Nextflow Modules
Showing module(s) with keyword "translation"
| Module | Keywords | Description |
|---|---|---|
| nf-core/amulety/translate | immunology BCR TCR translation amino acid nucleotide immunoinformatics | A module to translate BCR and TCR nucleotide sequences into amino acid sequences using amulety and igblast. |
| nf-core/anota2seq/anota2seqrun | riboseq rnaseq translation differential | Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq |
| nf-core/custom/orfnormalise | orf ribo-seq normalisation bed12 translation | Convert one ORF caller's per-sample output table into a unified BED12 plus a sidecar metadata TSV, ready for cross-caller merging. An "ORF caller" is a tool that scans ribosome-profiling (Ribo-seq) data and predicts which open reading frames are being translated. Each caller writes its own table format and uses its own location encoding, classification vocabulary, and confidence score. This module reconciles five callers into one harmonised schema. The `caller` val input selects the parser; supported values: - ribocode (RiboCode predicted ORF table; transcript-coord input, lifted to genomic blocks against the GTF) - ribotish (Ribo-TISH predict output; GenomePos + optional Blocks) - ribotricer (Ribotricer detect-orfs translating ORFs TSV; ORF span parsed from ORF_ID, multi-exon blocks recovered by intersecting with host-transcript exon structure from the GTF) - rpbp (Rp-Bp predicted-orfs BED12 with extra columns) - price (PRICE orfs.tsv; Gedi-style Location field, already genomic) Output BED12 column order: chrom start end name score strand thickStart thickEnd itemRgb blockCount blockSizes blockStarts The BED `name` column carries `<caller>|<caller-native-id>`. The BED `score` column is the caller's native score rescaled to 0-1000 (higher == more confident regardless of native direction). Output sidecar TSV columns: orf_id caller sample_id chrom start end strand gene_id transcript_id orf_class aa_length score Harmonised `orf_class` vocabulary written into the sidecar TSV: - canonical_cds: ORF maps to an annotated CDS (including truncated / extended variants of one). - uORF: upstream ORF (5'UTR-resident). - dORF: downstream ORF (3'UTR-resident). - novel_u: novel / intergenic ORF not assigned to an annotated CDS. - smORF: small ORF (aa_length <= 100); promoted regardless of location-based class so downstream tools can treat smORFs uniformly. - other: internal / overlap / frame variants and anything else. Per-caller mapping notes (lossy collapses): - PRICE `iORF` (internal ORF), `intronic`, and `orphan` map to `other`. Cross-caller catalogue tracking still flags these via `called_by_price`, but the specific PRICE sub-type is not preserved. - Rp-Bp's predicted-orfs BED carries no ORF-type column; this module defaults every Rp-Bp call to `canonical_cds` (the post- selectfinalpredictionset curated set is dominated by canonical CDSs). uORF/dORF/novel calls present in Rp-Bp's separate `.tab.gz` / `extracted-orfs.bed.gz` files are not propagated here. Each caller's native confidence score has a "direction" - some are lower-is-better (p-values), some are higher-is-better (Bayes factors, phase scores): ribocode: min (combined p-value) ribotish: min (combined p-value) ribotricer: max (phase_score) rpbp: max (Bayes factor mean) price: min (p-value) Downstream merging uses this to pick the best per-ORF call. |
| nf-core/gedi/price | riboseq orf price gedi translation | Identify translated ORFs from Ribo-seq BAMs using the PRICE algorithm |
| 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. |