| 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.
- 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.
- 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-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.
- 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
