nf-core pipeline ·
nf-core/riboseq
A Nextflow pipeline for processing ribosome profiling data, predicting translated ORFs, and evaluating translational efficiency.
What nf-core/riboseq is
nf-core/riboseq is a bioinformatics analysis pipeline designed for ribosome profiling (Ribo-seq, or ribosome footprinting) and translation initiation site sequencing (TI-seq). Ribosome footprinting assays sequence RNA fragments protected by translating ribosomes to monitor protein translation genome-wide. The pipeline handles raw sequencing reads through specialised post-alignment translation metrics.
Originally written by Jonathan Manning with contributions from researchers across academia and industry, the pipeline builds upon preprocessing procedures familiar from RNA-seq workflows while incorporating domain-specific tools for ribosome profiling. It enables researchers to identify active open reading frames (ORFs), quantify in-frame P-sites, and investigate transcriptional versus translational regulation.
What the pipeline does
Preprocessing begins by optionally merging re-sequenced FASTQ files, sub-sampling to infer strandedness, conducting quality control, extracting unique molecular identifiers (UMIs), and trimming adapters using Trim Galore! or fastp. After optional read-length matching for paired RNA-seq data, reads are screened for contaminants with BBSplit and depleted of ribosomal RNA via SortMeRNA, Bowtie2, or RiboDetector. STAR aligns remaining reads to both the genome and transcriptome, followed by SAMtools sorting, UMI deduplication (via UMI-tools or UMICollapse), and optional bigWig coverage generation.
Specialist downstream analysis calculates P-site offsets and periodicity using tools such as Ribo-TISH, riboWaltz, and plastid. The pipeline predicts translated ORFs and initiation sites de novo using Ribo-TISH and RiboCode by default, with opt-in callers including Ribotricer, PRICE, and Rp-Bp. It can assemble novel transcripts using StringTie, cluster candidate small ORFs (smORFs) into a consensus catalogue with MMseqs2, quantify ORF-level P-sites, and evaluate translational efficiency against matched RNA-seq data via anota2seq, deltaTE, or DOTSeq.
Key facts
- Pipeline framework
- Nextflow
- Latest release
- 1.2.0 (2025-12-03)
- Original author
- Jonathan Manning
- Input formats
- FASTQ (single-end or paired-end) via samplesheet CSV
- Supported data types
- Ribo-seq, TI-seq, and matched RNA-seq
- Alignment tool
- STAR
- Primary ORF callers
- Ribo-TISH, RiboCode (default); Ribotricer, PRICE, Rp-Bp (opt-in)
- Translational efficiency tools
- anota2seq, deltaTE (DESeq2), DOTSeq
What you can use it for
- De novo open reading frame discovery
- Identify translated canonical and non-canonical ORFs from ribosome footprint density and P-site periodicity across genomes using Ribo-TISH, RiboCode, PRICE, or Rp-Bp.
- Translational efficiency analysis
- Compare matched Ribo-seq and RNA-seq datasets across experimental conditions to decouple translational changes from transcriptional regulation using anota2seq or deltaTE.
- Novel transcript and smORF cataloguing
- Combine reference-guided transcript assembly via StringTie with multi-caller ORF prediction to build consensus catalogues of small translated peptides collapsed by MMseqs2.
- Ribosome footprint quality control and P-site mapping
- Inspect triplet periodicity, assess reading frame bias around annotated coding sequences, and determine read length-dependent P-site offsets using riboWaltz and plastid.
Run it here
The research agent runs nf-core/riboseq with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Supports multiple ribosome profiling-specific ORF callers including Ribo-TISH, RiboCode, PRICE, and Rp-Bp, with cross-caller consensus aggregation via MMseqs2.
- Incorporates complete end-to-end processing from raw FASTQ quality control, UMI deduplication, and rRNA depletion to final translational efficiency testing.
- Accommodates matched RNA-seq and Ribo-seq paired designs with built-in read-length equalisation and contrast testing.
Watch out for
- Ribotricer is disabled by default because benchmarking revealed its ORF-score metric is rank-unstable across biological replicates.
- Translational efficiency analyses require matched RNA-seq data alongside Ribo-seq datasets.
- Parameter overrides cannot be supplied via Nextflow custom configuration files using the -c flag, requiring CLI flags or parameter files instead.
Questions
Can I run nf-core/riboseq without my own cluster?
The research agent runs nf-core/riboseq with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/riboseq under?
MIT, like every nf-core pipeline.
What sample types can be supplied in the input samplesheet?
The samplesheet accepts single-end or paired-end FASTQ files designated as riboseq for regular ribosome profiling, tiseq for translation initiation site profiling, or rnaseq for matched RNA sequencing data.
Which methods are available for ribosomal RNA depletion?
Users can specify SortMeRNA (the default), Bowtie2, or RiboDetector to identify and eliminate ribosomal RNA reads prior to genome alignment.
How are P-sites identified and quantified?
The pipeline derives P-sites and quality control metrics from transcriptome alignments using riboWaltz and plastid. Plastid P-site estimates serve as the default input for gene- and ORF-level quantification.
Why is Ribotricer not included in the default ORF calling pipeline?
Ribotricer is opt-in because benchmarking indicated its ORF-score column lacks rank stability across biological replicates, leading to its exclusion from default caller sets and rank aggregation.
How to cite it
10.5281/zenodo.10966364
Written from nf-core/riboseq's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.