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
Introduction to ChIP-seq
FreeIntroduction 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.
- 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.
Importing data50 xpReading BAM files100 xpReading BED files100 xpTaking a closer look at peaks50 xpPlotting a region in detail100 xpAdding Annotations50 xpCleaning ChIP-seq data50 xpRemoving blacklisted regions100 xpFiltering reads100 xpCompare filtered data to raw reads50 xpAssessing enrichment50 xpComputing coverage100 xpPeaks vs background100 xp - 3
Comparing ChIP-seq samples
This chapter introduces techniques to identify and visualise differences between ChIP-seq samples.
Introduction to differential binding50 xpDo these samples look the same to you?50 xpClustering samples100 xpVisualising differences in protein binding100 xpTesting for differential binding50 xpLoading Read Counts50 xpSetting-up the model100 xpFitting the model100 xpRevisiting PCA and Heat map100 xpA closer look at differential binding50 xpMA plot100 xpVolcano plot100 xpSummarising differences in protein binding100 xp - 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.
Interpreting ChIP-seq peaks50 xpConsolidating peaks100 xpUsing Annotations100 xpAnnotating peaks100 xpWhich peaks are different?100 xpInterpreting Gene lists50 xpAssociating peaks with genes100 xpFinding common themes100 xpUnderstanding the impact on pathways100 xpA closer look at pathways100 xpAdvanced ChIP-seq analyses50 xp
In the following tracks
Analyzing Genomic Data in RCollaborators
Peter Humburg
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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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