# Ribosome profiling with Bayesian predictions (Rp-Bp)
Ribosome profiling (Ribo-seq) is an RNA-sequencing-based readout of RNA translation. Isolation and deep-sequencing of ribosome-protected RNA fragments (ribosome footprints) provides a genome-wide snapshot of the translatome at sub-codon resolution. **Rp-Bp** is an unsupervised Bayesian approach to predict translated open reading frames (ORFs) from ribosome profiles. **Rp-Bp** can be used for ORF discovery, or simply to estimate periodicity in a set of Ribo-seq samples. When used for ORF discovery, **Rp-Bp** automatically classifies ORFs into different biotypes or categories, relative to their host transcript.
**Rp-Bp** comes with two _interactive dashboards_ or _web applications_, one for read and periodicity quality control, the other to facilitate Ribo-seq ORFs discovery.
<p align="center">
<a href="https://rp-bp.readthedocs.io/en/latest/"><img alt="Rp-Bp" src="https://github.com/dieterich-lab/rp-bp/raw/master/docs/source/_static/logo-rpbp-dark.png"></a>
</p>
<p align="center">
<a href="http://bioconda.github.io/recipes/rpbp/README.html"><img alt="Install with bioconda" src="https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat"></a>
<a href="https://pypi.org/project/rpbp/"><img alt="PyPI" src="https://img.shields.io/pypi/v/rpbp"></a>
<a href="https://github.com/dieterich-lab/rp-bp/actions/workflows/ci.yml"><img alt="CI" src="https://github.com/dieterich-lab/rp-bp/actions/workflows/ci.yml/badge.svg"></a>
<a href="https://rp-bp.readthedocs.io/en/latest/?badge=latest"><img alt="Docs" src="https://readthedocs.org/projects/rp-bp/badge/?version=latest"></a>
</p>
---
## Documentation
Consult the [user guide](http://rp-bp.readthedocs.io/en/latest/) for instructions on how to install the package, or to use Docker/Singularity containers with the package pre-installed. Detailed usage instructions and tutorials are available.
## How to report issues
For bugs, issues, or feature requests, use the [bug tracker](https://github.com/dieterich-lab/rp-bp/issues). Follow the instructions and guidelines given in the templates.
## How to cite
Brandon Malone, Ilian Atanassov, Florian Aeschimann, Xinping Li, Helge Großhans, Christoph Dieterich. [Bayesian prediction of RNA translation from ribosome profiling](https://doi.org/10.1093/nar/gkw1350), _Nucleic Acids Research_, Volume 45, Issue 6, 7 April 2017, Pages 2960-2972.
## License
The MIT License (MIT). Copyright (c) 2016 dieterich-lab.
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