Ribosome profiling with Bayesian predictions (Rp-Bp)
An unsupervised Bayesian approach to predict translated open reading frames (ORFs) from ribosome profiles, using an automatic Bayesian Periodic fragment length and ribosome P-site offset Selection (BPPS).
Read the Docs: or just click here to access the complete documentation.
This package is written in Python3. It has a number of external dependencies, mostly standard bioinformatics tools. Rp-Bp is not published on PyPI, but the installation is easily 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. For detailed instructions, including dependencies/prerequisites and step-by-step details of installing within a virtual environment and anaconda. refer to installation instructrions.
Please see Running the Rp-Bp pipeline step-by-step for more detailed usage instructions. We also provide a number of tools to "post-process" and visualise the results, see QC and downstream analysis of the Rp-Bp results. To get started, the package also includes a small example using a C. elegans dataset. Please see Running Rp-Bp on the example dataset for instructions on running the example.
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.