Detailed installation instructions

This guide walks you through the minutiae of how to install each prerequisite component. We'll presume you're installing this in a Linux environment. If not the case, you'll need to go to each tool's respective site to find alternative installation approaches and options.

You have several options for installing the software prerequisites: 1) use a container, either a single container or with a multi-container environment manager, in which case you need only either docker or singularity; 2) install via conda or 3) install all prerequisites natively. We'll install everything natively in this guide.

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), pararead for parallel processing sequence reads, refgenie, optionally, to organize, build, and manage reference assemblies, cutadapt to remove adapters, and a handful of python libraries. You can do a user-specific install of required python packages using the included requirements.txt file in the pipeline directory:

pip install --user -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 (pipelines/peppro.yaml) tools section. The following tools are used by the pipeline by default:

We'll install each of these pieces of software before moving forward. Let's create an initial working directory to download and make all of this software.

mkdir tools && cd tools/


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.

tar -zxvf bedtools-2.30.0.tar.gz
rm bedtools-2.30.0.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/tools/bedtools2/bin/"


Next, let's install bowtie2. For more more specific instruction, read the author's installation guide.

cd ../
cd bowtie2-2.4.2/

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

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

Great! On to the next one.


Finally, because PRO-seq treats read1 differently than read2 in paired-end data, we need to re-sync paired-end files after processing. We use fastq_pair to do so efficiently.

cd ../
git clone
cd fastq-pair/
mkdir build
cd build/
cmake3 ..
make install


To obtain a plot to evaluate library quality when we have paired-end reads, we use FLASH to generate a distribution of reads.

cd ../../
tar xvfz FLASH-1.2.11-Linux-x86_64.tar.gz

And let's add FLASH to our PATH environment variable:

export PATH="$PATH:/path/to/tools/FLASH-1.2.11-Linux-x86_64/"


PEPPRO is built using PyPiper and relies upon the PyPiper NGSTK tool kit which itself employs Picard. 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 peppro.yaml file with the full PATH to the picard.jar file.

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


PEPPRO uses samtools, and samtools requires HTSlib internally. So first we'll install HTSlib.

tar xvfj htslib-1.14.tar.bz2
cd htslib-1.14/

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

make install


Next up, samtools.

tar xvfj samtools-1.14.tar.bz2
rm samtools-1.14.tar.bz2
cd samtools-1.14/

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. Otherwise, we will install to the default location.

make install


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

tar xvfj preseq_v2.0.3.tar.bz2
cd preseq/
make all SAMTOOLS_DIR=/path/to/tools/samtools-1.14

Add to PATH!

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


Let's grab seqkit now. Check out the author's installation guide for more instruction if necessary.

cd ../
tar -zxvf seqkit_linux_amd64.tar.gz

And then make sure that executable is in our PATH.

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

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

That should do it!

2. Install R packages

PEPPRO uses R to generate quality control plots. These are technically optional and the pipeline will run without them, but you would not get any QC plots. If you need to but don't have R installed, you can follow these instructions. We'll use and install the necessary packages in this example. Here is the list of required packages:

To install the needed packages, enter the following command in the pipeline folder:

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")'
wget ""
Rscript -e 'install.packages("GenomicDistributionsData_0.0.2.tar.gz", type="source", repos=NULL)'
Rscript -e 'devtools::install(file.path("PEPPROr/"), dependencies=TRUE, repos="")'

To extract files quicker, PEPPRO 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.

tar xvfz pigz-2.7.tar.gz
rm pigz-2.7.tar.gz
cd pigz-2.7/
cd ../

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

export PATH="$PATH:/path/to/tools/pigz-2.7/"

3. Download genomic assets using refgenie

PEPPRO can use refgenie to simplify asset management for alignment, quality control reports, and some outputs. You can initialize a refgenie config file like this:

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

Add the export REFGENIE=genome_config.yaml 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/fasta hg38/bowtie2_index hg38/refgene_anno hg38/ensembl_gtf hg38/ensembl_rb
refgenie build hg38/feat_annotation

PEPPRO also requires bowtie2_index for any pre-alignment genomes:

refgenie pull human_rDNA/fasta human_rDNA/bowtie2_index

That's it! Everything we need to run PEPPRO to its full potential should be installed.

4. Confirm installation

You can confirm the pipeline is now executable natively using the included checkinstall script. This can either be run directly from the peppro/ repository...


or from the web:

curl -sSL | bash