Identify and quantify MHC eluted peptides from mass spectrometry raw data
You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a tab-separated file with 4 columns, and a header row as shown in the examples below.
Multiple runs of the same sample
sample identifiers have to be the same when you have multiple runs. The
Condition identifiers can be used to further distinguish the sample groups.
Below is an example for the same sample sequenced across 3 lanes:
The pipeline will auto-detect whether a sample is either in mzML, raw or Bruker’s tdf file format using the information provided in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 4 columns to match those defined in the table below.
A final samplesheet file consisting of both single- and paired-end data may look something like the one below.
|An incrementing value which acts as a unique number for the given sample
|Custom sample name. This entry will be identical for multiple MS runs from the same sample. Spaces in sample names are automatically converted to underscores (
|Additional information of the sample can be defined here.
|Full path to the MS outcome file. These files have the extentions “.raw”, “.mzML” or “.d”
An example samplesheet has been provided with the pipeline.
Rescoring using MS²Rescore
By default the pipline generates additional features using MS²PIP and DeepLC via the MS²Rescore framework (
--feature_generators deeplc,ms2pip). Additional feature generators can be added (
basic,deeplc,ionmob,maxquant,ms2pip) to boost identification rates and quality. Please make sure you provide the correct
Immuno-HCD). All available MS²PIP models can be found on GitHub.
MS²Rescore creates a comprehensive QC report of the added features used for rescoring. This report is only available if
--rescoring_engine mokapot is specified (default:
percolator). The report can be found in
<OUTDIR>/multiqc/ms2rescore. Further information on the tool itself can be read up in the published paper Declerq et al. 2022
Running the pipeline
The typical command for running the pipeline is as follows:
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:
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
json file via
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:
You can also generate such
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. 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:
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/mhcquant 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
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
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.
-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.
- A profile with a complete configuration for automated testing
- Includes links to test data so needs no other parameters
- A generic configuration profile to be used with Docker
- A generic configuration profile to be used with Singularity
- A generic configuration profile to be used with Podman
- A generic configuration profile to be used with Shifter
- A generic configuration profile to be used with Charliecloud
- A generic configuration profile to be used with Apptainer
- 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.
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.
NB: Single hyphen (core Nextflow option)
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.
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.
Azure Resource Requests
To be used with the
azurebatch profile by specifying the
We recommend providing a compute
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.
-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
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