| 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. |
| tool_type | mixed |
| primary_tool | STAR |
Version Compatibility
Reference examples tested with: cutadapt 4.4+, umi_tools 1.1+, STAR 2.7.11+, SortMeRNA 4.3+, 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.
Pipeline overview
FASTQ -> UMI extract -> trim -> rRNA remove -> STAR (EndToEnd) -> dedup
-> periodicity QC + P-site offsets -> [ORF detection | translation efficiency | stalling]
Two upstream facts gate the whole pipeline: how cells were harvested (CHX pre-treatment distorts dwell-time analysis) and whether the library has UMIs (decides deduplication). Periodicity QC is a hard gate: a library without 3-nt periodicity supports only gene-level counts, not ORF/stalling analysis.
Step 1: Preprocess
Goal: Produce a clean, footprint-aware alignment.
Approach: Extract UMIs first (if present), trim with a permissive floor, deplete rRNA before alignment, align end-to-end, and deduplicate only with UMIs. See riboseq-preprocessing for the decision tables.
cutadapt -a CTGTAGGCACCATCAAT --discard-untrimmed -m 15 -M 40 -o trimmed.fq.gz reads.fq.gz
bowtie2 -x contaminant_index -U trimmed.fq.gz --un-gz noncontam.fq.gz -S /dev/null -p 8
STAR --genomeDir STAR_index --readFilesIn noncontam.fq.gz --readFilesCommand zcat \
--alignEndsType EndToEnd --seedSearchStartLmax 15 --outFilterMismatchNmax 2 \
--quantMode TranscriptomeSAM --outSAMtype BAM SortedByCoordinate --outFileNamePrefix ribo_
samtools index ribo_Aligned.sortedByCoord.out.bam
--quantMode TranscriptomeSAM writes a SEPARATE ribo_Aligned.toTranscriptome.out.bam alongside the sorted genome BAM. RiboCode and riboWaltz transcriptome paths consume the TRANSCRIPTOME BAM; the sorted genome BAM is for plastid/genome-coordinate steps. With UMIs, deduplicate the transcriptome BAM too (umi_tools dedup --per-contig; see riboseq-preprocessing), or its ORF/periodicity inputs stay PCR-inflated.
Step 2: Periodicity QC and P-site offsets
Goal: Certify the library and obtain per-length P-site offsets.
Approach: Run riboWaltz to filter periodic read lengths and calibrate offsets; the frame-0 fraction is the pass/fail metric. See ribosome-periodicity.
library(riboWaltz)
annotation <- create_annotation("annotation.gtf")
reads <- bamtolist("bams", annotation = annotation)
reads <- length_filter(reads, length_filter_mode = "periodicity", periodicity_threshold = 50)
offsets <- psite(reads, extremity = "auto")
Either riboWaltz (above) or the plastid metagene generate + psite CLI (used in the example script) is acceptable for offsets; pick one per project.
Step 3: Detect ORFs
Goal: Call translated ORFs once offsets are known.
Approach: Run RiboCode; read lengths come from the metaplots config, and -l is the longest-ORF toggle. See orf-detection.
prepare_transcripts -g annotation.gtf -f genome.fa -o annot
metaplots -a annot -r ribo_Aligned.toTranscriptome.out.bam -o metaplots
RiboCode -a annot -c metaplots_pre_config.txt -A CTG,GTG -l no -p 0.05 -o ribocode_result
Step 4: Translation efficiency
Goal: Test differential translation with matched RNA-seq.
Approach: Count both assays over the CDS and use a count-based GLM; use anota2seq when buffering vs control matters. See translation-efficiency.
library(riborex)
res <- riborex(rnaCntTable = rna_cds_counts, riboCntTable = ribo_cds_counts,
rnaCond = cond, riboCond = cond, engine = "DESeq2")
sig <- res[which(res$padj < 0.05), ]
Step 5: Optional analyses
Stalling/pausing (only on flash-frozen no-drug data; see ribosome-stalling) and initiation-site mapping (needs a harringtonine/LTM library; see initiation-site-mapping) run off the same aligned BAM and calibrated offsets.
Common Errors
| Symptom | Cause | Fix |
|---|
| Downstream analyses all noisy | Periodicity QC skipped | Gate ORF/stalling on the frame-0 fraction first |
| P-site offsets look wrong | Single hardcoded offset across lengths | Calibrate per length with riboWaltz |
| RiboCode uses wrong read lengths | -l passed read lengths | Read lengths come from metaplots; -l is a toggle |
| TE hits dominated by low-count genes | Ratio testing | Use riborex/Xtail/anota2seq count GLMs |
Related Skills
- ribo-seq/riboseq-preprocessing - UMI handling, trimming, rRNA removal, alignment
- ribo-seq/ribosome-periodicity - Periodicity QC and P-site calibration
- ribo-seq/orf-detection - Translated ORF calling
- ribo-seq/translation-efficiency - Differential TE and buffering
- ribo-seq/initiation-site-mapping - Start-codon mapping from TI-seq
- differential-expression/deseq2-basics - Count-based differential testing
References
- McGlincy NJ, Ingolia NT. 2017. Transcriptome-wide measurement of translation by ribosome profiling. Methods 126:112-129. doi:10.1016/j.ymeth.2017.05.028
- Lauria F, Tebaldi T, Bernabò P, Groen EJN, Gillingwater TH, Viero G. 2018. riboWaltz: Optimization of ribosome P-site positioning in ribosome profiling data. PLoS Comput Biol 14(8):e1006169. doi:10.1371/journal.pcbi.1006169
- Xiao Z, Huang R, Xing X, Chen Y, Deng H, Yang X. 2018. De novo annotation and characterization of the translatome with ribosome profiling data. Nucleic Acids Res 46(10):e61. doi:10.1093/nar/gky179
