Build Status Nextflow MIT License

install with bioconda Docker Singularity Container available

nfcore/rnafusion uses RNA-seq data to detect fusions genes.

The workflow processes RNA-sequencing data from FastQ files. It runs quality control on the raw data (FastQC), detects fusion genes (STAR-Fusion, Fusioncatcher, Ericscript, Pizzly, Squid), gathers information (FusionGDB), visualizes the fusions (FusionInspector), performs quality-control on the results (MultiQC) and finally generates custom summary report.

Final summary report

The pipeline works with both single-end and paired-end data, though not all fusion detection tools work with single-end data (Ericscript, Pizzly, Squid and FusionInspector).

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.

ToolSingle-end readsCPU (recommended)RAM (recommended)
Star-FusionYes>=16 cores~30GB
FusioncatcherYes>=16 cores~60GB
EricscriptNo>=16 cores~30GB
PizzlyNo>=16 cores~30GB
SquidNo>=16 cores~30GB
FusionInspectorNo>=16 cores~30GB

TL;DR: Make sure to download all required references for each tool. More details can be found in section tools.

nextflow run nf-core/rnafusion --reads '*_R{1,2}.fastq.gz' --genome GRCh38 -profile docker --star_fusion --fusioncatcher --ericscript --pizzly --squid --fusion_inspector

For available parameters or help run:

nextflow run nf-core/rnafusion --help


The nf-core/rnafusion pipeline comes with documentation about the pipeline, found in the docs/ directory:

  1. Installation
  2. Pipeline configuration
  3. Running the pipeline
  4. Output and how to interpret the results
  5. Troubleshooting

Use predefined configuration for desired Institution cluster provided at nfcore/config repository.