nf-core pipeline · dartseq, rna-editing, rnaseq
nf-core/dartseq
A bioinformatics pipeline for m6A RNA modification detection and quality control in DART-seq sequencing data.
What nf-core/dartseq is
nf-core/dartseq is a bioinformatics workflow designed for analysing DART-seq (deamination adjacent to RNA modification targets) style RNA sequencing datasets. It was originally developed by Mathieu Flamand, Olga Brovkina, Joana Pimenta Bernardes, and Fatemeh Nasehi.
The pipeline processes raw sequencing data to identify and quantify m6A RNA editing events alongside standard RNA-seq processing steps. It provides researchers with a standardised method to take raw reads through quality control, alignment, and post-processing statistical assessments of edited sites.
What the pipeline does
The pipeline begins by validating an input samplesheet specifying single-end or paired-end FASTQ files. Reads undergo optional adapter and quality trimming using either fastp or Trim Galore, followed by quality assessment with FastQC. Reads are then aligned to a reference genome using either STAR or HISAT2, which can be supplied by the user or generated by the workflow, yielding coordinate-sorted BAM files and index files.
For downstream RNA editing evaluation, the pipeline can run Bullseye-based post-processing. This stage parses alignments, summarises candidate editing sites, quantifies editing events, and compares experimental samples to controls. It can also perform RAC motif filtering, site aggregation, and generalised linear modelling (GLM). MultiQC compiles run metrics, quality reports, and software versions into an aggregated summary.
Key facts
- Inputs
- Single-end or paired-end FASTQ files defined via a CSV samplesheet
- Supported aligners
- STAR, HISAT2
- Read trimmers
- fastp, Trim Galore
- RNA editing tool
- Bullseye
- Outputs
- Sorted BAM and index files, QC reports, MultiQC report, edited-site tables
- Original developers
- Mathieu Flamand, Olga Brovkina, Joana Pimenta Bernardes, Fatemeh Nasehi
What you can use it for
- m6A RNA modification detection
- Identifying and quantifying m6A editing events across transcripts using DART-seq sequencing reads.
- RNA sequencing alignment and QC
- Trimming, assessing quality, and aligning single-end or paired-end RNA-seq reads using STAR or HISAT2.
- Comparative RNA editing analysis
- Contrasting edited sites against control samples and evaluating them via RAC filtering and generalised linear models in Bullseye.
Run it here
The research agent runs nf-core/dartseq 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
- Provides integrated, optional Bullseye modules to quantify RNA editing and compare samples against controls.
- Supports multiple aligners, accommodating both STAR and HISAT2 references.
- Offers flexible read preprocessing with the option to use fastp, Trim Galore, or skip trimming entirely.
Watch out for
- Downstream RNA editing discovery and site quantification are tied specifically to Bullseye-based analysis.
- Requires reference genomes and index parameters compatible with STAR or HISAT2.
Questions
Can I run nf-core/dartseq without my own cluster?
The research agent runs nf-core/dartseq 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/dartseq under?
MIT, like every nf-core pipeline.
What sequencing inputs does the pipeline accept?
It accepts single-end or paired-end FASTQ reads configured via a CSV samplesheet containing sample names and file paths.
Which aligners can be used?
The pipeline supports STAR and HISAT2, and can either construct the required index files or use pre-existing ones.
How does the pipeline analyse RNA editing?
It uses Bullseye to parse BAM files, quantify edit proportions, compare against controls, and optionally perform RAC motif filtering and generalised linear modelling.
Can read trimming be bypassed?
Yes, trimming with fastp or Trim Galore can be disabled if requested.
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/dartseq's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.