nf-core/rnafusion
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RNA-seq analysis pipeline for detection of gene-fusions

fusionfusion-genesgene-fusionrnarna-seq

These pages are for an old version of the pipeline (2.2.0). The latest stable release is 3.0.2 .

Launch version 2.2.0 https://github.com/nf-core/rnafusion

Introduction

The pipeline is divided into two parts:

Download and build references
- specified with --build_references parameter
- required only once before running the pipeline
- Important: rerun with each new release
Detecting fusions
- Supported tools: Arriba, FusionCatcher, pizzly, SQUID, STAR-Fusion and StringTie
- QC: Fastqc and MultiQC
- Fusion visualization: Arriba (only fusion detected with Arriba), fusion-report and FusionInspector

1. Download and build references

The rnafusion pipeline needs references for the fusion detection tools, so downloading these is a requirement. Whilst it is possible to download and build each reference manually, it is advised to download references with the rnafusion pipeline.

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

Download the references as shown below including your COSMIC credentials.

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>

Using QIAGEN download insead of SANGER (non-academic usage) for the COSMIC database

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

References directory tree

references/
|-- arriba
|-- ensembl
|-- fusion_report_db
|-- fusioncatcher
|   `-- human_v102
|-- pizzly
|-- star
`-- starfusion

Issues with building references

If process FUSIONREPORT_DOWNLOAD times out, it could be due to network restriction (e.g. if trying to run on HPC). As this process is lightweight in compute, storage 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 compute 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 {
  withName:  'NFCORE_RNAFUSION:BUILD_REFERENCES:FUSIONREPORT_DOWNLOAD' {
    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.

Non-human references

Non-human references, not supported by default, can be built manually and fed to rnafusion using the parameter --<tool>_ref.

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!

2. Detecting fusions

This step 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>

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 in step 1 build_references.

Alternatively, 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>

Trimming

There are 2 options to trim

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)

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> \
--trim
``
 
#### Adding custom fusions to consider as well as the detected set: whitelist
 
```bash
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:
 
```
GENE1--GENE2
GENE3--GENE4
```
 
#### Running FusionInspector only
 
FusionInspector can be run standalone with:
 
```bash
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:

GENE1--GENE2
GENE3--GENE4

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.

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use the --input parameter to specify its location. 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.

sample,fastq_1,fastq_2,strandedness
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,forward
CONTROL_REP2,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz,forward
CONTROL_REP3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz,forward
TREATMENT_REP1,AEG588A4_S4_L003_R1_001.fastq.gz,,forward
TREATMENT_REP2,AEG588A5_S5_L003_R1_001.fastq.gz,,forward
TREATMENT_REP3,AEG588A6_S6_L003_R1_001.fastq.gz,,forward
TREATMENT_REP3,AEG588A6_S6_L004_R1_001.fastq.gz,,forward

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.

An example samplesheet has been provided with the pipeline.

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. Below is an example for the same sample sequenced across 3 lanes:

sample,fastq_1,fastq_2,strandedness
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,forward
CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz,forward
CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz,forward

Running the pipeline

The typical command for running the pipeline is as follows.

nextflow run nf-core/rnafusion --input samplesheet.csv --outdir <OUTDIR> --genome GRCh38 -profile docker

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

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.

Set different `--limitSjdbInsertNsj` parameter

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

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

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

Compress to CRAM file

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

Reproducibility

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.

Core Nextflow arguments

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

`-profile`

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, 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
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 or Charliecloud.
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 !!!!

`-resume`

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.

`-c`

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.

