Run 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
git clone https://github.com/databio/peppro.git
2. Install bioinformatic tools
Be prepared for this initial installation process to take more than an hour to complete.
peppro/ 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
PEPPRO 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
conda environment. From the
conda activate peppro unset PYTHONPATH python -m pip install --ignore-installed --upgrade -r requirements.txt
4. Install R packages
R to generate quality control and read/peak annotation plots. We have packaged the
R code into a supporting package called PEPPROr. The
PEPPROr package relies on a few additional packages which can be installed to the
To ensure these packages are installed to the
conda environment, make sure to point your
R_LIBS environment variable to the
R library. For example:
conda activate peppro unset R_LIBS export R_LIBS="$CONDA_PREFIX/lib/R/library"
peppro/ 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("PEPPROr/"), dependencies=TRUE, repos="https://cloud.r-project.org/")
5. Get genome assets
refgenie and download assets
pip install refgenie export REFGENIE=genome_config.yaml refgenie init -c $REFGENIE
export REFGENIE line to your
.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 a
bowtie2_index asset for any pre-alignment genomes:
refgenie pull human_rDNA/fasta human_rDNA/bowtie2_index
5b. Download assets manually
If you prefer not to use
refgenie, you can also download assets manually. To realize the full potential of the pipeline, you will need the following:
- a chromosome sizes file: a text file containing "chr" and "size" columns.
- an ensembl_gtf asset used to build other derived assets including a comprehensive TSS annotation and gene body annotation.
- an [ensembl_rb] (http://refgenie.databio.org/en/latest/available_assets/#ensembl_rb) asset containing known genomic features such as promoters and used to produce derived assets such as genomic feature annotations.
- a refgene_anno asset used to produce derived assets including transcription start sites (TSSs), exons, introns, and premature mRNA sequences.
- a genomic feature annotation file
Even if you are not using
refgenie, you can still grab these assets for all required and optional assets from the
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
b769bcf2deaf9d061d94f2007a0e956249905c64653cb5c8 is the digest for "human_rDNA."
From within the
wget -O hg38.fasta.tgz http://refgenomes.databio.org/v3/assets/archive/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4/fasta?tag=default wget -O hg38.bowtie2_index.tgz http://refgenomes.databio.org/v3/assets/archive/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4/bowtie2_index?tag=default wget -O hg38.ensembl_gtf.tgz http://refgenomes.databio.org/v3/assets/archive/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4/ensembl_gtf?tag=default wget -O hg38.ensembl_rb.tgz http://refgenomes.databio.org/v3/assets/archive/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4/ensembl_rb?tag=default wget -O hg38.refgene_anno.tgz http://refgenomes.databio.org/v3/assets/archive/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4/refgene_anno?tag=default wget -O hg38.feat_annotation.gz http://big.databio.org/peppro/hg38_annotations.bed.gz wget -O human_rDNA.fasta.tgz http://refgenomes.databio.org/v3/assets/archive/b769bcf2deaf9d061d94f2007a0e956249905c64653cb5c8/fasta?tag=default wget -O human_rDNA.bowtie2_index.tgz http://refgenomes.databio.org/v3/assets/archive/b769bcf2deaf9d061d94f2007a0e956249905c64653cb5c8/bowtie2_index?tag=default
Then, extract those files:
tar xvf hg38.fasta.tgz tar xvf hg38.bowtie2_index.tgz mv hg38.feat_annotation.gz default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4.annotation.bed.gz tar xvf hg38.refgene_anno.tgz tar xvf hg38.ensembl_rb.tgz tar xvf hg38.ensembl_gtf.tgz tar xvf human_rDNA.fasta.tgz tar xvf human_rDNA.bowtie2_index.tgz
6. Confirm installation
After setting up your environment to run
conda, you can confirm the pipeline is executable with
conda using the included
checkinstall script. This can either be run directly from the
or from the web:
curl -sSL https://raw.githubusercontent.com/databio/peppro/checkinstall | bash
7. Run the sample processing pipeline
Now we can run the pipeline in the
peppro conda environment. The easiest approach is to use
looper, but you can also run the pipeline for a single sample directly at the command line.
