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.