Course Overview

Course Integrity

This course is compiled automatically on 2021-08-02

The course is tested and available on MacOS, Windows and Ubuntu Linux for R version 4.1.0 (2021-05-18)



Overview

This course introduces ChIPseq analysis in Bioconductor.

The course consists of 4 sections. This walk you through each step of a normal ChIPseq analysis workflow. It covers alignment, QC, peak calling, testing for enrichment in groups of genes, motif enrichment and differential ChIP analysis. Exercises and answer sheets are included after all subsections to practice techniques and provide future reference examples.

Course material and exercises are available to view as rendered HTML at https://rockefelleruniversity.github.io/Intro_To_R_1Day/. All material is available to download under GPL v2 license.



Setting up


System Requirements

Install IGV

IGV can be installed from the BROAD website.

https://www.broadinstitute.org/igv/


Install MACS2

There is no R package for MACS2, but MACS2 is available in the Anaconda package repository for Linux or MacOS. The easiest way to install MACS2 is using the R package Herper. Herper allows you to manage and install Anaconda packages from within R.

BiocManager::install("Herper")
library(Herper)

Once Herper is installed you can install MACS2 with the install_CondaTools function. Behind the scenes, Herper will install the most minimal version of conda (called miniconda), and then will create a new environment into which MACS2 will be installed. When you run the function it prints out where MACS2 is installed.

The env argument is the name you want to give the environment created. The pathToMiniConda specifies the location you want to install Miniconda, and all the conda tools like MACS2.

install_CondaTools(tools="macs2", env="PeakCalling_analysis", pathToMiniConda="/path/to/install")

More information on MACS2 and the new version MACS3 is available from the Tao Liu’s dedicated GitHub page.


Install R

R can be installed from the R-project website.

The R website can be found here http://www.r-project.org/.

The download links and associated installation instructions for multiple platforms can be found below provided by Revolution Analytics. https://cran.revolutionanalytics.com

We recommend installing R 4.1.0 as this is the version used to compile the course. Direct downloads for R 4.1.0 for the main platforms can be found below:


Install RStudio

RStudio can be installed from the RStudio website.

http://www.rstudio.com/

RStudio can be downloaded for all platforms at the link below

https://rstudio.com/products/rstudio/download/


Install required packages

From the course package

install.packages('BiocManager')
BiocManager::install('RockefellerUniversity/RU_ChIPseq',subdir='chip.seq')


From CRAN and Bioconductor

install.packages('BiocManager')
BiocManager::install('methods')
BiocManager::install('ggplot2')
BiocManager::install('rmarkdown')
BiocManager::install('ashr')
BiocManager::install('ChIPQC')
BiocManager::install('DiffBind')
BiocManager::install('ShortRead')
BiocManager::install('DESeq2')
BiocManager::install('limma')
BiocManager::install('BSgenome.Mmusculus.UCSC.mm10')
BiocManager::install('Rsubread')
BiocManager::install('Rbowtie2')
BiocManager::install('R.utils')
BiocManager::install('Rsamtools')
BiocManager::install('rtracklayer')
BiocManager::install('GenomicRanges')
BiocManager::install('TxDb.Mmusculus.UCSC.mm10.knownGene')
BiocManager::install('TFBSTools')
BiocManager::install('org.Mm.eg.db')
BiocManager::install('GenomeInfoDb')
BiocManager::install('ChIPseeker')
BiocManager::install('ggupset')
BiocManager::install('TxDb.Hsapiens.UCSC.hg38.knownGene')
BiocManager::install('GSEABase')
BiocManager::install('TxDb.Hsapiens.UCSC.hg19.knownGene')
BiocManager::install('org.Hs.eg.db')
BiocManager::install('tracktables')
BiocManager::install('goseq')
BiocManager::install('rGREAT')
BiocManager::install('GO.db')
BiocManager::install('JASPAR2020')
BiocManager::install('motifmatchr')
BiocManager::install('clusterProfiler')
BiocManager::install('enrichplot')
BiocManager::install('msigdbr')
BiocManager::install('ggnewscale')
BiocManager::install('knitr')
BiocManager::install('testthat')
BiocManager::install('yaml')


Download the material

Download the material




The Presentations


ChIPseq, Session 1

This section introduces the analysis of ChIPseq data in Bioconductor. Session sections:

  • Preprocessing ChIPseq data in R
  • Alignment of data
  • Creation of bigWig for visualisation

ChIPseq, Session 2

This section covers more in-depth QC of ChIPseq and calling peaks with MACS2. Session sections:

  • Quality control for ChIPseq data in R
  • Overview of peak calling
  • Annotation of peaks

ChIPseq, Session 3

This section introduces the analysis of ChIPseq data in Bioconductor Session sections:

  • Functional enrichment analysis of TF targets
  • R interface to GREAT server
  • Motif enrichment using Meme-ChIP

ChIPseq, Session 4

This section introduces the analysis of ChIPseq data in Bioconductor Session sections:

  • Identification of replicated, high confidence peaks
  • Finding peaks unique and common to conditions
  • Differential ChIP-seq

Getting help


Course help

For advice, help and comments for the material covered in this course please contact us at the issues page associated to this course.

The link to the help pages can be found here


General Bioinformatics support

If you would like contact us about general bioinformatics advice, support or collaboration, please contact us the Bioinformatics Resource Center at .