nf-core pipeline · circrna, circrna-pipeline, circrna-prediction
nf-core/circrna
Analyses total RNA sequencing data to identify, quantify, and predict miRNA targets for circular RNAs.
What nf-core/circrna is
nf-core/circrna is a bioinformatics pipeline designed to analyse total RNA sequencing data from organisms with a reference genome and annotation. Circular RNAs, or circRNAs, are non-coding RNA molecules formed through back-splicing events where a downstream splice donor joins an upstream splice acceptor. The workflow provides an end-to-end processing path from raw sequencing reads to circular RNA discovery, annotation, transcript quantification, and microRNA (miRNA) target prediction.
Originally created by Barry Digby and later refactored and expanded by Nico Trummer, the pipeline brings together standard quality assessment and multiple specialised circular RNA detection tools into a reproducible framework. It handles the extraction of circular sequences and compares circular transcript abundance against linear transcripts derived from reference annotations.
What the pipeline does
The workflow begins by running FastQC for raw read quality assessment and Trim Galore! for adapter trimming. Back-splice junction (BSJ) detection is performed using a selection of algorithms, including CIRI2, CIRI3, STAR 2-Pass mode (paired with CIRCexplorer2, circRNA finder, or circtools), circMiner, find_circ, MapSplice, psirc, and Segemehl. Detected circRNAs are annotated using a provided GTF file and optional database records. The pipeline then extracts circular RNA sequences to assemble a circular transcriptome, which is subsequently combined with the GTF-derived linear transcriptome.
Quantification across the combined transcriptome is conducted using psirc-quant alongside CIRI3 consensus back-splice junction and forward-splice junction count matrices. If mature miRNA sequences are provided, binding sites are predicted using miRanda and TargetScan, evaluated via majority voting, and optionally correlated with miRNA expression levels. When phenotype metadata is supplied, the pipeline carries out statistical testing through CircTest, before aggregating all run metrics into a MultiQC report.
Key facts
- Input formats
- FASTQ (single-end or paired-end), CSV samplesheet, GTF annotation file, reference genome
- Output formats
- Count matrices, MultiQC report
- BSJ detection tools
- CIRI2, CIRI3, CIRCexplorer2, circRNA finder, circtools, circMiner, find_circ, MapSplice, psirc, Segemehl
- Quantification tools
- CIRI3, psirc-quant
- miRNA prediction tools
- miRanda, TargetScan
- Statistical testing
- CircTest
- Quality control tools
- FastQC, Trim Galore!, MultiQC
- Original developer
- Barry Digby
What you can use it for
- Back-splice junction detection
- Identifying candidate circular RNA back-splice junctions from total RNA-seq reads using multiple detection algorithms.
- Transcriptome-wide circular RNA quantification
- Quantifying circular and linear transcript abundances simultaneously through CIRI3 consensus count matrices or psirc-quant.
- miRNA binding affinity prediction
- Predicting miRNA target binding sites across circRNAs using miRanda and TargetScan when mature miRNA sequences are supplied.
- Phenotype-associated statistical testing
- Evaluating circRNA expression differences across experimental conditions using CircTest when phenotype parameters are specified.
Run it here
The research agent runs nf-core/circrna 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 an extensive range of back-splice junction detection algorithms within a single framework.
- Quantifies combined circular and linear transcriptomes to contextualise circular transcript abundance.
- Integrates miRNA target site prediction using consensus voting across multiple prediction engines.
- Generates unified quality control summaries across all processing steps using MultiQC.
Watch out for
- Isoform-level circular RNA detection and quantification are not yet implemented.
- circRNA-miRNA interaction analysis using SPONGE and spongEffects is not yet functional.
- Requires an organism with an existing reference genome and GTF annotation.
- miRNA target prediction and statistical testing require additional optional parameters and data inputs.
Questions
Can I run nf-core/circrna without my own cluster?
The research agent runs nf-core/circrna 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/circrna under?
MIT, like every nf-core pipeline.
What inputs are required to run nf-core/circrna?
The pipeline requires a CSV samplesheet pointing to single-end or paired-end FASTQ files, a reference genome, and a matching GTF annotation file.
How does the pipeline identify miRNA binding sites?
When the mature miRNA parameter is supplied, miRanda and TargetScan predict binding sites on circular RNA sequences, followed by a majority-vote assessment and optional expression correlation analysis.
Can the pipeline quantify both linear and circular transcripts?
Yes, it merges the circular transcriptome with the linear transcriptome derived from the GTF file and quantifies them using psirc-quant and CIRI3 consensus count matrices.
Which back-splice junction detection tools are available?
The pipeline includes CIRI2, CIRI3, STAR 2-Pass mode with CIRCexplorer2, circRNA finder, or circtools, as well as circMiner, find_circ, MapSplice, psirc, and Segemehl.
How to cite it
Barry Digby, Stephen P. Finn, & Pilib Ó Broin. nf-core/circrna: a portable workflow for the quantification, miRNA target prediction and differential expression analysis of circular RNAs. BMC Bioinformatics 24, 27 (2023). doi: 10.1186/s12859-022-05125-8
Written from nf-core/circrna's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.