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ChIP-seq with Bioconductor in R

Intermediate
4.8+
12 reviews
Updated 05/2025
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 hours13 videos46 Exercises3,650 XP4,884Statement of Accomplishment

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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.

Prerequisites

Intermediate RIntroduction to Bioconductor in R
1

Introduction to ChIP-seq

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2

Back to Basics - Preparing ChIP-seq data

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3

Comparing ChIP-seq samples

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4

From Peaks to Genes to Function

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ChIP-seq with Bioconductor in R
Course
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*4.8
from 12 reviews
83%
17%
0%
0%
0%
  • Sungmin
    10 days

  • Nasibeh
    11 days

  • Jalyn
    28 days

  • Bertrand-David
    about 1 month

  • Siddhartha
    about 1 month

  • Scott
    about 2 months

    There may be too much assumed knowledge in some of the steps in the examples. At the same time, much of it can feel overly cookie-cutter. There are many times when functions are called to in the examples without explaining the parameters that are chosen, why they're chosen. alternate options and use-cases. This might make the course much longer, but it could be done relatively simply with footnotes (e.g.). Many of these tools seem like they would be out of date at this point, it might be worth while to mention other commonly used pipelines or to update the course at some point.

Sungmin

Nasibeh

Jalyn

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