bio-workflows-riboseq-pipeline

name	bio-workflows-riboseq-pipeline
description	End-to-end Ribo-seq analysis from FASTQ through periodicity QC, P-site calibration, ORF detection, translation efficiency, and stalling. Use when orchestrating a full ribosome profiling pipeline and deciding harvest/dedup/alignment options and which downstream analyses the library can support.
workflow	true
depends_on	["ribo-seq/riboseq-preprocessing","ribo-seq/ribosome-periodicity","ribo-seq/orf-detection","ribo-seq/translation-efficiency","ribo-seq/ribosome-stalling","ribo-seq/initiation-site-mapping"]
qc_checkpoints	[{"after_align":"EndToEnd footprint alignment; deduplicate ONLY with UMIs; transcriptome BAM emitted"},{"periodicity_gate":"Strong 3-nt frame-0 periodicity REQUIRED before any ORF/stalling analysis; else gene-level counts only"},{"after_psite":"Per-read-length P-site offsets calibrated (not a single hardcoded 28 / read 5' end)"},{"after_te":"TE via count-based GLM (riborex/Xtail/anota2seq), never a ratio of ratios"}]
origin	openai4s
category	bioskills/workflows
metadata	{"tool_type":"mixed","primary_tool":"STAR","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: cutadapt 4.4+, umi_tools 1.1+, STAR 2.7.11+, bowtie2 2.5.3+, plastid 0.6+, riboWaltz 2.0+, RiboCode 1.2+, riborex 2.4+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

• CLI: <tool> --version then <tool> --help to confirm flags
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Ribo-seq Pipeline

"Analyze my ribosome profiling data from FASTQ to translation efficiency" -> Orchestrate UMI handling, trimming, rRNA depletion, footprint-aware alignment, periodicity QC, P-site calibration, ORF detection, and differential translation, gating each downstream analysis on library quality.

This is a workflow skill: it owns the chaining decisions and hand-offs, not the internals of any one step.

The governing principle

A Ribo-seq analysis is decided at four seams, two of them set at the bench before any sequencing.

1. The harvest method is the deepest commitment and it fixes which analyses are even valid. Cycloheximide (CHX) pre-treatment freezes elongating ribosomes but distorts codon-level dwell times (it can flip the codon-occupancy/tRNA-abundance correlation, Hussmann 2015); dwell-time / stalling / pausing analyses are only valid on flash-frozen, no-drug libraries. Gene-level footprint counts and TE are robust to CHX; codon-resolution analyses are not.
2. Deduplicate ONLY when a UMI is present. Ribosome footprints are ~28-30 nt and massively over-represented at abundant transcripts, so identical reads are mostly real signal; position-deduplicating a non-UMI library destroys it. With UMIs, dedup BOTH the genome and the transcriptome BAM.
3. 3-nt periodicity is a HARD gate, not a QC nicety. Frame-based ORF calling and P-site analyses require strong frame-0 periodicity; a library that lacks it supports only gene-level counts. Certify it (riboWaltz frame-0 fraction) BEFORE any ORF/stalling step.
4. P-site offsets are per-read-length, and TE is a count model. Never hardcode a single 28-nt offset or the read 5' end; calibrate per length. Differential translation efficiency is a count-based GLM (riborex/Xtail/anota2seq) on CDS counts from BOTH assays, never a ratio of ribo/RNA ratios.

Pipeline overview