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https://github.com/dieterich-lab/rp-bp

Rp-Bp is a Bayesian approach to predict, at base-pair resolution, ribosome occupancy and translation.
https://github.com/dieterich-lab/rp-bp

Last synced: 2 months ago
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Rp-Bp is a Bayesian approach to predict, at base-pair resolution, ribosome occupancy and translation.

Host: GitHub
URL: https://github.com/dieterich-lab/rp-bp
Owner: dieterich-lab
License: mit
Created: 2016-04-15T10:44:17.000Z (over 8 years ago)
Default Branch: master
Last Pushed: 2024-04-01T16:27:28.000Z (6 months ago)
Last Synced: 2024-06-30T15:44:46.931Z (3 months ago)
Language: Python
Size: 4.38 MB
Stars: 7
Watchers: 9
Forks: 5
Open Issues: 6
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE

Awesome Lists containing this project

awesome-riboseq - Code - to-end pipeline; Bayesian Periodic fragment length and ribosome P-site offset Selection | (ORF Calling)

README

# 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.

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## 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