For example, if the nf-core/rnaseq pipeline is failing after multiple re-submissions of the STAR_ALIGN process due to an exit code of 137 this would indicate that there is an out of memory issue:

[62/149eb0] NOTE: Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1)
Error executing process > 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)'
 
Caused by:
    Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137)
 
Command executed:
    STAR \
        --genomeDir star \
        --readFilesIn WT_REP1_trimmed.fq.gz  \
        --runThreadN 2 \
        --outFileNamePrefix WT_REP1. \
        <TRUNCATED>
 
Command exit status:
    137
 
Command output:
    (empty)
 
Command error:
    .command.sh: line 9:  30 Killed    STAR --genomeDir star --readFilesIn WT_REP1_trimmed.fq.gz --runThreadN 2 --outFileNamePrefix WT_REP1. <TRUNCATED>
Work dir:
    /home/pipelinetest/work/9d/172ca5881234073e8d76f2a19c88fb
 
Tip: you can replicate the issue by changing to the process work dir and entering the command `bash .command.run`

For beginners

A first step to bypass this error, you could try to increase the amount of CPUs, memory, and time for the whole pipeline. Therefor you can try to increase the resource for the parameters --max_cpus, --max_memory, and --max_time. Based on the error above, you have to increase the amount of memory. Therefore you can go to the parameter documentation of rnaseq and scroll down to the show hidden parameter button to get the default value for --max_memory. In this case 128GB, you than can try to run your pipeline again with --max_memory 200GB -resume to skip all process, that were already calculated. If you can not increase the resource of the complete pipeline, you can try to adapt the resource for a single process as mentioned below.

Advanced option on process level

To bypass this error you would need to find exactly which resources are set by the STAR_ALIGN process. The quickest way is to search for process STAR_ALIGN in the nf-core/rnaseq Github repo. We have standardised the structure of Nextflow DSL2 pipelines such that all module files will be present in the modules/ directory and so, based on the search results, the file we want is modules/nf-core/star/align/main.nf. If you click on the link to that file you will notice that there is a label directive at the top of the module that is set to label process_high. The Nextflow label directive allows us to organise workflow processes in separate groups which can be referenced in a configuration file to select and configure subset of processes having similar computing requirements. The default values for the process_high label are set in the pipeline’s base.config which in this case is defined as 72GB. Providing you haven’t set any other standard nf-core parameters to cap the maximum resources used by the pipeline then we can try and bypass the STAR_ALIGN process failure by creating a custom config file that sets at least 72GB of memory, in this case increased to 100GB. The custom config below can then be provided to the pipeline via the -c parameter as highlighted in previous sections.

process {
    withName: 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN' {
        memory = 100.GB
    }
}

NB: We specify the full process name i.e. NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN in the config file because this takes priority over the short name (STAR_ALIGN) and allows existing configuration using the full process name to be correctly overridden.

If you get a warning suggesting that the process selector isn’t recognised check that the process name has been specified correctly.

Tool-specific options

For the ultimate flexibility, we have implemented and are using Nextflow DSL2 modules in a way where it is possible for both developers and users to change tool-specific command-line arguments (e.g. providing an additional command-line argument to the STAR_ALIGN process) as well as publishing options (e.g. saving files produced by the STAR_ALIGN process that aren’t saved by default by the pipeline). In the majority of instances, as a user you won’t have to change the default options set by the pipeline developer(s), however, there may be edge cases where creating a simple custom config file can improve the behaviour of the pipeline if for example it is failing due to a weird error that requires setting a tool-specific parameter to deal with smaller / larger genomes.

The command-line arguments passed to STAR in the STAR_ALIGN module are a combination of:

Mandatory arguments or those that need to be evaluated within the scope of the module, as supplied in the script section of the module file.
An options.args string of non-mandatory parameters that is set to be empty by default in the module but can be overwritten when including the module in the sub-workflow / workflow context via the addParams Nextflow option.

The nf-core/rnaseq pipeline has a sub-workflow (see terminology) specifically to align reads with STAR and to sort, index and generate some basic stats on the resulting BAM files using SAMtools. At the top of this file we import the STAR_ALIGN module via the Nextflow include keyword and by default the options passed to the module via the addParams option are set as an empty Groovy map here; this in turn means options.args will be set to empty by default in the module file too. This is an intentional design choice and allows us to implement well-written sub-workflows composed of a chain of tools that by default run with the bare minimum parameter set for any given tool in order to make it much easier to share across pipelines and to provide the flexibility for users and developers to customise any non-mandatory arguments.

