Run PEPATAC in a conda environment.

We also enable setup of the pipeline using conda. As with container-based approaches, some native installation is required for complete setup.

1: Clone the PEPATAC pipeline

git clone https://github.com/databio/pepatac.git

2: Install bioinformatic tools

You will need some common bioinformatics tools installed: bedtools (v2.25.0+), bowtie2 (v2.2.9+), preseq (v2.0+), samblaster (v0.1.24+), samtools (v1.7+), skewer (v0.1.126+), UCSC tools (wigToBigWig, bigWigCat, bedToBigBed), pigz (v2.3.4+).

Optionally, PEPATAC can report on fastq quality (FastQC) and utilize swappable tools for adapter removal (trimmomatic), deduplication (picard), and signal track generation (seqOutBias, bedGraphToBigWig, and bigWigMerge).

Be prepared for this initial installation process to take more than an hour to complete.

From the pepatac/ repository directory:

conda env create -f requirements-conda.yml

Note: The subsequent steps all assume you have installed using conda. Alternatively, you can follow instructions to install each individual program natively.

3. Install python packages

PEPATAC uses several Python packages under the hood. Not all of these are available through conda, so we'll ensure they are installed ourselves to the pepatac conda environment. From the pepatac/ directory:

conda activate pepatac
unset PYTHONPATH
python -m pip install --ignore-installed --upgrade -r requirements.txt

4. Install R packages

PEPATAC uses R to generate quality control and read/peak annotation plots. We have packaged the pepatac specific R code into a supporting package called PEPATACr. The PEPATACr package relies on a few additional packages which can be installed to the conda environment.

To ensure these packages are installed to the pepatac conda environment, make sure to point your R_LIBS environment variable to the conda environment R library. For example:

conda activate pepatac
unset R_LIBS
export R_LIBS="$CONDA_PREFIX/lib/R/library"

From the pepatac/ directory, open R and install the following packages:

install.packages("optigrab")
devtools::install_github("databio/GenomicDistributions")
install.packages("http://big.databio.org/GenomicDistributionsData/GenomicDistributionsData_0.0.2.tar.gz", repos=NULL)
devtools::install(file.path("PEPATACr/"), dependencies=TRUE, repos="https://cloud.r-project.org/")

5: Get genome assets

5a: Initialize refgenie and download assets

PEPATAC can utilize refgenie assets. Because assets are user-dependent, these files must still be available natively. Therefore, we need to install and initialize a refgenie config file.. For example:

pip install refgenie
export REFGENIE=/path/to/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/fasta hg38/bowtie2_index hg38/refgene_anno hg38/ensembl_gtf hg38/ensembl_rb
refgenie build hg38/feat_annotation

PEPATAC also requires a bowtie2_index asset for any pre-alignment genomes:

refgenie pull rCRSd/bowtie2_index

5b: Download assets manually

If you prefer not to use refgenie, you can also download and construct assets manually. The minimum required assets for a genome includes:

Optional assets include:

6. Confirm installation

After setting up your environment to run PEPATAC with conda, you can confirm the pipeline is now executable with conda using the included checkinstall script. This can either be run directly from the pepatac/ repository...

./checkinstall

or from the web:

curl -sSL https://raw.githubusercontent.com/databio/pepatac/checkinstall | bash

7: Use looper to run the sample processing pipeline

Start by running the example project (test_config.yaml) in the examples/test_project/ folder. PEPATAC can utilize a project management tool called looper to run the sample-level pipeline across each sample in a project. Let's use the -d argument to first try a dry run, which will create job scripts for every sample in a project, but will not execute them:

If you are using refgenie, you can grab the path to the --chrom-sizes and --genome-index files as follows:

refgenie seek hg38/fasta.chrom_sizes
refgenie seek hg38/bowtie2_index.dir
refgenie seek rCRSd/bowtie2_index.dir

Alternatively, if you are not using refgenie, you can still grab premade --chrom-sizes and --genome-index files from the refgenie servers. Refgenie uses algorithmically derived genome digests under-the-hood to unambiguously define genomes. That's what you'll see being used in the example below when we manually download these assets. Therefore, 2230c535660fb4774114bfa966a62f823fdb6d21acf138d4 is the digest for the human readable alias, "hg38", and 94e0d21feb576e6af61cd2a798ad30682ef2428bb7eabbb4 is the digest for "rCRSd."

wget -O hg38.fasta.tgz http://rg.databio.org/v3/assets/archive/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4/fasta?tag=default
wget  -O hg38.bowtie2_index.tgz http://rg.databio.org/v3/assets/archive/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4/bowtie2_index?tag=default
wget  -O rCRSd.bowtie2_index.tgz http://refgenomes.databio.org/v3/assets/archive/94e0d21feb576e6af61cd2a798ad30682ef2428bb7eabbb4/bowtie2_index?tag=default

Then, extract these files:

tar xvf hg38.fasta.tgz
tar xvf hg38.bowtie2_index.tgz 
tar xvf rCRSd.bowtie2_index.tgz

From the pepatac/ repository folder (using the manually downloaded genome assets):

looper run -d examples/test_project/test_config.yaml

If that looked good, let's actually run the example by taking out the -d flag:

looper run examples/test_project/test_config.yaml

There are lots of other cool things you can do with looper, like dry runs, report results, check on pipeline run status, clean intermediate files to save disk space, lump multiple samples into one job, and more. For details, consult the looper docs.

8: Use looper to run the project level pipeline

PEPATAC also includes a project-level processing pipeline to do things like:

From the pepatac/ repository folder (using the manually downloaded genome assets):

looper runp examples/test_project/test_config.yaml

This should take < a minute on the test sample and will generate a summary/ directory containing project level output in the parent project directory. In this small example, there won't be a consensus peak set or count table because it is only a single sample. To see more, you can run through the extended tutorial to see this in action.