How do I create my own PEP? A simple example
To use any PEP-compatible tool, you first need a PEP. A PEP describes a collection of data with its metadata. To create a PEP to represent your dataset, you create 2 files:
- Project config file - a
yamlfile with project settings
- Sample table - a
csvfile with 1 row per sample
In the simplest case, project_config.yaml is just a few lines of yaml, a simple and widely-used hierarchical markup language used to store key-value pairs; you can read more about yaml here. Here's a minimal example project_config.yaml:
pep_version: 2.0.0 sample_table: "path/to/sample_table.csv"
sample_table key points to the second part of a PEP, a comma-separated value (
csv) file annotating samples in the project. Here's a small example of sample_table.csv:
"sample_name", "protocol", "file" "frog_1", "RNA-seq", "frog1.fq.gz" "frog_2", "RNA-seq", "frog2.fq.gz" "frog_3", "RNA-seq", "frog3.fq.gz" "frog_4", "RNA-seq", "frog4.fq.gz"
With those two simple files, you are ready to use the pepkit tools! With a single line of code, you could load this into R using pepr, into python using peppy, or run each sample through an arbitrary command-line pipeline using looper. You can use this formulation to run a workflow written in CWL or using SnakeMake. If you make a habit of describing all your projects like this, you'll never parse another sample annotation sheet again. You'll never write another pipeline submission loop.
This simple example presents a minimal functioning PEP. In practice, there are many advanced features of PEP structure. For instance, you can add additional sections to tailor your project for specific tools. But at its core, PEP is simple and generic; this way, you can start with the basics, and only add more complexity as you need it.
More advanced features are described in the complete PEP specification.