpret key outputs such as quality control plots, volcano plots, heatmaps, and differential expression tables to identify biologically meaningful gene expression changes.
This hands-on lesson demonstrates how to perform DEG analysis using the iDEP web platform on Biowulf. Participants will learn how to upload data, configure analysis settings, and interpret key outputs such as quality control plots, volcano plots, heatmaps, and differential expression tables to identify biologically meaningful gene expression changes.
Register
Where
OnlineDescription
Whether you are measuring mRNA expression, protein expression, DNA methylation, expressed miRNAs, protein binding to DNA or RNA, etc., you will likely end up with a list of genes or gene products from which you would like to derive functional relationships. In the -omics world, functional enrichment analysis is an umbrella term encompassing approaches used to derive biological / functional meaning from gene lists. This lesson introduces concepts, methods, tools, and databases related to functional Read More
Whether you are measuring mRNA expression, protein expression, DNA methylation, expressed miRNAs, protein binding to DNA or RNA, etc., you will likely end up with a list of genes or gene products from which you would like to derive functional relationships. In the -omics world, functional enrichment analysis is an umbrella term encompassing approaches used to derive biological / functional meaning from gene lists. This lesson introduces concepts, methods, tools, and databases related to functional enrichment and pathway analysis. This is NOT a hands-on lesson.
Register
Where
onllineDescription
This practical session explores the pathway and enrichment analysis options available within iDEP. While reviewing the range of supported analyses, the lesson focuses on pre-ranked Gene Set Enrichment Analysis (GSEA), guiding participants through gene ranking strategies, execution of GSEA in iDEP, and interpretation of enrichment plots, leading-edge genes, and pathway-level results.
This practical session explores the pathway and enrichment analysis options available within iDEP. While reviewing the range of supported analyses, the lesson focuses on pre-ranked Gene Set Enrichment Analysis (GSEA), guiding participants through gene ranking strategies, execution of GSEA in iDEP, and interpretation of enrichment plots, leading-edge genes, and pathway-level results.