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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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.
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  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.
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  3. 3

    Comparing ChIP-seq samples

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