Extracting Biological Information from Gene Lists

About the course  |  Intended audience  |  Prerequisites  |  Content details

About the course

Many experimental studies produce lists of genes or transcripts associated with a biological condition or phenotype. While small lists can often be examined individually, larger datasets usually require structured approaches to identify common biological themes and functional relationships.

This course provides a practical introduction to extracting biological information from gene lists using gene set enrichment approaches. Participants will learn about the software packages, databases, and statistical methods used to perform functional analyses and interpret their results.

The course covers the theory of gene set enrichment, different types of enrichment tests, and the sources of gene sets available for analysis. Participants will gain practical experience using both web-based tools and programmatic approaches in R. The course also discusses artefacts and sources of bias that can lead to misleading conclusions and considers appropriate ways to perform analyses and present results.

By the end of the course, participants should be able to perform and critically evaluate gene set enrichment analyses and effectively communicate their findings.

Teaching is primarily hands-on, with short presentations and demonstrations introducing the concepts and methods needed to analyse and interpret gene lists.

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Intended audience

This course is suitable for:

• researchers and students who work with gene or transcript lists generated from experimental studies
• participants who want practical experience with gene set enrichment and functional analysis approaches
• researchers interested in extracting biological meaning from high-throughput datasets
• participants with a working knowledge of R

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Prerequisites

Participants should have:

• a working knowledge of R

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Content details

The course covers the following topics:

• Introduction to gene set analysis
  Introduces the principles of gene set enrichment analysis and explains how functional analyses can be used to identify biological themes within gene and transcript lists.
• Sources of gene sets
  Covers the major databases and resources that provide gene sets for functional analysis and discusses their strengths and appropriate applications.
• Testing for gene enrichment
  Introduces the statistical approaches used to assess enrichment and explains how different enrichment tests can be applied to different types of data.
• Qualitative and quantitative enrichment analyses
  Covers approaches for analysing both ranked and unranked gene lists and demonstrates how different analytical strategies can be used to address biological questions.
• Web-based gene set enrichment tools
  Introduces several web-based tools for gene set enrichment analysis and provides practical experience in performing functional analyses using these resources.
• Programmatic analysis in R
  Covers approaches for conducting gene set enrichment analyses programmatically in R and demonstrates how these methods can be integrated into reproducible analysis workflows.
• Exploring and presenting results
  Introduces approaches for interpreting, visualising, and communicating the results of gene set analyses for reports and publications.
• Artefacts and biases in gene set analysis
  Discusses common sources of bias and analytical artefacts that can lead to misleading conclusions and considers strategies for performing robust and reproducible analyses.

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