nf-core/epitopeprediction

Edit

Introduction

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 of a comma-separated file that consists of 3 columns and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Input Formats

The pipeline currently accepts three different types of input that are genomic variants, peptides and proteins.

Genomic variants

The supported file formats for genomic variants are .vcf, .vcf.gz.

tsv files with genomic variants have to provide the following columns:

start, end, #chr, ref, obs, gene, tumour_genotype, coding_and_splicing_details, variant_details, variant_type, coding_and_splicing
...
chr1 12954870 12954870 C T . 0 NORMAL:414,TUMOR:8 . missense_variant 0.5 transcript PRAMEF10 missense_variant PRAMEF10:ENST00000235347:missense_variant:MODERATE:exon3:c.413G>A:p.Cys138Tyr
...
 

For genomic variants, reference information from Ensembl BioMart is used. The default database version is the most recent GRCh37 version. If you want to do the predictions based on GRCh38 as the reference genome, please specify --genome_reference grch38 in your pipeline call.

You can also specify valid Ensembl BioMart archive version urls as --genome_reference value, e.g. the archive version of December 2021.

Peptide sequences

Peptide sequences have to be provided in tsv format with two mandatory columns id and sequence. Additional columns will be added as metadata to results.

Protein sequences

Protein input is supported in FASTA format.

Multiple runs of the same sample

The sample identifiers are used to determine which sample belongs to the input file. Below is an example for the same sample with different input files that can be used:

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,A*01:01;A*02:01;B*07:02;B*24:02;C*03:01;C*04:01,I,gbm_1_peptides.tsv
GBM_1,A*01:01;A*02:01;B*07:02;B*24:02;C*03:01;C*04:01,I,gbm_1_proteins.fasta

You can also perform predictions for multiple MHC classes (I, II and H-2) in the same run by specifying the value in the corresponding column (one value per row). Please make sure to select the alleles accordingly.

Full samplesheet

The pipeline accepts allele information in a file or as string in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 3 columns to match those defined in the table below.

A final samplesheet file consisting of both allele data and different input types of two samples may look something like the one below.

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
GBM_1,A*02:01;A*24:01;B*07:02;B*08:01;C*04:01;C*07:01,I,gbm_1_peptides.vcf
GBM_1,A*01:01;A*24:01;B*07:02;B*08:01;C*03:01;C*07:01,I,gbm_1_proteins.vcf
GBM_2,alleles.txt,I,gbm_2_variants.vcf

An example samplesheet has been provided with the pipeline.

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 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
<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 params.yaml containing:

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

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

Running the pipeline with external prediction tools

The pipeline can be used with external prediction tools that cannot be provided with the pipeline due to license restrictions.

Currently we do support prediction tools of the netMHC family. Please refer to the parameter docs for the list of supported tools. If one of the external tools is specified, the path to the corresponding tarball has to be specified. When using conda, the parameter --netmhc_system (if the default value linux is not applicable) must also be specified.

A typical command is as follows:

nextflow run nf-core/epitopeprediction --input samplesheet.csv -profile docker --tools netmhcpan-4.1 --netmhcpan_path /path/to/netMHCpan-4.1.Linux.tar.gz --outdir <OUTDIR>

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 a pipeline version when running the pipeline on your data, ensuring one specific version on your analysis, 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 re-use parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

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 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.

-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 input data, you may find that you want to customize 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.

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.

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 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'