nf-core/macproqc: Usage¶

Please read this documentation on the nf-core website: https://mpc-bioinformatics.github.io/McQuaC/usage/¶

Documentation of pipeline parameters is generated automatically from the pipeline schema and can no longer be found in markdown files.

Introduction¶

This page describes how to prepare your input data and run the nf-core/macproqc pipeline. For general nf-core and Nextflow concepts (profiles, resource configuration, reproducibility) please also refer to the nf-core documentation.

Samplesheet input¶

You will need to create a samplesheet with information about the MS runs you would like to analyse. Use --input to specify its location. The file must be a comma-separated CSV with a header row.

--input '[path to samplesheet file]'

Samplesheet format¶

samplesheet.csv

sample,spectrum_file
msrunone,/path/to/data/CONTROL_REP1.raw
msruntwo,/path/to/data/CONTROL_REP2.d.tar.gz
msrunthree,/path/to/data/CONTROL_REP3.mzML.gz

Column	Description
`sample`	Custom sample name. Cannot contain spaces.
`spectrum_file`	Full path to the mass spectrometry data file. See supported formats in the table below.

Supported input formats¶

The pipeline auto-detects the instrument vendor and compression type from the file extension:

Extension	Vendor	Notes
`.raw`	Thermo Fisher	Converted via ThermoRawFileParser
`.d`	Bruker	Converted via tdf2mzml
`.mzML`	Any	Used directly, no conversion needed
`.[raw,d,mzML].[zip, tar.gz, tar.bz2]`	See above	Will be decompressed and processed as above

An example samplesheet has been provided with the pipeline.

Protein sequence database¶

The pipeline requires a protein sequence database in FASTA format for peptide identification. A single database is applied globally to all MS runs listed in the samplesheet.

--fasta '[path to FASTA file]'

Accepted file extensions: .fasta, .fa, .fas, .faa — optionally gzip-compressed (.gz).

Decoy database generation¶

By default, the pipeline appends reversed decoy sequences to the target database using the OpenMS DecoyDatabase tool, producing a combined target-decoy FASTA. This is required for FDR estimation during peptide identification.

Parameter	Default	Description
`--skip_decoy_generation`	`false`	Skip decoy generation. Use this when your FASTA already contains decoy sequences (labelled with the string defined by `--decoy_string`).
`--decoy_string`	`DECOY_`	String that is combined with the accession of the protein identifier to indicate a decoy protein. Used also later by tools to estimate the FDR.
`--decoy_method`	`reverse`	Method by which decoy sequences are generated from target sequences. Valid parameters are 'reverse' and 'shuffle'.
`--save_decoy_database`	`false`	Write the generated target-decoy FASTA to `<outdir>/decoy_database/`. Useful for archiving the exact database used in a run or for reuse in a subsequent run with `--skip_decoy_generation`.

[!TIP] If you run the pipeline repeatedly on the same dataset, generate the decoy database once with --save_decoy_database, then pass that file via --fasta and set --skip_decoy_generation in subsequent runs to avoid redundant processing.

Peptide identification¶

The pipeline searches spectra against the (target-decoy) database using Comet. Search parameters are configured through a combination of pipeline parameters and an optional Comet parameter template file.

Comet configuration template¶

Comet is configured via a .params file. The pipeline ships a built-in default template (assets/default_configs/comet.params) that is used when no custom file is provided. You can supply your own template to control settings not exposed as pipeline parameters (e.g. enzyme, missed cleavages, ion series):

--comet_config_template '/path/to/your/comet.params'

The template is adjusted at runtime: output format flags, mass tolerances, fragment ion settings, and modifications are overwritten with the values of the pipeline parameters listed below. All other settings in the template are preserved as-is.

Search parameters¶

The following parameters are overwritten in the Comet config at runtime:

Parameter	Default	Description
`--peptide_mass_tolerance_upper`	`5.0`	Upper bound of precursor mass tolerance (`peptide_mass_tolerance_upper`)
`--peptide_mass_tolerance_lower`	`-5.0`	Lower bound of precursor mass tolerance (`peptide_mass_tolerance_lower`). Usually negative.
`--peptide_mass_units`	`2`	Units for precursor mass tolerance: `0` = amu, `1` = mmu, `2` = ppm
`--isotope_error`	`2`	Isotope offset range: `0` = off, `1` = 0/1 (C13), `2` = 0/1/2, `3` = 0/1/2/3, `4` = -1/0/1/2/3, `5` = -1/0/1
`--fragment_bin_tol`	`0.02`	Fragment ion bin width in amu (`fragment_bin_tol`)
`--fragment_bin_offset`	`0.0`	Fragment ion bin offset, 0.0–1.0 (`fragment_bin_offset`)
`--theoretical_fragment_ions`	`0`	`0` = use flanking peaks, `1` = monoisotopic peak only
`--psms_per_spectrum`	`5`	Number of PSM candidates reported per spectrum (`num_output_lines`)

The pipeline always enforces the following settings regardless of what is set in the template:

Output format is set to mzIdentML (all other Comet output formats are disabled).
Internal decoy search is disabled (decoy_search = 0); decoys are handled externally by the pipeline.
max_duplicate_proteins = -1 to report all protein matches per PSM.
equal_I_and_L = 0 (isoleucine and leucine are treated as distinct).

Peptide modifications¶

Variable and static modifications can be set via pipeline parameters. The syntax matches the Comet params file format; multiple entries are separated by a semicolon. The modification string must be enclosed in single quotes when passed on the command line.

Parameter	Default	Description
`--variable_modifications`	`""`	Variable modifications to set in the Comet params file (`variable_modXX = ...` entries)
`--static_modifications`	`""`	Static modifications to set in the Comet params file (`add_XX_... = ...` entries)

[!TIP] When passing modification strings on the command line, wrap the entire value in double quotes and the Comet-format string in single quotes:
--variable_modifications "'variable_mod01 = 15.9949 M 0 3 -1 0 0 0.0;variable_mod02 = 79.966331 STY 0 3 -1 0 0 97.976896'"
--static_modifications "'add_C_cysteine = 57.021464'"
Only modifications already present in the template can be overwritten this way. To add new modification slots, provide a custom --comet_config_template.

Running the pipeline¶

The typical command for running the pipeline is as follows:

nextflow run nf-core/macproqc \
   --input ./samplesheet.csv \
   --outdir ./results \
   -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 the specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other Nextflow hidden files, e.g. history of pipeline runs and old logs

Params file¶

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify them in a YAML or JSON params file via -params-file <file>.

[!WARNING] 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).

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

with params.yaml:

params.yaml

input: "./samplesheet.csv"
outdir: "./results/"

You can also generate parameter 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 pipeline version, regularly update the cached version:

nextflow pull nf-core/macproqc

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.

First, go to the nf-core/macproqc releases page and find the latest pipeline version — numeric only (e.g. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) — e.g. -r 1.3.1.

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. 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.

[!TIP] 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¶

[!NOTE] 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, Apptainer, Conda) — see below.

[!IMPORTANT] 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 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
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 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. 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 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.

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'