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Course

ChIP-seq with Bioconductor in R

СреднийУровень мастерства

Обновлено 09.2024

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

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RProbability & Statistics4 ч13 videos46 Exercises3,650 XP5,261Свидетельство о достижениях

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Описание курса

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.

Предварительные требования

Intermediate R Introduction to Bioconductor in R

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.

What is ChIP-seq?

ChIP-seq recap

Sequencing data

ChIP-seq Workflow

Genes that make a difference

ChIP-seq results summary

Understanding ChIP-seq data

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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 data

Reading BAM files

Reading BED files

Taking a closer look at peaks

Plotting a region in detail

Adding Annotations

Cleaning ChIP-seq data

Removing blacklisted regions

Filtering reads

Compare filtered data to raw reads

Assessing enrichment

Computing coverage

Peaks vs background

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3

Comparing ChIP-seq samples

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

Introduction to differential binding

Do these samples look the same to you?

Clustering samples

Visualising differences in protein binding

Testing for differential binding

Loading Read Counts

Setting-up the model

Fitting the model

Revisiting PCA and Heat map

A closer look at differential binding

Volcano plot

Summarising differences in protein binding

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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 peaks

Consolidating peaks

Using Annotations

Annotating peaks

Which peaks are different?

Interpreting Gene lists

Associating peaks with genes

Finding common themes

Understanding the impact on pathways

A closer look at pathways

Advanced ChIP-seq analyses

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ChIP-seq with Bioconductor in R

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