Enrichment analysis: interpret gene lists like a pro

How to get more information from the results of high throughput gene expression data with easy-to-use web tools?
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  • Lectures 16
  • Length 43 mins
  • Skill Level Intermediate Level
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
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About This Course

Published 8/2014 English

Course Description

Gene enrichment analysis is the most popular systematic approach to assign ontologies, pathways, and transcription factors to gene lists usually resulted from high throughput experiments. This short course introduces the two most frequently applied methods to locate the common features of large gene lists, and provide opportunities to practice this analysis in the most common research scenarios using GeneTrail (for gene set enrichment analysis) and WebGestalt (for over-representation analysis), the two web tools used most often in the field.

You get written material, video explanations for the background, and prepared example data files for practices. You are provided with (almost) real life discussions between a wet-lab biologist and a bioinformatician to clarify the most common misunderstandings related to this analysis. You are led step by step by two experts of the field, both active researchers; and in the meantime, you are challenged to proof your knowledge in quizzes.

If you have a solid background in molecular biology research, you will be able to expert these methods in a maximum of 8-10 active course hours, but if you have only a general idea of the field, you will get a plenty of background material to learn and practice all in a few days. We recommend to cover all the provided material approximately in a week to maximize the efficiency of your learning.

As you know, bionformatics and data analysis skills are highly demanded among molecular biology and pharma researchers. Why not equip yourself with these easy but very useful tools to extend your future potential as a researcher?

What are the requirements?

  • Basic knowledge of molecular biology
  • Very general idea about high throughput gene expression experiments (microarray, RNAseq)

What am I going to get from this course?

  • You will learn how to perform over-representation and gene set enrichment analysis on microarray and RNA-Seq
  • You will understand the relevant statistical approaches which are needed to find Gene Ontology, KEGG or other pathway terms which are associated with gene lists
  • You will be able to design and execute such analysis
  • You will be able to use GeneTrail and WebGestalt web tools
  • You will be able to interpret the results from such an analysis

Who is the target audience?

  • Biotechnology university students
  • Molecular biology researchers
  • College students interested in bioinformatics
  • Clinical researchers

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


Section 1: Welcome to the Gene Enrichment Analysis course!

Short guide to the course. What will you learn, and how can you use it?

2 pages

Introduction and background.

3 pages

Definition of terms often used in context with enrichment analysis.

Enrichment Quiz
6 questions
Section 2: Over-Representation Analysis (ORA)

Theory behind over-representation analysis.

2 pages

What you need for analyzing over-representation and what you get as a result?


In this lecture, you have an opportunity to practice over-representation analysis with real life experimental data. If you have own data from your own research, please do not hesitate to test its performance. Otherwise, you can use the datasets from the supplementary material.

human_gene_set.txt – a list of 892 human genes identified by Entrez Gene IDs.

mouse_probe_set.txt – mouse genes identified by 247 probe IDs from Affymetrix Mouse430_2 platform.


Questions from the biologist and answers from the bioinformatician.

1 page

Most important concepts of over-representation analysis.

ORA Quiz
3 questions
Section 3: Gene Set Emrichment Analysis (GSEA)

Theoretical background of gene set enrichment analysis.

2 pages

What you need for gene set enrichment analysis, and how to understand the results.


Step-by-step instruction to accomplish Gene Set Enrichment Analysis using a web service. You can use your own data or the set provided in the supplementary material.

mouse_ranked_gene_set.txt - a ranked list of 2300 mouse genes identified by Entrez Gene IDs.


The biologist asks, the bioinformatician answers.

1 page

Most important concepts of gene set enrichment analysis.

6 questions
Section 4: Concluding remarks
2 pages

Comparison of ORA and GSEA.

2 pages

If you speak the language of computers...


Short summary of the learned techniques.

4 questions

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Instructor Biography

Dr. Csaba Ortutay, Adjunct professor of bioinformatics

I have experience with bioinformatics related to human immunity, focused on databases, evolution and systems biology. I teach genetics, phylogenetics, and evolution at different Universities in Finland and Hungary.

My expert areas: computational and systems biology related to immune processes, molecular evolution of gene families.

I have conducted bioinformatics research during the past two decades using and developing algorithms and tools for answering research questions using computers as tools. I use (among many other methods) enrichment analysis routinely when I want to characterize larger gene or protein groups, and I have applied it for locating essential genes in human immune functions. I have published more than two dozen scientific publications from my results, for example, about disease gene identification, the evolution of immune functions, and biological networks.

Other areas I am involved: Biological databases, Genomics related web applications, University level teaching online and on-site.

Instructor Biography

PhD Zsuzsanna Ortutay, Molecular biologist, immunologist

My current project focuses on the role of proprotein convertase FURIN in T cell receptor signaling. My main fields of interest are the molecular mechanisms of T cell stimulation in health and diseases, and the role of glycans in immune mechanisms.

I investigated the role of glycosidase enzymes in the pathomechanism of rheumatoid diseases. I also have experiences with protein structure function studies.

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