Extracting Biological Information from Gene Lists

Available Dates

Course Location Starting Date
Extracting Biological Information from Gene Lists Online 15 April 2026 View

The end point of many different high throughput experiments is a list of interesting genes, often accompanied by metrics such as p-values or fold changes. Making biological sense of these lists can be challenging but is crucial if we are to target the most relevant aspects of biology.

In this course we look at the data sources and analysis techniques which allow us to find the interesting biology behind a set of genes. We look at functional gene set enrichment analysis from both lists of gene names and quantitative data. We consider the choices we get in these techniques, the artefacts and biases which can mislead us, and the options for how to present the results we get.

We run the analyses initially in a web browser, but then also show how to do this in R

Pre-Course Requirements & Suggestions

This course makes no assumption about specific skills or knowledge

Course Content

(click to expand each section)

We start by looking at Gene Set Analysis. We look at the different sources of functional gene sets and how these can be compared to either categorical or quantitative experimental gene sets to identify biological themes. We examine a number of different tools which can be used to perform these types of analysis.
We move on to looking at ways in which gene set analyses can give biased or misleading results. We show how technical effects can produce convincing looking results and how there are other explanations which are worth considering. We look at ways to spot and correct for biases where they are present.
Here we look at the different options for the presentation and visualisation of gene set analysis results. We go from simple tabular representations through to more complex graphing, network and pathway visualisaitons and show how they can help to make the results more interpretable.
In the final section we use the ClusterProfiler toolset in R to show how we can automate gene set analysis statistics, and see how we can use different packages to plot out the results.