Pipeline summary

The pipeline is divided into two parts:

  1. Download and build references
    • specified with --build_references parameter
    • required only once before running the pipeline
    • *Important*: has to be run with each new release
  2. Detecting fusions
    • Supported tools: Arriba, FusionCatcher, pizzly, SQUID, STAR-Fusion, and StringTie
    • QC: Fastqc, MultiQC, and Qualimap rnaseq
    • Fusions visualization: Arriba, fusion-report and FusionInspector, VCF file creation based on MegaFusion

Download and build references

The rnafusion pipeline needs references for the fusion detection tools, so downloading these is a *requirement*.


  • Note that this step takes about 24 hours to complete on HPC.
  • Do not provide a samplesheet via the input parameter, otherwise the pipeline will run the analysis directly after downloading the references (except if that is what you want).
nextflow run nf-core/rnafusion \  
  --build_references --all \  
  --cosmic_username <EMAIL> --cosmic_passwd <PASSWORD> \  
  --genomes_base <PATH/TO/REFERENCES> \  
  --outdir <PATH/TO/REFERENCES>  

References for each tools can also be downloaded separately with:

nextflow run nf-core/rnafusion \  
  --build_references --<tool1> --<tool2> ... \  
  --cosmic_username <EMAIL> --cosmic_passwd <PASSWORD> \  
  --genomes_base <PATH/TO/REFERENCES> \  
  --outdir <OUTPUT/PATH>  

Downloading the cosmic database with SANGER or QUIAGEN

For academic users

First register for a free account at COSMIC at https://cancer.sanger.ac.uk/cosmic/register using a university email. The account is *only activated upon* clicking the link in the registration email.

For non-academic users

Use credentials from QIAGEN and add --qiagen

nextflow run nf-core/rnafusion \  
  --build_references --<tool1> --<tool2> ... \  
  --cosmic_username <EMAIL> --cosmic_passwd <PASSWORD> \  
  --genomes_base <PATH/TO/REFERENCES> \  
  --outdir <OUTPUT/PATH> --qiagen  

STAR-Fusion references downloaded vs built

By default STAR-Fusion references are *built*. You can also download them from CTAT by using the flag --starfusion_build FALSE for both reference building and fusion detection. This allows more flexibility for different organisms but *be aware that STAR-Fusion reference download is not recommended as not fully tested!*

Issues with building references

If process FUSIONREPORT_DOWNLOAD times out, it could be due to network restriction (for example if trying to run on HPC). As this process is lightweight in cpu, memory and time, running on local machines with the following options might solve the issue:

nextflow run nf-core/rnafusion  \  
  --build_references \  
  --cosmic_username <EMAIL> --cosmic_passwd <PASSWORD> \  
  --fusionreport \  
  --genomes_base <PATH/TO/REFERENCES> \  
  --outdir <OUTPUT/PATH>  

Adjustments for cpu and memory requirements can be done by feeding a custom configuration with -c /PATH/TO/CUSTOM/CONFIG.
Where the custom configuration could look like (adaptation to local machine necessary):

process {  
    memory = '8.GB'  
    cpus = 4  

The four fusion-report files: cosmic.db, fusiongdb.db, fusiongdb2.db, mitelman.db
should then be copied into the HPC <REFERENCE_PATH>/references/fusion_report_db.

Running the pipeline

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. The pipeline will detect whether a sample is single- or paired-end from the samplesheet - the fastq_2 column is empty for single-end. The samplesheet has to be a comma-separated file (.csv) but can have as many columns as you desire. There is a strict requirement for the first 4 columns to match those defined in the table below with the header row included.
A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 6 samples, where TREATMENT_REP3 has been sequenced twice.


As you can see above for multiple runs of the same sample, the sample name has to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis.

| Column | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| sample | Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (_). |
| fastq_1 | Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz". |
| fastq_2 | Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz". |
| strandedness | Strandedness: forward or reverse. |

Starting commands

The pipeline can either be run using all fusion detection tools or specifying individual tools. Visualisation tools will be run on all fusions detected. To run all tools (arriba, fusioncatcher, pizzly, squid, starfusion, stringtie) use the --all parameter:

nextflow run nf-core/rnafusion \  
  --all \  
  --input <SAMPLE_SHEET.CSV> \  
  --genomes_base <PATH/TO/REFERENCES> \  
  --outdir <OUTPUT/PATH>  

To run only a specific detection tool use: --tool:

nextflow run nf-core/rnafusion \  
  --<tool1> --<tool2> ... \  
  --input <SAMPLE_SHEET.CSV> \  
  --genomes_base <PATH/TO/REFERENCES> \  
  --outdir <OUTPUT/PATH>  

*IMPORTANT: Either --all or --<tool>* is necessary to run detection tools

--genomes_base should be the path to the directory containing the folder references/ that was built with --build_references.

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files  
<OUTDIR>            # Finished results in specified location (defined with --outdir)  
.nextflow_log       # Log file from Nextflow  
# Other nextflow hidden files, eg. history of pipeline runs and old logs.  

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

⚠️ Do not use -c <file> to specify parameters as this will result in errors. Custom config files specified with -c must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/rnafusion -profile docker -params-file params.yaml  

with params.yaml containing:

input: './samplesheet.csv'  
outdir: './results/'  

You can also generate such YAML/JSON files via nf-core/launch.



