Usage

Samplesheet input

Create a samplesheet before you run the pipeline. The samplesheet is a comma-separated file with a header row and one row for each input file. Use the --input parameter to specify its location:

--input '[path to samplesheet file]'

Samplesheet columns

The pipeline supports three input types:

Example samplesheet

One sample can have several rows, for example with different input files or MHC classes:

sample,alleles,mhc_class,filename
GBM_1,A*01:01;A*02:01;B*07:02;B*24:02;C*03:01;C*04:01,I,gbm_1_variants.vcf.gz
GBM_1,gbm_1_alleles.txt,I,gbm_1_proteins.fasta
GBM_1,DRB1*01:01,II,gbm_1_peptides.tsv

An example samplesheet is included with the pipeline.

Alleles

You can write alleles in different nomenclatures, for example A*01:01 or HLA-A*01:01. The pipeline uses mhcgnomes to convert them to one format. It truncates 3- and 4-field typings to 2 fields.

Make sure that the alleles match the MHC class of the row.

Pan-species prediction

To predict against all supported alleles of a species, write <species>-all in the alleles column. The pipeline then uses all alleles that each prediction tool supports for that species and MHC class.

sample,alleles,mhc_class,filename
sample1,HLA-all,I,peptides.tsv

Peptide lengths

--min_peptide_length_classI, --max_peptide_length_classI, --min_peptide_length_classII and --max_peptide_length_classII set the peptide lengths to predict. Each prediction tool also has a fixed length range. A tool gets a peptide only if the length is in both ranges.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/epitopeprediction --input ./samplesheet.csv --outdir ./results -profile docker

This command uses the docker profile and the default prediction tool, mhcnuggets. Use --tools to select other tools, for example --tools mhcflurry,mhcnuggets. 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.

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>.

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

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

with:

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

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

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. After this, it will use the cached version if available - even if the pipeline has been updated since. To ensure 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

Reproducibility

It is a good idea to specify the 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 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 reproducibility, you can use share and reuse parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Variant input

Give raw somatic variant calls. Do not annotate the VCF in advance, because the pipeline runs VEP itself.

The pipeline prepares each VCF as follows:

1. It keeps only records with FILTER PASS.
2. It renames contigs to Ensembl style, for example chr1 to 1 and chrM to MT.
3. It splits multiallelic sites into one record per allele.

The pipeline also accepts VCFs without a GT field, for example from Strelka.

Multi-sample VCFs

Some VCFs have more than one sample column, for example tumor/normal calls from Mutect2, Strelka or DRAGEN. For these VCFs, write the name of the tumor sample in the tumor_sample column. To show the sample names, run bcftools query -l your.vcf. Strelka names the samples NORMAL and TUMOR.

Leave tumor_sample empty for single-sample VCFs.

Which variants give peptides

The pipeline makes peptides from these variant types:

• Missense variants
• In-frame insertions and deletions
• Frameshifts

Other variant types do not give peptides, for example synonymous, stop-gain, stop-loss, splice and non-coding variants.

The pipeline uses only protein-coding transcripts with a complete coding sequence. If a variant is on complete and incomplete transcripts, the pipeline uses only the complete transcripts.

Nearby somatic variants

Two somatic missense variants can be close enough to occur in the same peptide. The pipeline assumes that such variants are on the same chromosome copy. It then makes peptides with both variants, in addition to the peptides with each variant alone. If the VCF contains read-based phasing (FORMAT/HP), the pipeline uses this phasing instead.

Germline context

The pipeline takes the sequence around a variant from the reference genome. If the patient has a germline variant near the somatic variant, the reference sequence is not the sequence of the patient.

To correct this, give the germline VCF of the patient in the germline_vcf column. nf-core/sarek and comparable pipelines write one germline VCF for each normal sample. The pipeline then applies the germline variants to the wild-type and the mutant sequence.

Germline variants give no peptides of their own. The pipeline uses only germline missense variants near a somatic variant, because pVACseq supports only these.

The pipeline reports each peptide with and without the germline variant. You therefore keep all peptides if the two variants are on different chromosome copies.

Self-filtering

Some mutant peptides also occur in other normal proteins. To remove these peptides, give a reference proteome with --proteome_reference, for example a UniProt or Ensembl pep.all.fa file.

Reference data

Variant input needs a VEP cache and a genome FASTA of the same assembly. You do not need to give VEP plugins. The pipeline takes the Wildtype and Frameshift plugins from the pVACtools container.

You can get the reference data in three ways.

