peprs 0.1.0 - Docs.rs

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peprs - A spicy 🌶️ library for managing biological sample metadata to enable reproducible and scalable bioinformatics

Don't let sample metadata parsing bottleneck your pipelines!

About this project

peprs is a rust implementation of the PEP specification and expanded ecosystem. In short, PEP is a framework for managing biological sample metadata. PEP is a community driven effort to create a fast, reliable, reusable, and scalable library for handling biological sample metadata.

PEP and its ecosystem is developed and maintained by the Databio team. As a challenge and learning experience, we have been rewriting the core components of the PEP ecosystem in Rust for performance and reliability.

We are starting with the core PEP specification for metadata management and will expand to include the full ecosystem (looper, pephub-client, pipestat). The core PEP specification is implemented in the peprs-core crate. The Python bindings are implemented in the peprs-py crate.

📦 Modules

peprs-core — Core library implementing the PEP specification. With core module user can create pep objects and do all kind of manipulations.
peprs-eido — Schema-based validation of PEP projects against JSON schemas with eido-specific extensions (imports, tangible file checks).
peprs-cli — Command-line interface with inspect, validate, and convert subcommands.
peprs-py — Python bindings via PyO3. Exposes the Project class with full Polars/Pandas DataFrame interoperability.
pephub-client — Work in progress

⚙️ Installation

Python (recommended)

pip install peprs

Python (from source)

To build and install the Python package from source (requires maturin and Rust toolchain):

git clone https://github.com/pepkit/peprs.git
cd peprs/peprs-py
maturin develop

Rust

Add to your Cargo.toml:

[dependencies]
peprs-core = { git = "https://github.com/pepkit/peprs" }

CLI

Using source

cargo install --path peprs-cli

Using Python

pip install peprs

🐍 Quick Python example

import peprs

# Load a PEP from a YAML config file
project = peprs.Project("path/to/project_config.yaml")
# or
project = peprs.Project.from_pephub("databio/example:default")

# Inspect the project
print(project.name)
print(project.description)
print(len(project))  # number of samples

# Get samples as a Polars DataFrame
df_pl = project.to_polars()
print(df_pl)

# Get samples as a Pandas DataFrame
df_pd = project.to_pandas()
print(df_pd)

# Look up a single sample by name
sample = project.get_sample("3-1_11102016")

# Iterate over samples
for sample in project.samples:
    print(sample)

# Convert projects
project.write_csv("output.csv")
project.write_yaml("output.yaml")
project.write_json("output.json")

Benchmarks

Comparison of peppy (pure Python) vs peprs (Rust bindings). Averaged over 3 runs per sample size.

Initialization Time (seconds)

Library	5	20	100	500	1,000	5,000	10,000	50,000	100,000	600,000
peppy	0.019	0.026	0.096	0.428	0.851	4.226	8.700	44.017	87.613	297.433
peprs	0.003	0.002	0.002	0.003	0.004	0.014	0.036	0.043	0.068	0.339
speedup	7x	15x	50x	149x	196x	306x	244x	1,021x	1,288x	877x

Validation Time (seconds)

Library	5	20	100	500	1,000	5,000	10,000	50,000	100,000	600,000
peppy	0.004	0.006	0.017	0.070	0.166	0.685	1.380	6.928	14.208	84.452
peprs	0.012	0.001	0.002	0.008	0.008	0.038	0.079	0.423	0.794	4.339
speedup	0.4x	9x	10x	9x	20x	18x	17x	16x	18x	19x

🚀 Afterword

We are looking forward to integrating this project with WDL, Snakemake, and Nextflow. All contributions are welcome. Please open an issue or submit a pull request.