Nextflow Modules
Showing module(s) with keyword "metagene"
| 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/extractmetageneprofiles | rpbp metagene orf riboseq | Build per-read-length pileups of Ribo-seq read 5'-ends around annotated start codons - the "metagene profile". For each read length, the profile counts how many reads of that length have their 5' end at each position in a window around every annotated start codon, summed across all transcripts. Looking at the profile across the window reveals whether reads of that length show the 3-nucleotide periodicity characteristic of translating ribosomes. This per-length view matters because different ribosome footprint lengths place the ribosomal P-site (the codon being decoded) at different offsets from the read's 5' end, so each length needs its own offset calibration. Output is consumed by `rpbp/estimatemetagenebayesfactors`, which scores each (length, offset) combination for periodicity. |