7a. Run the pipeline using
PEPPRO can utilize a pipeline submission engine called
looper to run the pipeline across each sample in a project. We can 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.
Run the pipeline with looper and refgenie
looper run examples/meta/peppro_test_refgenie.yaml
Run the pipeline with looper and manual asset specifications
looper run examples/meta/peppro_test.yaml
There are lots of other cool things you can do with
looper, like the dry runs, or 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.
7b. Run the pipeline at the command line
If you are using
refgenie, but running directly at the command-line you need to specify paths to any assets that you pulled above. When the pipeline is run with
looper, you can simply specify human-readable aliases to auto-populate these variables. See the looper refgenie configuration file for an example.
You can grab the path to the minimally required
--genome-index files as follows:
refgenie seek hg38/fasta.chrom_sizes refgenie seek hg38/bowtie2_index.dir
And if you are using pre-alignments, you need the genome index for any pre-alignment genomes,
refgenie seek human_rDNA/bowtie2_index.dir
For the full potential of the pipeline, you'll also need the file paths for the following assets:
You'll need to update the paths to the assets to reflect the results from
refgenie seek. Below is an example where all those assets are local to the
peppro/ repository folder (using
refgenie managed genome assets file paths):
pipelines/peppro.py --single-or-paired single \ --prealignment-index human_rDNA=default/b769bcf2deaf9d061d94f2007a0e956249905c64653cb5c8 \ --genome-index default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4 \ --chrom-sizes default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4.chrom.sizes \ --genome hg38 \ --sample-name test \ --input examples/data/test_r1.fq.gz \ --TSS-name default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_TSS.bed \ --anno-name default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4.annotation.bed.gz \ --pre-name default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_pre-mRNA.bed \ --exon-name default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_exons.bed \ --intron-name default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_introns.bed \ --pi-tss default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_ensembl_TSS.bed \ --pi-body default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_ensembl_gene_body.bed \ -O peppro_test
In the previous example, we used
refgenie assets that we placed in the same location as if we manually downloaded assets to the
peppro/ repository, so the file paths here look the same. From the
peppro/ repository folder (using the manually downloaded genome assets):
pipelines/peppro.py --single-or-paired single \ --prealignment-index human_rDNA=default/b769bcf2deaf9d061d94f2007a0e956249905c64653cb5c8 \ --genome hg38 \ --genome-index default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4 \ --chrom-sizes default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4.chrom.sizes \ --sample-name test \ --input examples/data/test_r1.fq.gz \ --TSS-name default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_TSS.bed \ --anno-name default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4.annotation.bed.gz \ --pre-name default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_pre-mRNA.bed \ --exon-name default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_exons.bed \ --intron-name default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_introns.bed \ --pi-tss default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_ensembl_TSS.bed \ --pi-body default/2230c535660fb4774114bfa966a62f823fdb6d21acf138d4_ensembl_gene_body.bed \ -O peppro_test
looper to run the project level pipeline
PEPPRO also includes a project-level processing pipeline to summarize the pipeline with reports on sample library complexities and count matrices across the samples in the project.
Run the project pipeline with looper and refgenie managed assets
looper runp examples/meta/peppro_test_refgenie.yaml
Run the project pipeline with looper and manual asset specifications
looper runp examples/meta/peppro_test.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.
9. Generate an HTML report using
Looper can generate a pipeline HTML report that makes all our results easy to view and browse. Using the same configuration file we used to run the samples through the pipeline, we'll now employ the
report function of
Generate the HTML report with looper and refgenie managed assets
looper report examples/meta/peppro_test_refgenie.yaml
Generate the HTML report with looper and manual asset specifications
looper report examples/meta/peppro_test.yaml