huang-sh/deepscience/bio-workflows-riboseq-pipeline

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.

# 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.

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")   # 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.

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

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