When including the sub-workflow above in the main pipeline workflow we use the same include statement, however, we now have the ability to overwrite options for each of the tools in the sub-workflow including the align_options variable that will be used specifically to overwrite the optional arguments passed to the STAR_ALIGN module. In this case, the options to be provided to STAR_ALIGN have been assigned sensible defaults by the developer(s) in the pipeline’s modules.config and can be accessed and customised in the workflow context too before eventually passing them to the sub-workflow as a Groovy map called star_align_options. These options will then be propagated from workflow -> sub-workflow -> module.

As mentioned at the beginning of this section it may also be necessary for users to overwrite the options passed to modules to be able to customise specific aspects of the way in which a particular tool is executed by the pipeline. Given that all of the default module options are stored in the pipeline’s modules.config as a params variable it is also possible to overwrite any of these options via a custom config file.

Say for example we want to append an additional, non-mandatory parameter (i.e. --outFilterMismatchNmax 16) to the arguments passed to the STAR_ALIGN module. Firstly, we need to copy across the default args specified in the modules.config and create a custom config file that is a composite of the default args as well as the additional options you would like to provide. This is very important because Nextflow will overwrite the default value of args that you provide via the custom config.

As you will see in the example below, we have:

appended --outFilterMismatchNmax 16 to the default args used by the module.
changed the default publishDir value to where the files will eventually be published in the main results directory.
appended 'bam':'' to the default value of publish_files so that the BAM files generated by the process will also be saved in the top-level results directory for the module. Note: 'out':'log' means any file/directory ending in out will now be saved in a separate directory called my_star_directory/log/.

params {
    modules {
        'star_align' {
            args          = "--quantMode omeSAM --twopassMode Basic --outSAMtype BAM Unsorted --readFilesCommand zcat --runRNGseed 0 --outFilterMultimapNmax 20 --alignSJDBoverhangMin 1 --outSAMattributes NH HI AS NM MD --quantomeBan Singleend --outFilterMismatchNmax 16"
            publishDir    = "my_star_directory"
            publish_files = ['out':'log', 'tab':'log', 'bam':'']
        }
    }
}

Updating containers (advanced users)

The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. If for some reason you need to use a different version of a particular tool with the pipeline then you just need to identify the process name and override the Nextflow container definition for that process using the withName declaration. For example, in the nf-core/viralrecon pipeline a tool called Pangolin has been used during the COVID-19 pandemic to assign lineages to SARS-CoV-2 genome sequenced samples. Given that the lineage assignments change quite frequently it doesn’t make sense to re-release the nf-core/viralrecon everytime a new version of Pangolin has been released. However, you can override the default container used by the pipeline by creating a custom config file and passing it as a command-line argument via -c custom.config.

Check the default version used by the pipeline in the module file for Pangolin
Find the latest version of the Biocontainer available on Quay.io

Create the custom config accordingly:

For Docker:

process {
    withName: PANGOLIN {
        container = 'quay.io/biocontainers/pangolin:3.0.5--pyhdfd78af_0'
    }
}

For Singularity:

process {
    withName: PANGOLIN {
        container = 'https://depot.galaxyproject.org/singularity/pangolin:3.0.5--pyhdfd78af_0'
    }
}

For Conda:

process {
    withName: PANGOLIN {
        conda = 'bioconda::pangolin=3.0.5'
    }
}

NB: If you wish to periodically update individual tool-specific results (e.g. Pangolin) generated by the pipeline then you must ensure to keep the work/ directory otherwise the -resume ability of the pipeline will be compromised and it will restart from scratch.

nf-core/configs

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'

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