This course is compiled automatically on 2025-06-11
The course is tested and available on MacOS, Windows and
Ubuntu Linux for R version 4.4.0 (2024-04-24)
This course consists of 6 sections, walking participants through the basic concepts in analyzing epigenomic data: Sequencing QC, Alignment, Peak Calling, Technique Specific QC, Differentials, Annotation and Motif Analysis.
Each section is presented as HTMl presentations or single page document. 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/RU_Course_template/.
All material is available to download under GPL v2 license.
Though there is a R package for MACS, we typically use MACS3 from the Anaconda package repository for Linux or MacOS. The easiest way to install MACS3 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 MACS3 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 MACS3 will be installed. When you run the function it prints out where MACS3 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="macs3", env="PeakCalling_analysis", pathToMiniConda="/path/to/install")
More information on MACS3 is available from the Tao Liu’s dedicated GitHub page.
There is no R package for MEME, but MEME is available in the Anaconda package repository for Linux or MacOS. The easiest way to install MEME 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 MEME 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 MEME will be installed. When you run the function it prints out where MEME 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 MEME.
install_CondaTools(tools="meme", env="Motif_analysis", pathToMiniConda="/path/to/install", channels="bioconda")
More information on MEME can be found on the website.
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/
There is no R package for SEACR, but you can download it from the Fred Hutch GitHub page.
You will need to unzip it and then you should be good to go. Here are the commands to do this all in R.
download.file("https://github.com/FredHutch/SEACR/archive/refs/tags/v1.4-beta.2.zip", destfile = "~/Downloads/SEACR_v1.4.zip")
unzip("~/Downloads/SEACR_v1.4.zip", exdir = "~/Downloads/SEACR_v1.4" )
seacr_path <- "~/Downloads/SEACR_v1.4/SEACR-1.4-beta.2/SEACR_1.4.sh"
To test SEACR is running correctly you can run the SEACR help command from R.
system(paste(seacr_path, "-h"))
Some people get an error message at this stage as SEACR does not have permission to run. You can update your permissions like this.
system(paste("chmod 777", seacr_path))
system(paste(seacr_path, "-h"))
More information on SEACR is available on the GitHub page along with the SEACR website (which also includes a web interface for running SEACR).
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')
BiocManager::install('RockefellerUniversity/ATAC.Cut-Run.ChIP',subdir='ATACCutRunChIP')
install.packages('BiocManager')
BiocManager::install('methods')
BiocManager::install('ggplot2')
BiocManager::install('rmarkdown')
BiocManager::install('rio')
BiocManager::install('ShortRead')
BiocManager::install('Rfastp')
BiocManager::install('msigdbr')
BiocManager::install('ggupset')
BiocManager::install('BSgenome.Mmusculus.UCSC.mm10')
BiocManager::install('Rsubread')
BiocManager::install('Rsamtools')
BiocManager::install('rtracklayer')
BiocManager::install('Herper')
BiocManager::install('GenomicAlignments')
BiocManager::install('ChIPQC')
BiocManager::install('ATACseqQC')
BiocManager::install('soGGi')
BiocManager::install('limma')
BiocManager::install('DESeq2')
BiocManager::install('tracktables')
BiocManager::install('TxDb.Mmusculus.UCSC.mm10.knownGene')
BiocManager::install('EnhancedVolcano')
BiocManager::install('clusterProfiler')
BiocManager::install('rGREAT')
BiocManager::install('org.Mm.eg.db')
BiocManager::install('GenomeInfoDb')
BiocManager::install('motifmatchr')
BiocManager::install('memes')
BiocManager::install('ChIPseeker')
BiocManager::install('enrichplot')
BiocManager::install('MotifDb')
BiocManager::install('JASPAR2024')
BiocManager::install('RSQLite')
BiocManager::install('TFBSTools')
BiocManager::install('seqLogo')
BiocManager::install('ggseqlogo')
BiocManager::install('universalmotif')
BiocManager::install('Biostrings')
BiocManager::install('knitr')
BiocManager::install('testthat')
BiocManager::install('yaml')
This section focuses on sequencing QC
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 focuses on alignment and peak calling
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 focuses on technique-specific QC
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 focuses on differentials peak analysis
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 focuses on annotation of peaks and functional enrichment analysis
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 focuses on motif analysis
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.