nf-core pipeline ·
nf-core/drop
Detects aberrant expression, splicing, and mono-allelic expression in RNA-seq data for rare disorder diagnosis.
What nf-core/drop is
nf-core/drop is a Nextflow bioinformatics pipeline developed to detect aberrant expression, aberrant splicing, and mono-allelic expression from RNA sequencing data. It is a Nextflow reimplementation of DROP (Detection of RNA Outliers Pipeline), originally authored by Vicente Yepez, Christian Mertes, Michaela Mueller, Daniela Andrade, and Leonhard Wachutka from the Gagneur lab at the Technical University of Munich and the German Human Genome-Phenome Archive. The nf-core adaptation was led by Nicolas Vannieuwkerke and Yun Wang, with contributions from Ata Jadid Ahari and Drew Behrens.
The pipeline is designed to assist in the diagnosis of rare disorders by uncovering transcriptomic outliers that may indicate pathogenic regulatory or structural variants. By automating the extraction of aberrant molecular signals across large sample sets, it provides clinical and biological researchers with systematic detection of rare molecular events.
What the pipeline does
The workflow evaluates transcriptomic data through three distinct analysis modules followed by quality reporting. For aberrant expression analysis, read count matrices are generated using GenomicAlignments and evaluated with OUTRIDER to discover expression outliers. For aberrant splicing detection, split and non-split reads are counted using GenomicAlignments and Subread, after which FRASER identifies anomalous splicing junctions.
For mono-allelic expression analysis, GATK ASEReadCounter measures allelic read counts at heterozygous sites, which DESeq2 then uses to test for significantly skewed allelic expression. Finally, MultiQC aggregates quality control metrics into a unified summary report across the analysed samples.
Key facts
- Pipeline framework
- Nextflow (nf-core)
- Input formats
- TSV samplesheet, BAM, BAI, VCF (for MAE), TBI (for MAE)
- Aberrant expression module
- GenomicAlignments, OUTRIDER
- Aberrant splicing module
- GenomicAlignments, Subread, FRASER
- Mono-allelic expression module
- GATK ASEReadCounter, DESeq2
- QC reporting
- MultiQC
- Latest release
- 1.0.0 (2026-04-02)
What you can use it for
- Rare disorder transcriptomic diagnostics
- Screens patient RNA-seq profiles against a cohort to identify atypical transcriptomic events linked to rare genetic conditions.
- Aberrant splicing discovery
- Identifies non-canonical or dysfunctional splice junctions across mapped RNA-seq reads using split and non-split read counting in FRASER.
- Expression outlier detection
- Finds genes showing significant under-expression or over-expression across cohort samples using OUTRIDER.
- Mono-allelic expression profiling
- Quantifies allele-specific read counts and identifies genes with skewed mono-allelic expression using matched DNA variant calls and DESeq2.
Run it here
The research agent runs nf-core/drop 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
- Combines aberrant expression, aberrant splicing, and mono-allelic expression detection into a single automated pipeline.
- Uses established, dedicated outlier-detection statistical methods including OUTRIDER and FRASER.
- Generates unified quality control visualisations across the dataset using MultiQC.
Watch out for
- Requires a cohort size exceeding 30 samples to detect transcriptomic outliers with statistical confidence.
- Mono-allelic expression assessment requires matched DNA variant files (VCF and TBI) in addition to RNA-seq data.
Questions
Can I run nf-core/drop without my own cluster?
The research agent runs nf-core/drop 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/drop under?
MIT, like every nf-core pipeline.
What sample size is recommended for running this pipeline?
A cohort size of more than 30 samples is required to detect expression and splicing outliers reliably.
What inputs are required to run all modules?
A TSV samplesheet specifying RNA BAM and BAI files, strand orientation, and sample grouping is required. If running mono-allelic expression, matched DNA VCF and index files with genome designations must also be supplied.
Which software tools perform the outlier detection?
Aberrant expression outliers are identified using OUTRIDER, aberrant splicing events are detected with FRASER, and skewed mono-allelic expression is identified using DESeq2.
How to cite it
10.5281/zenodo.19383502
Written from nf-core/drop's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.