Nextflow handles job submissions on SLURM or other environments, and supervises running the jobs. Thus the Nextflow process must run until the pipeline is finished. We recommend that you put the process running in the background through screen / tmux or similar tool. Alternatively you can run nextflow within a cluster job submitted your job scheduler.

It is recommended to limit the Nextflow Java virtual machines memory. We recommend adding the following line to your environment (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'  

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/epitopeprediction --somatic_mutations "\*.vcf.gz" -profile docker  

This will launch the pipeline with the docker configuration profile and default options (syfpeithi by default). 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  
results         # Finished results (configurable, see below)  
.nextflow_log   # Log file from Nextflow  
# Other nextflow hidden files, eg. history of pipeline runs and old logs.  

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/epitopeprediction  


It's 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/epitopeprediction releases page and find the latest version number - numeric only (eg. 1.0.0). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.0.0.

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.

Generic pipeline arguments


Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments. Note that multiple profiles can be loaded, for example: -profile docker - the order of arguments is important!

If -profile is not specified at all the pipeline will be run locally and expects all software to be installed and available on the PATH.

* awsbatch
* A generic configuration profile to be used with AWS Batch.
* conda
* A generic configuration profile to be used with conda
* Pulls most software from Bioconda
* docker
* A generic configuration profile to be used with Docker
* Pulls software from dockerhub: nfcore/epitopeprediction
* singularity
* A generic configuration profile to be used with Singularity
* Pulls software from DockerHub: nfcore/epitopeprediction
* test
* A profile with a complete configuration for automated testing
* Includes links to test data so needs no other parameters

Main pipeline parameters


The path to the file containing the MHC alleles. Alleles should be provided in the format A\*01:01, one per line.


The path to the file containing the somatic mutations in gz compressed VCF format.


Instead of genomic variants, peptide sequences can be provided in a TSV file. In this case, MHC binding predictions will be made for the provided sequences. The TSV file has to include the following columns: id, sequence. All additional columns will be added to the prediction output as annotation.

Additional pipeline parameters


Specifies that peptides should be filtered against the specified human proteome references. By default, this is turned off.


Specifies whether the predictions should be done for MHC class I or class II. By default, this is set to 1 (class I).


Specifies the minimum peptide length. By default, for MHC Class I this is 8 amino acids. For MHC Class II this is 15 amino acids.


Specifies the maximum peptide length. By default, for MHC Class I this is 11 amino acids. For MHC Class II this is by default 16
amino acids.


This defines against which reference genome the pipeline performs the analysis. The default choice is GRCh37, as most clinical labs still rely on GRCh37 as the human reference genome to use. Available are GRCh37 and GRCh38.


Specifies the reference proteome files that are used for self-filtering. Should be either a folder of FASTA files or a single FASTA file containing the reference proteome(s).


Specifies the set of tools used for performing prediction. Default is syfpeithi. Available are:

syfpeithi, mhcnuggets-class-1, mhcnuggets-class-2 and mhcflurry

You can use multiple options and concatenate these with a ,, e.g. syfpeithi,mhcflurry works fine.
Note that the FRED2 framework supports many more prediction methods, which we currently don't support due to legal restrictions in licencing of these methods (e.g. netMHCPan, netMHCpanII) that forbid any bundling in pipelines such as this one. We believe in open source and therefore dropped any support in an early alpha version of this pipeline due to this.


Specifies that wild-type sequences of mutated peptides should be predicted as well. By default, this is turned off.

Job resources

Automatic resubmission

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 an error code of 143 (exceeded requested resources) it will automatically resubmit with higher requests (2 x original, then 3 x original). If it still fails after three times then the pipeline is stopped.

Custom resource requests

Wherever process-specific requirements are set in the pipeline, the default value can be changed by creating a custom config file. See the files hosted at nf-core/configs for examples.

If you are likely to be running nf-core pipelines 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 (see definition below). 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.

If you have any questions or issues please send us a message on Slack.

Other command line parameters


The output directory where the results will be saved.


Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (~/.nextflow/config) then you don't need to specify this on the command line for every run.


This works exactly as with --email, except emails are only sent if the workflow is not successful.


Threshold size for MultiQC report to be attached in notification email. If file generated by pipeline exceeds the threshold, it will not be attached (Default: 25MB).


Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic.

This is used in the MultiQC report (if not default) and in the summary HTML / e-mail (always).

*NB:* Single hyphen (core Nextflow option)


Specify this when restarting a pipeline. Nextflow will used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously.

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.

*NB:* Single hyphen (core Nextflow option)


Specify the path to a specific config file (this is a core NextFlow command).

*NB:* Single hyphen (core Nextflow option)

Note - you can use this to override pipeline defaults.


Provide git commit id for custom Institutional configs hosted at nf-core/configs. This was implemented for reproducibility purposes. Default is set to master.

## Download and use config file with following git commid id  
--custom_config_version d52db660777c4bf36546ddb188ec530c3ada1b96  


If you're running offline, nextflow will not be able to fetch the institutional config files
from the internet. If you don't need them, then this is not a problem. If you do need them,
you should download the files from the repo and tell nextflow where to find them with the
custom_config_base option. For example:

## Download and unzip the config files  
cd /path/to/my/configs  
## Run the pipeline  
cd /path/to/my/data  
nextflow run /path/to/pipeline/ --custom_config_base /path/to/my/configs/configs-master/  

Note that the nf-core/tools helper package has a download command to download all required pipeline
files + singularity containers + institutional configs in one go for you, to make this process easier.


Use to set a top-limit for the default memory requirement for each process.
Should be a string in the format integer-unit. eg. --max_memory '8.GB'


Use to set a top-limit for the default time requirement for each process.
Should be a string in the format integer-unit. eg. --max_time '2.h'


Use to set a top-limit for the default CPU requirement for each process.
Should be a string in the format integer-unit. eg. --max_cpus 1


Set to receive plain-text e-mails instead of HTML formatted.


Set to disable colourful command line output and live life in monochrome.


Specify a path to a custom MultiQC configuration file.