feat(rpbp): add 7 rpbp modules + fasta_gtf_bam_rpbp subworkflow#11695
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feat(rpbp): add 7 rpbp modules + fasta_gtf_bam_rpbp subworkflow#11695pinin4fjords wants to merge 6 commits into
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…_rpbp subworkflows Adds the Rp-Bp Ribo-seq ORF caller (Malone et al. 2017, doi:10.1093/nar/gkw1141) as 8 split modules plus 2 orchestration subworkflows ported from nf-core/riboseq#174. Splitting per-tool (rather than wrapping rpbp's `predict-translated-orfs` umbrella command) gives independent caching on resume and lets the pipeline's own STAR alignment run instead of being re-done inside rpbp. Modules: - rpbp/buildconfig: render the Rp-Bp YAML config from pipeline-supplied fasta+gtf. - rpbp/preparegenome: build the Rp-Bp genome index (STAR index, ribosomal index, ORF BEDs). - rpbp/extractmetageneprofiles: per-read-length metagene profiles around starts. - rpbp/estimatemetagenebayesfactors: periodicity Bayes factors per read length. - rpbp/selectperiodicoffsets: pick a single P-site offset per high-quality length. - rpbp/extractorfprofiles: per-ORF P-site profile matrix using selected offsets. - rpbp/estimateorfbayesfactors: Bayesian translated-vs-untranslated model. - rpbp/selectfinalpredictionset: filter to the final predicted-ORF BED + FASTAs. Subworkflows: - bam_rpbp_predictorfs: per-sample 6-step chain on a Ribo-seq BAM with the cohort-shared annotation outputs supplied by the caller. - fasta_gtf_bam_rpbp: top-level end-to-end run; renders config, prepares the index once, then runs the per-sample chain. Containers: Wave-built `community.wave.seqera.io/library/rpbp_star:247a8ae84a6babfb` (co-installs `bioconda::rpbp=4.0.1` + `bioconda::star=2.7.11b`) for all rpbp tools; `quay.io/biocontainers/coreutils:9.5` for rpbp/buildconfig (config templating only). Versions are emitted via the topic-channel pattern. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…across the board Bypass `prepare-rpbp-genome` umbrella; call rpbp's internal `get_orfs` function directly via a small inline Python wrapper. Skips the umbrella script's bowtie2-build (rRNA index) and STAR genome-generate steps entirely - none of the downstream Rp-Bp tools we wrap consume those indices, and upstream alignment is supplied as the BAM. rpbp/buildconfig removed - the config dict is now built in-memory inside rpbp/preparegenome from the input fasta + gtf paths. fasta_gtf_bam_rpbp loses the corresponding setup step. extractorfprofiles replicates rpbp's metagene-length filter (`ribo_utils.utils.get_periodic_lengths_and_offsets`) inline before calling extract-orf-profiles, so the upstream select-periodic-offsets output can drive --lengths/--offsets without going through rpbp's filename-driven config plumbing. Filter thresholds are exposed via ext.args2 (4 space-separated tokens, defaults match rpbp.defaults.metagene_options). All 7 inner-module tests now run the upstream chain on real chr20 data (no stub-only tests). Subworkflow tests cover bam_rpbp_predictorfs and fasta_gtf_bam_rpbp end-to-end. The 3 inner modules that need the lower threshold (extractorfprofiles / estimateorfbayesfactors / selectfinalpredictionset) carry tests/nextflow.config setting ext.args2 = '10 1 None 0.0' so the chr20 BAM produces non-empty profiles. Wave container unchanged (community.wave.seqera.io/library/rpbp_star:247a8ae84a6babfb). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
pinin4fjords
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The per-sample 6-step chain was only ever wrapped by fasta_gtf_bam_rpbp; keeping it as a standalone subworkflow added a thin layer no realistic caller needs (rpbp's BED outputs only come from preparegenome). Inlined into fasta_gtf_bam_rpbp — now one subworkflow with 7 process invocations end-to-end. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…m/nf-core/modules into rpbp-add-modules-and-subworkflows