Option 1: annotation-cache

The annotation-cache bucket contains VEP caches for many species. You do not need to download them:

nextflow run nf-core/epitopeprediction -profile docker \
  --input samplesheet.csv --outdir results \
  --vep_species mus_musculus --vep_genome GRCm39 --vep_cache_version 116 \
  --vep_cache s3://annotation-cache/vep_cache/116_GRCm39/ \
  --ref_fasta <genome.fa>

To read the bucket without AWS credentials, add aws.client.anonymous = true to your config.

Option 2: helper script

If annotation-cache does not have your species or release, assets/download_vep_references.sh downloads the cache and the FASTA from Ensembl:

SPECIES=mus_musculus ASSEMBLY=GRCm39 RELEASE=116 assets/download_vep_references.sh

Option 3: download in the pipeline

With --vep_download_cache, the pipeline downloads the cache and the FASTA itself. It writes both to <outdir>/references. The download is approximately 20 GB. Do it once and then give the files with --vep_cache and --ref_fasta.

nextflow run nf-core/epitopeprediction -profile docker \
  --input samplesheet.csv --outdir results \
  --vep_species homo_sapiens --vep_genome GRCh38 --vep_cache_version 116 \
  --vep_download_cache

VEP uses FTP to find the available caches. If passive FTP does not work on your network, the download stops with “No matching species found”. In this case, use option 1 or 2.

Prediction tools

NetMHCpan and NetMHCIIpan

The pipeline supports NetMHCpan 4.2 and NetMHCIIpan 4.3, including their sub-releases (for example 4.2b or 4.3i). DTU distributes these tools under its own license, so the pipeline does not include them. Download the Linux tarballs from NetMHCpan-4.2 and NetMHCIIpan-4.3. Then give their paths with --netmhcpan_path and --netmhciipan_path.

The pipeline checks that each tarball contains the expected tool and version. It writes the exact sub-release to the software versions.

A typical command is as follows:

nextflow run nf-core/epitopeprediction \
  -profile docker \
  --input ./samplesheet.csv \
  --outdir ./results \
  --tools 'netmhcpan,netmhciipan' \
  --min_peptide_length_classI 8 \
  --max_peptide_length_classI 12 \
  --min_peptide_length_classII 12 \
  --max_peptide_length_classII 25 \
  --netmhcpan_path /path/to/netMHCpan-4.2bstatic.Linux.tar.gz \
  --netmhciipan_path /path/to/netMHCIIpan-4.3i.Linux.tar.gz

NetMHCpan 4.2 has three prediction modes. We recommend the default mode. To select a different mode, add -mode to the tool arguments in a custom config file:

process {
    withName: NETMHCPAN {
        ext.args = '-BA -mode 2'
    }
}

Keep -BA in the arguments. The pipeline needs it to report binding affinities. In modes 1 and 2, NetMHCpan does not report a binding affinity score, so the BA column is empty.

MixMHCpred and MixMHC2pred

The pipeline supports MixMHCpred (mixmhcpred) for MHC class I and MixMHC2pred (mixmhciipred) for MHC class II.

The licenses do not allow a prebuilt container. Add -with-wave to the command, and Wave builds the container from the module Dockerfile. Do not use -profile wave for these tools. This profile enables Wave freeze mode, which fails without a private build repository.

You can also build the containers yourself:

1. Build the images from modules/local/mixmhcpred/Dockerfile and modules/local/mixmhciipred/Dockerfile.
2. Keep the images private. The licenses do not allow redistribution.
3. Give the images in a custom config file with -c. Use fully qualified image names, because the pipeline adds quay.io/ to names without a registry.

process {
    withName: 'MIXMHCPRED' {
        container = 'registry.example.org/mixmhcpred:3.0'
    }
    withName: 'MIXMHCIIPRED' {
        container = 'registry.example.org/mixmhc2pred:2.0.2'
    }
}

A typical command for MHC class I is:

nextflow run nf-core/epitopeprediction \
  -profile docker \
  -with-wave \
  --input ./samplesheet.csv \
  --outdir ./results \
  --tools 'mixmhcpred' \
  --accept_mixmhcpred_license \
  --min_peptide_length_classI 8 \
  --max_peptide_length_classI 12

For MHC class II, use --tools 'mixmhciipred' and set --min_peptide_length_classII and --max_peptide_length_classII to values between 12 and 21.

Core Nextflow arguments

-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, Apptainer, Conda) - see below.

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 check if your system is supported, 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 environment.

• 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
• wave
  • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
• 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.

-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 pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (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 customise process resources section of the nf-core website.

Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps 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.

nf-core/configs

In most cases, you will need to create a custom config as a one-off but if you, and others within your organization, are likely to be running nf-core pipelines regularly and need to use the same settings regularly then we can advise that you request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this, test that the config file works with your pipeline of choice using the -c parameter. Then you can 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.

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 a 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'