There are 2 options to trim

  1. Fastp
    In this case all tools use the trimmed reads. Quality and adapter trimming by default. In addition, tail trimming and adapter_fastq specification are possible. Example usage:
nextflow run nf-core/rnafusion \  
--<tool1> --<tool2> ... \  
--input <SAMPLE_SHEET.CSV> \  
--genomes_base <PATH/TO/REFERENCES> \  
--outdir <OUTPUT/PATH> \  
--fastp_trim \  
--trim_tail <INTEGER> (optional) \  
--adapter_fastq <PATH/TO/ADAPTER/FASTQ> (optional)  
  1. Hard trimming
    In this case, only reads fed to fusioncatcher are trimmed. This is a harsh workaround in case of high read-through. The recommended trimming is thus the fastp_trim one. The trimming is done at 75 bp from the tails. Example usage:
nextflow run nf-core/rnafusion \  
--<tool1> --<tool2> ... \  
--input <SAMPLE_SHEET.CSV> \  
--genomes_base <PATH/TO/REFERENCES> \  
--outdir <OUTPUT/PATH> \  

Filter fusions detected by 2 or more tools

nextflow run nf-core/rnafusion \  
  --<tool1> --<tool2> ... \  
  --input <SAMPLE_SHEET.CSV> \  
  --genomes_base <PATH/TO/REFERENCES> \  
  --outdir <OUTPUT/PATH>  

--fusioninspector_filter feed only fusions detected by 2 or more tools to fusioninspector for closer analysis (false by default).
--fusionreport_filter displays only fusions detected by 2 or more tools in fusionreport html index (true by default).

Adding custom fusions to consider as well as the detected set: whitelist

nextflow run nf-core/rnafusion \  
  --<tool1> --<tool2> ... \  
  --input <SAMPLE_SHEET.CSV> \  
  --genomes_base <PATH/TO/REFERENCES> \  
  --outdir <OUTPUT/PATH>  
  --whitelist <WHITELIST/PATH>  

The custom fusion file should have the following format:


Running FusionInspector only

FusionInspector can be run as a standalone with:

nextflow run nf-core/rnafusion \  
--fusioninspector_only \  
--fusioninspector_fusions <PATH_TO_CUSTOM_FUSION_FILE> \  
--input <SAMPLE_SHEET.CSV> \  
--outdir <PATH>  

The custom fusion file should have the following format:


Skipping QC

nextflow run nf-core/rnafusion \  
--skip_qc \  
--all OR <--tool>  
--input <SAMPLE_SHEET.CSV> \  
--genomes_base <PATH/TO/REFERENCES> \  
--outdir <PATH>  

This will skip all QC-related processes (metrics collection, Qualimap)

Skipping visualisation

nextflow run nf-core/rnafusion \  
--skip_vis \  
--all OR <--tool>  
--input <SAMPLE_SHEET.CSV> \  
--genomes_base <PATH/TO/REFERENCES> \  
--outdir <PATH>  

This will skip all visualisation processes, including fusion-report, FusionInspector and Arriba visualisation.

Optional manual feed-in of fusion files

It is possible to give the output of each tool manually using the argument: --<tool>_fusions PATH/TO/FUSION/FILE: this feature need more testing, don't hesitate to open an issue if you encounter problems.

Set different --limitSjdbInsertNsj parameter

There are two parameters to increase the --limitSjdbInsertNsj parameter if necessary:

  • --fusioncatcher_limitSjdbInsertNsj, default: 2000000
  • --fusioninspector_limitSjdbInsertNsj, default: 1000000

Use the parameter --cram to compress the BAM files to CRAM for specific tools. Options: arriba, squid, starfusion. Leave no space between options:

  • --cram arriba,squid,starfusion, default: []
  • --cram arriba

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull nf-core/rnafusion  


It is a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the nf-core/rnafusion releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in reproducbility, you can use share and re-use parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

💡 If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.

Core Nextflow arguments

*NB:* These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).


Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important!
They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended, since it can lead to different results on different machines dependent on the computer enviroment.

  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters
  • docker
    • A generic configuration profile to be used with Docker
  • singularity
    • A generic configuration profile to be used with Singularity
  • podman
    • A generic configuration profile to be used with Podman
  • shifter
    • A generic configuration profile to be used with Shifter
  • charliecloud
    • A generic configuration profile to be used with Charliecloud
  • apptainer
    • A generic configuration profile to be used with Apptainer
  • conda
    • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.
  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters
    • Needs to run in two steps: with --build_references first and then without --build_references to run the analysis
    • !!!! Run with -stub as all references need to be downloaded otherwise !!!!


Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.


Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases you may wish to change which container or conda environment a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the biocontainers or bioconda projects. However in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.


In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel. -->

Azure Resource Requests

To be used with the azurebatch profile by specifying the -profile azurebatch.
We recommend providing a compute params.vm_type of Standard_D16_v3 VMs by default but these options can be changed if required.

Note that the choice of VM size depends on your quota and the overall workload during the analysis.
For a thorough list, please refer the Azure Sizes for virtual machines in Azure.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time.
Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory.
We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'