Functional enrichment analysis is a method to determine classes of genes or proteins that are over-represented in a large group of genes or proteins and may have relations with disease phenotypes. This approach uses statistical methods to determine significantly enriched groups of genes. After obtaining the differentially expressed genes in RNA-seq analysis, we finally perform the downstream analysis. In downstream analysis first comes the Functional Enrichment Analysis. 

BioCode is offering a Functional Enrichment Analysis Course based on R scripts and webservers. You will learn how to perform Functional Enrichment Analysis using webservers and R scripts, even if you have no knowledge of scripting. You will first go through the theory of Functional Enrichment Analysis, Gene Ontology, Pathways Analysis and by the end of this course, you will have a greater theoretical and practical understanding of Functional Enrichment Analysis, Gene Ontology Analysis, and KEGG Pathways Analysis. You’ll learn Functional Enrichment Analysis workflow and analysis, leading to skills in discovering differentially expressed genes and biological processes which might be important for your condition of interest.

This course is for absolute beginners who have no prior experience in Functional Enrichment Analysis, Downstream Analysis, GO, and KEGG Pathways analysis. The course covers both theoretical and practical aspects of Functional Enrichment Analysis which includes Gene Ontology, KEGG Pathways, and Protein-Protein Interaction Analysis. This course is project-based. That means the instructor takes a raw and real-life data set and performs the entire analysis on it which you can follow step-by-step. 

This course will include the following sections:

Section 1: In-depth Functional Enrichment Analysis of the DEGs (Theoretical)

Description: This section will focus on making sure that the students learn about the theoretical concepts of functional enrichment analysis and how it is done.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Understand What happens after DEG Analysis.
2. Explain how we can interpret the Biomarkers further.
3. Describe Functional Enrichment.
4. Discuss the Tools for Functional Enrichment.

Section 2: Gene Ontology Analysis Using topGO and EnrichR (Practical)

Description: This section will focus on making sure that the students learn how gene ontology analysis is performed using EnrichR and topGo package.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Perform Biological Scripting in R Language.
2. Perform Gene Ontology Analysis Using topGo in R.
3. Perform Gene Ontology Analysis Using enrichR.

Section 3: Pathways Analysis Using KEGG, PANTHER, Reactome (Practical)

Description: This section will focus on making sure that the students learn about KEGG, PANTHER and Reactome and how the pathways analysis is performed using each server.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Perform KEGG pathways analysis.
2. Perform PANTHER pathways analysis.
3. Perform Reactome pathways analysis.

Section 4: Protein-Protein Interaction Analysis Using STRING (Practical)

Description: This section will focus on making sure that the students learn about protein-protein interaction analysis and how it is performed on the STRING database.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Explain Protein-Protein Interaction Analysis.
2. Discuss STRING database.
3. Perform Protein-Protein Interaction Analysis on STRING database.