pinin4fjords
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…nnel: column Cleans up the file: - Drops the multi-line header narrative recapping design choices and step ordering. - Drops the numbered "1. ... 2. ... 3. ..." inline narrative blocks. - Aligns the `// channel:` annotations in the emit block (12 of 13 lines were one column off the longest emit's reference position). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Adds 7
rpbp/*modules and thefasta_gtf_bam_rpbpsubworkflow that wraps Rp-Bp's translated-ORF caller chain (Malone et al. 2017, doi:10.1093/nar/gkw1141). Ported from nf-core/riboseq#174.Design — bypass the umbrella
Rp-Bp's
prepare-rpbp-genomebundles three independent steps:bowtie2-build(rRNA index),STAR --runMode genomeGenerate(alignment index), andget_orfs(the BED prep used by all downstream tools). The 6 per-sample tools we wrap don't consume the bowtie or STAR indices — upstream alignment is supplied as the BAM.rpbp/preparegenometherefore invokesget_orfsdirectly via a small Python wrapper, skipping the unused index builds entirely. Real runs take ~3 minutes on chr20 instead of the multi-minute STAR build the umbrella triggers.The per-sample chain similarly avoids
predict-translated-orfsandcreate-orf-profiles, both of which internally call flexbar + bowtie + STAR on raw FASTQs (re-doing the upstream alignment). Splitting the 6 internal steps into separate modules also gives independent caching on resume.Modules
rpbp/preparegenome— chainsgtf-to-bed12→extract-bed-sequences→extract-orf-coordinates→split-bed12-blocks→label-orfsvia the internalget_orfsfunction.rpbp/extractmetageneprofiles— per-read-length metagene profiles around annotated start codons.rpbp/estimatemetagenebayesfactors— Bayes factors comparing periodic vs non-periodic models per read length.rpbp/selectperiodicoffsets— pick one P-site offset per high-quality read length.rpbp/extractorfprofiles— applies the metagene-length filter (replicatingribo_utils.utils.get_periodic_lengths_and_offsets) and runsextract-orf-profiles. Filter thresholds viaext.args2(4 space-separated tokens; defaults mirrorrpbp.defaults.metagene_options).rpbp/estimateorfbayesfactors— Bayesian translated-vs-untranslated model per ORF.rpbp/selectfinalpredictionset— apply BF/length/overlap rules and emit the final predicted-ORF BED, DNA FASTA, protein FASTA.Subworkflow
fasta_gtf_bam_rpbp— end-to-end run:rpbp/preparegenome(once per invocation) followed by the per-sample 6-step chain (extract-metagene → metagene BF → select offsets → extract-orf-profiles → orf BF → select final).Container
All
rpbp/*modules share a Wave-built container co-installingbioconda::rpbp=4.0.1andbioconda::star=2.7.11b:community.wave.seqera.io/library/rpbp_star:247a8ae84a6babfbhttps://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/3a/3a8aa95ce76934f6269b2d8cbdd3d57c13db029c704152975b2315e35b7a2154/dataVersions emitted via
topic: versions.Test plan
Real + stub tests pass for each of the 7 modules and the subworkflow under
nf-core modules test --profile docker/nf-core subworkflows test --profile docker. Test fixtures (chr20) live innf-core/test-datasets:modulesundergenomics/homo_sapiens/riboseq_expression/— same chr20 FASTA / GTF / BAMs the other riboseq modules use.The 3 modules whose chain extends through
extract-orf-profiles(extractorfprofiles,estimateorfbayesfactors,selectfinalpredictionset) and the subworkflow itself carry atests/nextflow.configsettingext.args2 = '10 1 None 0.0'so chr20's modest per-length read counts survive the metagene filter and produce non-empty profiles.Source: nf-core/riboseq#174.