Course Overview
Course Integrity
This course is compiled automatically on 2021-07-28
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 ATACseq analysis in Bioconductor.
The course consists of 2 sections. This walk you through each step of a normal ATACseq analysis workflow. It covers alignment, QC, peak calling, testing for enrichment in groups of genes, motif enrichment and testing for differential accessibility. 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_ATACseq/. All material is available to download under GPL v2 license.
Setting up
System Requirements
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.
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_ATACseq',subdir='atacseq')From CRAN and Bioconductor
install.packages('BiocManager')
BiocManager::install('methods')
BiocManager::install('ggplot2')
BiocManager::install('rmarkdown')
BiocManager::install('ShortRead')
BiocManager::install('ashr')
BiocManager::install('ChIPQC')
BiocManager::install('DiffBind')
BiocManager::install('BSgenome.Hsapiens.UCSC.hg19')
BiocManager::install('Rsubread')
BiocManager::install('Rbowtie2')
BiocManager::install('R.utils')
BiocManager::install('Rsamtools')
BiocManager::install('BSgenome.Hsapiens.UCSC.hg38')
BiocManager::install('rtracklayer')
BiocManager::install('ChIPseeker')
BiocManager::install('soGGi')
BiocManager::install('GenomicAlignments')
BiocManager::install('TxDb.Hsapiens.UCSC.hg19.knownGene')
BiocManager::install('DESeq2')
BiocManager::install('BSgenome.Mmusculus.UCSC.mm10')
BiocManager::install('TxDb.Hsapiens.UCSC.hg38.knownGene')
BiocManager::install('tracktables')
BiocManager::install('clusterProfiler')
BiocManager::install('TxDb.Mmusculus.UCSC.mm10.knownGene')
BiocManager::install('devtools')
BiocManager::install('tidyr')
BiocManager::install('DT')
BiocManager::install('dplyr')
BiocManager::install('rGREAT')
BiocManager::install('MotifDb')
BiocManager::install('Biostrings')
BiocManager::install('GenomicRanges')
BiocManager::install('pheatmap')
BiocManager::install('universalmotif')
BiocManager::install('seqLogo')
BiocManager::install('org.Mm.eg.db')
BiocManager::install('ATACseqQC')
BiocManager::install('JASPAR2020')
BiocManager::install('motifmatchr')
BiocManager::install('chromVAR')
BiocManager::install('ggseqlogo')
BiocManager::install('TFBSTools')
BiocManager::install('motifStack')
BiocManager::install('knitr')
BiocManager::install('testthat')
BiocManager::install('yaml')The Presentations
ATACseq, Session 1
This section introduces the analysis of ATACseq data in Bioconductor Session sections:
- Preprocessing ATACseq data in R
- Alignment of data
- Creation of bigWig for visualisation
Links to presentations:
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
ATACseq, Session 2
This section demonstrates how to assess global changes in accesibility with ATACseq data. Session sections:
- Annotating ATACseq data in R
- Plotting nucleosome free and mono-nucleosome signal
- Plotting cut-sites around DNA binding proteins
Links to presentations:
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
ATACseq, Session 3
This section demonstrates how to assess motifs within ATAC-seq data. Session sections:
- Retrieving motifs from database.
- Plotting motifs.
- Identifying known motifs.
Links to presentations:
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
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 brc@rockefeller.edu.