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 orf_type_native is_smorf `orf_type_native` carries the caller's own ORF-type label verbatim, so every harmonisation decision below stays auditable without re-running the caller. ORF-type tokens are matched exactly (casefolded) against each caller's closed vocabulary, on the part before any colon so that a qualified label such as Ribo-TISH's `Novel:CDSFrameOverlap` matches on `Novel`. Tokens whose location matches no entry cause the process to fail; successful outputs report `unmapped_orf_type=0` on the `# parser_columns:` provenance line. `orf_class` is purely positional: it records where the ORF sits relative to the annotated CDS and never encodes its length. Select small ORFs with the `is_smorf` column (`aa_length` <= `--smorf-max-aa`, default 100). `aa_length` is derived as `(nucleotide_length - 3) // 3` for every caller except Ribo-TISH, whose `AALen` column is taken verbatim. If `AALen` counts the stop codon, `is_smorf` is caller-dependent by one residue exactly at the `--smorf-max-aa` boundary. Not reconciled: it needs a Ribo-TISH run whose nucleotide lengths can be compared against `AALen` directly. 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, not overlapping the CDS. - uoORF: upstream ORF overlapping the CDS out of frame. - dORF: downstream ORF, not overlapping the CDS. - doORF: downstream ORF overlapping the CDS out of frame. - intORF: ORF contained within the CDS, out of frame. - novel_u: novel / intergenic ORF not assigned to an annotated CDS. - other: anything a caller cannot place. Not every caller can report every class, so the absence of a class for one caller is a tool limitation rather than evidence about the ORF: - Ribo-TISH's `5'UTR` covers both uORF and uoORF, and `3'UTR` both dORF and doORF: `tisType()` tests only the start position. - PRICE's vocabulary has no doORF. - ribotricer's `internal` is the terminal fall-through of `check_orf_type()` rather than a positive out-of-frame call, so it maps to `other`; mapping it to intORF would assert a frame relationship ribotricer never tested. Per-caller mapping notes (lossy collapses): - PRICE's `Gene` column concatenates every gene overlapping an ORF's genomic span, so `gene_id` is resolved from the GTF via the ORF's transcript id; the `Gene` column is used only for transcripts absent from the annotation, and can then carry a multi-gene value. - PRICE `iORF` maps to `intORF` and `orphan` to `novel_u` (it is both the not-transcript-consistent label and PRICE's fall-through). `intronic` has no positional equivalent and maps to `other`. The exact PRICE sub-type is preserved in `orf_type_native`. - 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/dotseq/dotseq | riboseq rnaseq translation differential orf | Detect differential ORF usage (DOU) and ORF-level differential translation efficiency (DTE) from Ribo-seq with matched RNA-seq using DOTSeq. Wraps DOTSeqDataSetsFromSummarizeOverlaps() + DOTSeq() + getContrasts() and emits the package's native contrast tables plus plotDOT() visualisations. |
| 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. |