Skip to main content

ChIP-seq with Bioconductor in R

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

Start Course for Free
4 Hours13 Videos46 Exercises3,057 Learners
3650 XP

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

Course Description

ChIP-seq analysis is an important branch of bioinformatics. It provides a window into the machinery that makes the cells in our bodies tick. Whether it is a brain cell helping you to read this web page or an immune cell patrolling your body for microorganisms that would make you sick, they all carry the same genome. What differentiates them are the genes that are active at any given time. Which genes these are is determined by a complex system of proteins that can activate and deactivate genes. When this regulatory machinery gets out of control, it can lead to cancer and other debilitating diseases. ChIP-seq analysis allows us to understand the function of regulatory proteins, how they can contribute to disease and can provide insights into how we may be able to intervene to prevent cells from spinning out of control. In this course, you will explore a real dataset while learning how to process and analyze ChIP-seq data in R.

  1. 1

    Introduction to ChIP-seq


    Introduction to ChIP-seq experiments. Why are they interesting? What sort of phenomena can be studied with ChIP-seq and what can we learn from these experiments.

    Play Chapter Now
    What is ChIP-seq?
    50 xp
    ChIP-seq recap
    50 xp
    Sequencing data
    100 xp
    Peak calls
    100 xp
    ChIP-seq Workflow
    50 xp
    Heat map
    100 xp
    Genes that make a difference
    100 xp
    ChIP-seq results summary
    50 xp
    Understanding ChIP-seq data
    50 xp
  2. 4

    From Peaks to Genes to Function

    Being able to identify differential binding between groups of samples is great, but what does it mean? This chapter discusses strategies to interpret differential binding results to go from peak calls to biologically meaningful insights.

    Play Chapter Now

In the following tracks

Analyzing Genomic Data


David CamposRichie CottonShon Inouye
Peter Humburg Headshot

Peter Humburg


Peter Humburg has extensive experience in the analysis of genomic data as a Bioinformatician. His doctoral work focused on the development of statistical methods for the analysis of ChIP-seq data. Peter now works as a Statistician at Macquarie University in Sydney, where he advises researchers on diverse aspects of data analysis and teaches R and Python as a Carpentries Instructor.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA