Define where the pipeline should find input data and save output data.

URI/path to an SDRF file (.sdrf.tsv) OR OpenMS-style experimental design with paths to spectra files (.tsv)

required
type: string
pattern: ^\S+\.(?:tsv|sdrf)$

The output directory where the results will be saved.

required
type: string
default: ./results

Email address for completion summary.

type: string
pattern: ^([a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$

MultiQC report title. Printed as page header, used for filename if not otherwise specified.

type: string

Root folder in which the spectrum files specified in the SDRF/design are searched

type: string

Overwrite the file type/extension of the filename as specified in the SDRF/design

type: string
default: mzML

Proteomics data acquisition method

type: string

Settings that relate to the mandatory protein database and the optional generation of decoy entries. Note: Decoys for DIA will be created internally.

The fasta protein database used during database search. Note: For DIA data, it must not contain decoys.

required
type: string
pattern: ^\S+\.(?:fasta|fa)$

Generate and append decoys to the given protein database

type: boolean

Pre- or suffix of decoy proteins in their accession

type: string
default: DECOY_

Location of the decoy marker string in the fasta accession. Before (prefix) or after (suffix)

type: string
default: prefix

Choose the method to produce decoys from input target database.

type: string

Maximum nr. of attempts to lower the amino acid sequence identity between target and decoy for the shuffle algorithm

type: integer
default: 30

Target-decoy amino acid sequence identity threshold for the shuffle algorithm. if the sequence identity is above this threshold, shuffling is repeated. In case of repeated failure, individual amino acids are ‘mutated’ to produce a difference amino acid sequence.

type: number
default: 0.5

Debug level for DecoyDatabase step. Increase for verbose logging.

hidden
type: integer

In case you start from profile mode mzMLs or the internal preprocessing during conversion with the ThermoRawFileParser fails (e.g. due to new instrument types), preprocessing has to be performed with OpenMS. Use this section to configure.

Activate OpenMS-internal peak picking

type: boolean

Perform peakpicking in memory

type: boolean

Which MS levels to pick as comma separated list. Leave empty for auto-detection.

type: string

Convert bruker .d files to mzML

type: boolean

Force initial re-indexing of input mzML files. Also fixes some common mistakes in slightly incomplete/outdated mzMLs. (Default: true for safety)

type: boolean
default: true

A comma separated list of search engines to use (and combine). Valid: comet, msgf, sage

type: string
default: comet

Number of sage processes to be spawned.

type: integer
default: 1

The enzyme to be used for in-silico digestion, in ‘OpenMS format’

type: string
default: Trypsin

Specify the amount of termini matching the enzyme cutting rules for a peptide to be considered. Valid values are fully (default), semi, or none

type: string

Specify the maximum number of allowed missed enzyme cleavages in a peptide. The parameter is not applied if unspecific cleavage is specified as enzyme.

type: integer
default: 2

Precursor mass tolerance used for database search. For High-Resolution instruments a precursor mass tolerance value of 5 ppm is recommended (i.e. 5). See also --precursor_mass_tolerance_unit.

type: integer
default: 5

Precursor mass tolerance unit used for database search. Possible values are ‘ppm’ (default) and ‘Da’.

type: string

Fragment mass tolerance used for database search. The default of 0.03 Da is for high-resolution instruments.

type: number
default: 0.03

Fragment mass tolerance unit used for database search. Possible values are ‘ppm’ (default) and ‘Da’.

type: string

A comma-separated list of fixed modifications with their Unimod name to be searched during database search

type: string
default: Carbamidomethyl (C)

A comma-separated list of variable modifications with their Unimod name to be searched during database search

type: string
default: Oxidation (M)

The fragmentation method used during tandem MS. (MS/MS or MS2).

hidden
type: string
default: HCD

Comma-separated range of integers with allowed isotope peak errors for precursor tolerance (like MS-GF+ parameter ‘-ti’). E.g. -1,3

type: string
default: 0,1

Type of instrument that generated the data. ‘low_res’ or ‘high_res’ (default; refers to LCQ and LTQ instruments)

type: string
default: high_res

MSGF only: Labeling or enrichment protocol used, if any. Default: automatic

type: string
default: automatic

Minimum precursor ion charge. Omit the ’+’

type: integer
default: 2

Maximum precursor ion charge. Omit the ’+’

type: integer
default: 4

Minimum peptide length to consider (works with MSGF and in newer Comet versions)

type: integer
default: 6

Maximum peptide length to consider (works with MSGF and in newer Comet versions)

type: integer
default: 40

Specify the maximum number of top peptide candidates per spectrum to be reported by the search engine. Default: 1

type: integer
default: 1

Maximum number of modifications per peptide. If this value is large, the search may take very long.

type: integer
default: 3

The minimum precursor m/z for the in silico library generation or library-free search

type: number

The maximum precursor m/z for the in silico library generation or library-free search

type: number

The minimum fragment m/z for the in silico library generation or library-free search

type: number

The maximum fragment m/z for the in silico library generation or library-free search

type: number

Debug level when running the database search. Logs become more verbose and at ‘>5’ temporary files are kept.

hidden
type: integer

Settings for calculating a localization probability with LucXor for modifications with multiple candidate amino acids in a peptide.

Turn the mechanism on.

type: boolean

Which variable modifications to use for scoring their localization.

type: string
default: Phospho (S),Phospho (T),Phospho (Y)

List of neutral losses to consider for mod. localization.

hidden
type: string

How much to add to an amino acid to make it a decoy for mod. localization.

hidden
type: number

List of neutral losses to consider for mod. localization from an internally generated decoy sequence.

hidden
type: string

Debug level for Luciphor step. Increase for verbose logging and keeping temp files.

hidden
type: integer

What to do when peptides are found that do not follow a unified set of rules (since search engines sometimes differ in their interpretation of them).

type: string

Should isoleucine and leucine be treated interchangeably when mapping search engine hits to the database? Default: true

type: boolean
default: true

Choose between different rescoring/posterior probability calculation methods and set them up.

How to calculate posterior probabilities for PSMs:

  • ‘percolator’ = Re-score based on PSM-feature-based SVM and transform distance to hyperplane for posteriors
  • ‘fit_distributions’ = Fit positive and negative distributions to scores (similar to PeptideProphet)
type: string

FDR cutoff on PSM level (or peptide level; see Percolator options) per run before going into feature finding, map alignment and inference. This can be seen as a pre-filter. See

type: number
default: 0.01

Debug level when running the IDFilter step. Increase for verbose logging

hidden
type: integer

Debug level when running the re-scoring. Logs become more verbose and at ‘>5’ temporary files are kept.

hidden
type: integer

Debug level when running the re-scoring. Logs become more verbose and at ‘>5’ temporary files are kept.

hidden
type: integer

In the following you can find help for the Percolator specific options that are only used if --posterior_probabilities was set to ‘percolator’. Note that there are currently some restrictions to the original options of Percolator:

  • no Percolator protein FDR possible (currently OpenMS’ FDR is used on protein level)
  • no support for separate target and decoy databases (i.e. no min-max q-value calculation or target-decoy competition strategy)
  • no support for combined or experiment-wide peptide re-scoring. Currently search results per input file are submitted to Percolator independently.

Calculate FDR on PSM (‘psm-level-fdrs’) or peptide level (‘peptide-level-fdrs’)?

type: string

The FDR cutoff to be used during training of the SVM.

type: number
default: 0.05

The FDR cutoff to be used during testing of the SVM.

type: number
default: 0.05

Only train an SVM on a subset of PSMs, and use the resulting score vector to evaluate the other PSMs. Recommended when analyzing huge numbers (>1 million) of PSMs. When set to 0, all PSMs are used for training as normal. This is a runtime vs. quality tradeoff. Default: 300,000

type: integer
default: 300000

Retention time features are calculated as in Klammer et al. instead of with Elude. Default: false

hidden
type: boolean

Use additional features whose values are learnt by correct entries. See help text. Default: 0 = none

type: integer

Debug level for Percolator step. Increase for verbose logging

hidden
type: integer

Use this instead of Percolator if there are problems with Percolator (e.g. due to bad separation) or for performance

How to handle outliers during fitting:

  • ignore_iqr_outliers (default): ignore outliers outside of 3*IQR from Q1/Q3 for fitting
  • set_iqr_to_closest_valid: set IQR-based outliers to the last valid value for fitting
  • ignore_extreme_percentiles: ignore everything outside 99th and 1st percentile (also removes equal values like potential censored max values in XTandem)
  • none: do nothing
type: string

Debug level for IDPEP step. Increase for verbose logging

hidden
type: integer

How to combine the probabilities from the single search engines: best, combine using a sequence similarity-matrix (PEPMatrix), combine using shared ion count of peptides (PEPIons). See help for further info.

type: string

Only use the top N hits per search engine and spectrum for combination. Default: 0 = all

type: integer

A threshold for the ratio of occurrence/similarity scores of a peptide in other runs, to be reported. See help.

type: number

Debug level for ConsensusID. Increase for verbose logging

hidden
type: integer

Extracts and normalizes labeling information

Operate only on MSn scans where any of its precursors features a certain activation method. Set to empty to disable.

type: string

Allowed shift (left to right) in Th from the expected position

type: number
default: 0.002

Minimum intensity of the precursor to be extracted

type: number
default: 1

Minimum fraction of the total intensity. 0.0:1.0

type: number

Minimum intensity of the individual reporter ions to be extracted.

type: number

Maximum allowed deviation (in ppm) between theoretical and observed isotopic peaks of the precursor peak

type: number
default: 10

Enable isotope correction (highly recommended)

type: boolean
default: true

Enable normalization of the channel intensities

type: boolean

The reference channel, e.g. for calculating ratios.

type: integer
default: 126

Set the debug level

hidden
type: integer

Assigns protein/peptide identifications to features or consensus features. Here, features generated from isobaric reporter intensities of fragment spectra.

Debug level for IDMapper step. Increase for verbose logging

hidden
type: integer

To group proteins, calculate scores on the protein (group) level and to potentially modify associations from peptides to proteins.

The inference method to use. ‘aggregation’ (default) or ‘bayesian’.

type: string

[Ignored in Bayesian] How to aggregate scores of peptides matching to the same protein

type: string

[Ignored in Bayesian] Also use shared peptides during score aggregation to protein level

type: boolean
default: true

[Ignored in Bayesian] Minimum number of peptides needed for a protein identification

type: integer
default: 1

Consider only the top X PSMs per spectrum to find the best PSM per peptide. 0 considers all.

type: integer
default: 1

[Bayesian-only; Experimental] Update PSM probabilities with their posteriors under consideration of the protein probabilities.

hidden
type: boolean

The experiment-wide protein (group)-level FDR cutoff. Default: 0.01

type: number
default: 0.01

Use picked protein FDRs

type: boolean
default: true

The experiment-wide PSM-level FDR cutoff. Default: 0.01

type: number
default: 0.01

Debug level for the protein inference step. Increase for verbose logging

hidden
type: integer

General protein quantification settings for both LFQ and isobaric labelling.

Specify the labelling method that was used. Will be ignored if SDRF was given but is mandatory otherwise

type: string

Calculate protein abundance from this number of proteotypic peptides (most abundant first; ‘0’ for all, Default 3)

type: integer
default: 3

Averaging method used to compute protein abundances from peptide abundances.

type: string

Distinguish between fraction and charge states of a peptide. (default: ‘false’)

type: boolean

Add the log2 ratios of the abundance values to the output.

type: boolean
default: false

Scale peptide abundances so that medians of all samples are equal.(Default false)

type: boolean
default: false

Use the same peptides for protein quantification across all samples.(Default false)

type: boolean
default: false

Include results for proteins with fewer proteotypic peptide than indicated by top.

type: boolean
default: true

Quantify proteins based on:

  • ‘unique_peptides’ = use peptides mapping to single proteins or a group of indistinguishable proteins (according to the set of experimentally identified peptides)
  • ‘strictly_unique_peptides’ (only LFQ) = use peptides mapping to a unique single protein only
  • ‘shared_peptides’ = use shared peptides, too, but only greedily for its best group (by inference score and nr. of peptides)
type: string

Export the results in mzTab format.

type: boolean
default: true

Choose between feature-based quantification based on integrated MS1 signals (‘feature_intensity’; default) or spectral counting of PSMs (‘spectral_counting’). WARNING: ‘spectral_counting’ is not compatible with our MSstats step yet. MSstats will therefore be disabled automatically with that choice.

