Detailed installation instructions

This guide walks you through the nitty-gritty of how to install each prerequisite package.

1. Install required software

Python packages. The pipeline uses pypiper to run a single sample, looper to handle multi-sample projects (for either local or cluster computation), and pararead for parallel processing sequence reads. For peak calling, the pipeline uses MACS2 as the default. You can do a user-specific install using the included requirements.txt file in the pipeline directory:

pip install --user --upgrade -r requirements.txt

Required executables. We will need some common bioinformatics tools installed. The complete list (including optional tools) is specified in the pipeline configuration file tools section. The following tools are used by the pipeline:


We'll install each of these pieces of software before moving forward. Let's start right at the beginning and install bedtools. We're going to install from source, but if you would prefer to install from a package manager, you can follow the instructions in the bedtools' installation guide.

cd tools/
tar -zxvf bedtools-2.29.0.tar.gz
rm bedtools-2.29.0.tar.gz
cd bedtools2

Now, let's add bedtools to our PATH environment variable. Look here to learn more about the concept of environment variables if you are unfamiliar.

export PATH="$PATH:/path/to/pepatac_tutorial/tools/bedtools2/bin/"


Next, let's install bowtie2.

cd ../
cd bowtie2-2.4.1

Again, let's add bowtie2 to our PATH environment variable:

export PATH="$PATH:/path/to/pepatac_tutorial/tools/bowtie2-2.4.1/"


The pipeline uses preseq to calculate library complexity. Check out the author's page for more instruction.

tar xvfj preseq_linux_v2.0.tar.bz2

Add to PATH!

export PATH="$PATH:/path/to/peppro_tutorial/tools/preseq_v2.0/"


Now we'll get samblaster. For a full guide, check out the samblaster installation instructions.

git clone git://
cd samblaster/
export PATH="$PATH:/path/to/pepatac_tutorial/tools/samblaster/"


Next up, samtools.

tar xvfj samtools-1.10.tar.bz2
rm samtools-1.10.tar.bz2
cd samtools-1.10

Alternatively, if you do not have the ability to install samtools to the default location, you can specify using the --prefix=/install/destination/dir/ option. Learn more about the --prefix option here.

make install

As for our other tools, add samtools to our PATH environment variable:

export PATH="$PATH:/path/to/pepatac_tutorial/tools/samtools-1.10/"


Time to add skewer to the collection.

cd ../
mv skewer-0.2.2-linux-x86_64 skewer
chmod 755 skewer

UCSC utilities

Finally, we need a few of the UCSC utilities. You can install the entire set of tools should you choose, but here we'll just grab the subset that we need.

chmod 755 wigToBigWig
chmod 755 bigWigCat
chmod 755 bedToBigBed

Add our tools/ directory to our PATH environment variable.

export PATH="$PATH:/path/to/pepatac_tutorial/tools/"

That should do it! Now we'll install some optional packages. Of course, these are not required, but for the purposes of this tutorial we're going to be completionists.

2. Install optional software

PEPATAC uses R to generate quality control and read/peak annotation plots, so you'll need to have R functional if you want these outputs. We have packaged all the R code into a supporting package called PEPATACr. The PEPATAC package relies on a few additional packages which can be installed at the command line as follows:

Rscript -e 'install.packages("devtools")'
Rscript -e 'devtools::install_github("pepkit/pepr")'
Rscript -e 'install.packages("BiocManager")'
Rscript -e 'BiocManager::install("GenomicRanges")'
Rscript -e 'devtools::install_github("databio/GenomicDistributions")'
Rscript -e 'BiocManager::install(c("BSgenome", "GenomicFeatures", "ensembldb"))'
Rscript -e 'install.packages("", repos=NULL)'

Then, install the PEPATAC package. From the pepatac/ directory:

Rscript -e 'devtools::install(file.path("PEPATACr/"), dependencies=TRUE, repos="")'

Optionally, PEPATAC can mix and match tools for adapter removal, deduplication, and signal track generation. FastQC, if present, will be automatically run on input fastq files. seqOutBias can be used with the --sob argument to take into account mappability at a given read length, the Tn5 sequence bias, and to scale the sample signal tracks by the expected over observed cut frequency.

Optional tools:


You will need to have java installed to use FastQC. At the command prompt, you can type java -version, press enter, and if you don't see an error you should be alright. You'll need a version greater than 1.6 to work with FastQC. Read more from the FastQC installation instructions.

cd /path/to/pepatac_tutorial/tools/

We also need to make the FastQC wrapper executable. To learn more about this, check out this introduction to chmod.

chmod 755 FastQC/fastqc

Add FastQC to our PATH environment variable:

export PATH="$PATH:/path/to/pepatac_tutorial/tools/FastQC/"


PEPATAC can alternatively use picard MarkDuplicates for duplicate identification and removal. Read the picard installation guide for more assistance.

chmod +x picard.jar

Create an environmental variable pointing to the picard.jar file called $PICARD. Alternatively, update the pepatac.yaml file with the full PATH to the picard.jar file.

export PICARD="/path/to/peppro_tutorial/tools/picard.jar"


To extract files quicker, PEPATAC can also utilize pigz in place of gzip if you have it installed. Let's go ahead and do that now. It's not required, but it can help speed everything up when you have many samples to process.

cd /path/to/pepatac_tutorial/tools/
tar xvfz pigz-2.4.tar.gz
rm pigz-2.4.tar.gz
cd pigz-2.4/

Don't forget to add this to your PATH too!

export PATH="$PATH:/path/to/pepatac_tutorial/tools/pigz-2.4/"

That's it! Everything we need to run PEPATAC to its full potential should be installed. If you are interested and have experience using containers, you can check out the alternate installation methods.

3. Create environment variables

We also need to create some environment variables to help point looper to where we keep our data files and our tools. You may either set the environment variables up, like we're going to do now, or you may simply hard code the necessary locations in our configuration files. First, let's create a PROCESSED variable that represents the location where we want to save output.

export PROCESSED="/path/to/pepatac_tutorial/processed/"

Second, we'll create a variable representing the root path to all our tools named CODEBASE.

export CODEBASE="/path/to/pepatac_tutorial/tools/"

(Add these environment variables to your .bashrc or .profile so you don't have to always do this step).
Fantastic! Now that we have the pipeline and its requirements installed, we're ready to get our reference genome(s).

4. Download a reference genome

Before we analyze anything, we also need a reference genome. PEPATAC uses refgenie assets for alignment. If you haven't already, initialize a refgenie config file like this:

pip install --user refgenie
export REFGENIE=your_genome_folder/genome_config.yaml
refgenie init -c $REFGENIE

Add the export REFGENIE line to your .bashrc or .profile to ensure it persists.

Next, pull the assets you need. Replace hg38 in the example below if you need to use a different genome assembly. If these assets are not available automatically for your genome of interest, then you'll need to build them. Download these required assets with this command:

refgenie pull hg38/bowtie2_index refgene_anno feat_annotation

PEPATAC also requires bowtie2_index for any pre-alignment genomes:

refgenie pull rCRSd/bowtie2_index
refgenie pull human_repeats/bowtie2_index