nf-core pipeline · dms, map, rna-structure
nf-core/rnastructurome
Analyses SHAPE and DMS chemical RNA structure-probing data to quantify reactivity and predict secondary structures.
What nf-core/rnastructurome is
nf-core/rnastructurome is a bioinformatics workflow designed to process high-throughput chemical RNA structure-probing sequencing data. Originally developed by Victoria Begley and Pedro Madrigal from RNAcentral at EMBL-EBI, it handles data generated from SHAPE (selective 2'-hydroxyl acylation analysed by primer extension) or DMS (dimethyl sulphate) experiments. These assays resolve RNA secondary structure at single-nucleotide resolution using either reverse transcription stopping (RT-stop) or mutational profiling (MaP) readout strategies.
The pipeline coordinates read quality control, trimming, mapping, duplicate handling, per-base reactivity quantification, and control normalisation. It supports genome and transcriptome alignments, capable of sourcing reference sequences locally or fetching them automatically from Ensembl or NCBI for organisms such as viruses and bacteria.
Normalised reactivities are utilised to fold RNA secondary structures, evaluate cross-replicate reproducibility, and export standardised files. Outputs include reactivity tracks, Shannon entropy tracks, two-dimensional structure diagrams, and RMDB-compatible RDAT files for downstream investigation and public data deposition.
What the pipeline does
The workflow starts by merging re-sequenced FASTQ files, checking quality with FastQC, extracting optional unique molecular identifiers (UMIs) with UMI-tools, and trimming adapters with Cutadapt using chemistry-aware settings. Reads are aligned against a reference genome using STAR by default, or against transcript sequences using Bowtie for RT-stop data or Bowtie2 for MaP data. Deduplication is handled by UMI-tools when UMIs are provided, whereas coordinate-based duplicate removal is disabled by default to avoid discarding independent molecules with identical 5' cleavage or modification ends.
Per-nucleotide reactivity is tabulated using RNA Framework tools: rf-count processes transcript alignments directly, or rf-count-genome tallies genome coordinates with strandedness inferred by RSeQC before extracting transcript reactivities via rf-rctools. rf-norm normalises reactivities by pairing treated samples with untreated or denatured controls. Secondary structures are modelled using rf-fold across grouped replicates, yielding base-pair probabilities and Shannon entropy tracks. Final visualisations and files are produced via ViennaRNA RNAplot, containerised R2DT template matching, and custom export to RDAT format, with run statistics compiled into a MultiQC report.
Key facts
- Input formats
- FASTQ files, samplesheet (CSV), optional FASTA and GTF reference files
- Supported probing chemistries
- SHAPE, DMS
- Supported readout principles
- RT-stop, mutational profiling (MaP)
- Alignment engines
- STAR (genome route), Bowtie and Bowtie2 (transcriptome route)
- Core quantification framework
- RNA Framework (rf-count, rf-norm, rf-fold)
- Structure visualisation
- ViennaRNA (RNAplot), R2DT
- Output formats
- WIG, BigWig, PostScript/SVG diagrams, RDAT, MultiQC HTML
- Original developers
- Victoria Begley and Pedro Madrigal (RNAcentral, EMBL-EBI)
What you can use it for
- Per-base reactivity profiling
- Quantify modification rates across transcripts from SHAPE or DMS libraries using RT-stop or mutational profiling readouts.
- Data-directed secondary structure folding
- Incorporate per-nucleotide reactivity constraints into secondary structure folding algorithms to predict base-paired conformers and compute Shannon entropy.
- Microbial and viral structural probing
- Analyse probing libraries for non-model organisms, bacteria, or viruses with automated retrieval of reference sequences and annotations from NCBI.
- Structural repository submission
- Generate standard RDAT files linking reactivity values, experimental metadata, and folded structures for archiving in databases such as RMDB.
- Normalisation parameter calibration
- Calibrate normalisation thresholds using rf-jackknife or benchmark predicted RNA models against curated reference structures using rf-eval.
Run it here
The research agent runs nf-core/rnastructurome 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
- Accommodates both RT-stop and mutational profiling methods across SHAPE and DMS chemistries with principle-specific parameter selection.
- Automatically downloads required genome FASTA and GTF reference files from Ensembl or NCBI when local annotations are omitted.
- Avoids standard position-based deduplication by default to protect authentic independent probing stops that share 5' terminal coordinates.
- Renders secondary structure visualisations using both ViennaRNA and template-based R2DT layouts in parallel.
Watch out for
- Does not execute differential statistical testing or comparative structure analysis across separate biological conditions.
- R2DT template-matched structural drawing is restricted to containerised execution and does not run in pure Conda or Mamba environments.
- Requires UMIs if PCR duplicate removal is necessary, as coordinate-based deduplication cannot distinguish duplicates from distinct probing events.
Questions
Can I run nf-core/rnastructurome without my own cluster?
The research agent runs nf-core/rnastructurome 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/rnastructurome under?
MIT, like every nf-core pipeline.
Can the pipeline function without locally stored genome reference files?
Yes. When no local FASTA or GTF is provided, the pipeline automatically fetches reference sequences from Ensembl, or from NCBI for organisms such as bacteria and viruses.
Why is coordinate-based duplicate removal turned off by default?
In chemical structure-probing experiments without UMIs, multiple fragments sharing the same 5' start coordinate typically represent valid independent reverse transcription events rather than PCR artifacts. Standard duplicate removal tools would mistakenly discard these valid signals.
Does the pipeline support direct alignment against transcriptome sequences?
Yes. By supplying a transcript FASTA and setting the transcriptome flag, the pipeline aligns reads using Bowtie for RT-stop libraries or Bowtie2 for MaP libraries instead of running STAR on a genome.
Can the pipeline identify structural differences between two experimental conditions?
No. The pipeline generates normalised reactivities and folds secondary structures per sample group, but it does not perform differential comparative statistics between distinct conditions.
How to cite it
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
Written from nf-core/rnastructurome's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.