modules/
rpbp_estimateorfbayesfactors

Score every candidate ORF for evidence of active translation. For each ORF, Rp-Bp fits two competing Bayesian models to its per-codon P-site count vector: a "translated" model that expects P-site density to concentrate at codon-start positions (the in-frame signal a translating ribosome produces), and an "untranslated" / noise model for the same data. The Bayes factor (ratio of marginal likelihoods) quantifies how much the data favour the translated hypothesis.

Emits a BED-style table with one row per ORF carrying genomic coordinates plus the mean and variance of the log Bayes factor across MCMC samples. Downstream, rpbp/selectfinalpredictionset applies Bayes-factor, length and overlap rules to this table to produce the final filtered prediction set.

Uses the Stan models bundled inside the rpbp Python package.

rpbp orf bayes translation riboseq