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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.

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4 Hours13 Videos46 Exercises3,057 Learners
3650 XP

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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

    Free

    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. 2

    Back to Basics - Preparing ChIP-seq data

    Now the ChIP-seq analysis begins in earnest. This chapter introduces Bioconductor tools to import and clean the data.

    Play Chapter Now
    Importing data
    50 xp
    Reading BAM files
    100 xp
    Reading BED files
    100 xp
    Taking a closer look at peaks
    50 xp
    Plotting a region in detail
    100 xp
    Adding Annotations
    50 xp
    Cleaning ChIP-seq data
    50 xp
    Removing blacklisted regions
    100 xp
    Filtering reads
    100 xp
    Compare filtered data to raw reads
    50 xp
    Assessing enrichment
    50 xp
    Computing coverage
    100 xp
    Peaks vs background
    100 xp
  3. 3

    Comparing ChIP-seq samples

    This chapter introduces techniques to identify and visualise differences between ChIP-seq samples.

    Play Chapter Now
    Introduction to differential binding
    50 xp
    Do these samples look the same to you?
    50 xp
    Clustering samples
    100 xp
    Visualising differences in protein binding
    100 xp
    Testing for differential binding
    50 xp
    Loading Read Counts
    50 xp
    Setting-up the model
    100 xp
    Fitting the model
    100 xp
    Revisiting PCA and Heat map
    100 xp
    A closer look at differential binding
    50 xp
    MA plot
    100 xp
    Volcano plot
    100 xp
    Summarising differences in protein binding
    100 xp
  4. 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
    Interpreting ChIP-seq peaks
    50 xp
    Consolidating peaks
    100 xp
    Using Annotations
    100 xp
    Annotating peaks
    100 xp
    Which peaks are different?
    100 xp
    Interpreting Gene lists
    50 xp
    Associating peaks with genes
    100 xp
    Finding common themes
    100 xp
    Understanding the impact on pathways
    100 xp
    A closer look at pathways
    100 xp
    Advanced ChIP-seq analyses
    50 xp

In the following tracks

Analyzing Genomic Data

Datasets

Androgen Receptor ChIP-seq Peaks datasetChromosome 20 dataset

Collaborators

David CamposRichie CottonShon Inouye

Prerequisites

Intermediate RIntroduction to Bioconductor in R
Peter Humburg Headshot

Peter Humburg

Statistician

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.
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