This course is compiled automatically on 2025-06-05
The course is tested and available on MacOS, Windows and
Ubuntu Linux for R version 4.4.0 (2024-04-24)
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.
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.
R can be installed from the R-project website.
The R website can be found here http://www.r-project.org/. This website has all the latest information about R updates, conferences and installation
You can use this direct links to the install for each major OS:
RStudio can be installed from the RStudio website.
RStudio can be downloaded for all platforms at the link below
https://rstudio.com/products/rstudio/download/
R Packages can be installed from the course package or from CRAN/Bioconductor. These commands should be written into the R console. Once R and RStudio is installed, you can copy and paste these install commands into lower left pane of RStudio which should be labelled “Console”. If you run into any errors, do this one line at a time.
install.packages('BiocManager')
install.packages('remotes')
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')
This section introduces the analysis of ChIPseq data in Bioconductor. Session sections:
The html slide presentation can be found at this link Slide
The single page html presentation can be found at this link Single Page
The code use in the presentations can be found at R code
This section covers more in-depth QC of ChIPseq and calling peaks with MACS2. Session sections:
The html slide presentation can be found at this link Slide
The single page html presentation can be found at this link Single Page
The code use in the presentations can be found at R code
This section introduces the analysis of ChIPseq data in Bioconductor Session sections:
The html slide presentation can be found at this link Slide
The single page html presentation can be found at this link Single Page
The code use in the presentations can be found at R code
This section introduces the analysis of ChIPseq data in Bioconductor Session sections:
The html slide presentation can be found at this link Slide
The single page html presentation can be found at this link Single Page
The code use in the presentations can be found at R code
This section introduces the analysis of CutAndRun data in Bioconductor Session sections:
The html slide presentation can be found at this link Slide
The single page html presentation can be found at this link Single Page
The code use in the presentations can be found at R code
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
If you would like contact us about general bioinformatics advice, support or collaboration, please contact us the Bioinformatics Resource Center at brc@rockefeller.edu.