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.
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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:
<tool> --version then <tool> --help to confirm flagspackageVersion('<pkg>') then ?function_name to verify parameterspip show <package> then help(module.function) to check signaturesIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"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.
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.
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.
# UMI-extract (if present) -> trim -> rRNA remove -> STAR EndToEnd -> dedup (UMI only)
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.
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") # per-length P-site offsets
Either riboWaltz (above) or the plastid metagene generate + psite CLI (used in the example script) is acceptable for offsets; pick one per project.
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
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), ]
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.
| 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 |
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