Nextflow Modules
Showing module(s) with keyword "bayes"
| Module | Keywords | Description |
|---|---|---|
| nf-core/rpbp/estimatemetagenebayesfactors | rpbp metagene bayes orf riboseq | Score how strongly each per-read-length metagene profile shows the 3-nucleotide periodicity expected of actively translating ribosomes. For each candidate (read length, P-site offset) pair, Rp-Bp fits two competing Bayesian models to the count window around annotated start codons: a "periodic" model whose signal repeats every three nucleotides, and a "non-periodic" background model. The Bayes factor (ratio of the two marginal likelihoods) quantifies how much the data prefer the periodic explanation. Returns one row per (length, offset) pair with the mean and variance of the log Bayes factor across MCMC samples. Downstream, `rpbp/selectperiodicoffsets` picks the best offset per length from this table, and `rpbp/getperiodiclengthsoffsets` filters to the high-confidence pairs that drive ORF-level scoring. Uses the Stan models bundled inside the rpbp Python package. |
| nf-core/rpbp/estimateorfbayesfactors | rpbp orf bayes translation riboseq | 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. |
| nf-core/rpbp/selectfinalpredictionset | rpbp orf bayes prediction riboseq | Produce the final filtered set of predicted translated ORFs from the per-ORF Bayes factor table. Applies the standard Rp-Bp prediction rules: a minimum Bayes-factor cutoff (favouring translated over untranslated), a minimum ORF length, and overlap resolution so that among overlapping candidates only the highest-scoring representative is kept. Emits three files describing the same prediction set: a BED of ORF genomic coordinates plus score, a FASTA of ORF DNA sequences (extracted from the genome FASTA), and a FASTA of the corresponding translated protein sequences. This is the terminal step of the Rp-Bp per-sample chain. |