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Rp-Bp is a Bayesian approach to predict, at base-pair resolution, ribosome occupancy and translation.

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jorgeboucas/rp-bp

 
 

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This project contains scripts for running the Rp-Bp translation prediction pipeline.

Installation

This package is written in python3. It has a number of external dependencies, mostly standard bioinformatics tools. Please see docs/installation.md. (Locally, docs/installation.html may be easier to view.)

Installation is managed through pip3. The required privileges are determined by the installation location of pip3. In particular, if pip3 does not require sudo access, then none of the installation process requires sudo access; this is the case within a virtual environment, for example.

Installation requires running pip:

git clone [email protected]:dieterich-lab/rp-bp.git
cd rp-bp
pip3 install --verbose -r requirements.txt

If possible, we recommend installing inside a virtual environment. We also support running inside anaconda.

Please see docs/installation.md for more detailed installation instructions, including prerequisites and step-by-step details of installing within a virtual machine and anaconda. (Locally, docs/installation.html may be easier to view.)

Example

The package includes a small example using a C. elegans dataset. Please see docs/running-example.md for instructions on running the example. (Locally, docs/running-example.html may be easier to view.)

Usage

Please see docs/usage-instructions.md for more detailed usage instructions. (Locally, docs/usage-instructions.html may be easier to view.)

Uninstallation

The rpbp python packages can also be removed with pip.

pip3 uninstall misc riboutils rpbp

Citation

Malone, B.; Atanassov, I.; Aeschimann, F.; Li, X. & Dieterich, C. Bayesian prediction of RNA translation from ribosome profiling. Nucleic Acids Research, 2017, gkw1350. (Volume and pages have not yet been assigned). The paper is available at NAR.

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Rp-Bp is a Bayesian approach to predict, at base-pair resolution, ribosome occupancy and translation.

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  • Jupyter Notebook 54.1%
  • Python 44.1%
  • Stan 1.8%