type: string

Recalibrates masses based on precursor mass deviations to correct for instrument biases. (default: ‘false’)

type: boolean

Only looks for quantifiable features at locations with an identified spectrum. Set to false to include unidentified features so they can be linked and matched to identified ones (= match between runs). (default: ‘true’)

type: boolean
default: true

The minimum probability (e.g.: 0.25) an identified (=id targeted) feature must have to be kept for alignment and linking (0=no filter).

type: number

The minimum probability (e.g.: 0.75) an unidentified feature must have to be kept for alignment and linking (0=no filter).

type: number

The minimum intensity for a feature to be considered for quantification. (default: ‘10000’)

type: number
default: 10000

The order in which maps are aligned. Star = all vs. the reference with most IDs (default). TreeGuided = an alignment tree is calculated first based on similarity measures of the IDs in the maps.

type: string

Also quantify decoys? (Usually only needed for Triqler post-processing output with --add_triqler_output, where it is auto-enabled)

type: boolean

Debug level when running the re-scoring. Logs become more verbose and at ‘>666’ potentially very large temporary files are kept.

hidden
type: integer

Settings for DIA-NN - a universal software for data-independent acquisition (DIA) proteomics data processing.

Choosing the MS2 mass accuracy setting automatically

type: boolean
default: true

Choosing scan_window setting automatically

type: boolean
default: true

Set the scan window radius to a specific value

type: integer
default: 7

Only peaks with correlation sum exceeding min_corr will be considered

type: number
default: 2

Peaks with correlation sum below corr_diff from maximum will not be considered

type: number
default: 1

A single score will be used until RT alignment to save memory

type: boolean
default: true

Controls the protein inference mode

type: number

Instructs DIA-NN to add the organism identifier to the gene names

type: boolean

The spectral library to use for DIA-NN

type: string

Debug level

hidden
type: integer

Enable cross-run normalization between runs by diann.

type: boolean
default: true

Skip MSstats/MSstatsTMT for statistical post-processing?

type: boolean

Experimental: Instead of all pairwise contrasts (default), uses the given condition name/number (corresponding to your experimental design) as a reference and creates pairwise contrasts against it.

type: string

Experimental: Allows full control over contrasts by specifying a set of contrasts in a semicolon separated list of R-compatible contrasts with the condition names/numbers as variables (e.g. 1-2;1-3;2-3). Overwrites --ref_condition.

type: string

The threshold value for differential expressed proteins in MSstats plots based on adjusted p-value

type: number
default: 0.05

Also create an output in Triqler’s format for an alternative manual post-processing with that tool

type: boolean

Which features to use for quantification per protein: ‘top3’ or ‘highQuality’ which removes outliers only

type: string

which summary method to use: ‘TMP’ (Tukey’s median polish) or ‘linear’ (linear mixed model)

type: string

Omit proteins with only one quantified feature?

type: boolean
default: true

Keep features with only one or two measurements across runs?

type: boolean
default: true

Use unique peptide for each protein

type: boolean
default: true

Remove the features that have 1 or 2 measurements within each run

type: boolean
default: true

select the feature with the largest summmation or maximal value

type: string

summarization methods to protein-level can be perfomed

type: string

Reference channel based normalization between MS runs on protein level data?

type: boolean
default: true

Remove ‘Norm’ channels from protein level data

type: boolean
default: true

Reference channel based normalization between MS runs on protein level data

type: boolean
default: true

Export MSstats profile QC plots including all proteins

type: boolean

Enable generation of pmultiqc report? default: ‘false’

type: boolean

Skip idXML files (do not generate search engine scores) in pmultiqc report? default: ‘true’

type: boolean

Parameters used to describe centralised config profiles. These should not be edited.

Git commit id for Institutional configs.

hidden
type: string
default: master

Base directory for Institutional configs.

hidden
type: string
default: https://raw.githubusercontent.com/nf-core/configs/master

Institutional config name.

hidden
type: string

Institutional config description.

hidden
type: string

Institutional config contact information.

hidden
type: string

Institutional config URL link.

hidden
type: string

Set the top limit for requested resources for any single job.

Maximum number of CPUs that can be requested for any single job.

hidden
type: integer
default: 16

Maximum amount of memory that can be requested for any single job.

hidden
type: string
default: 128.GB
pattern: ^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$

Maximum amount of time that can be requested for any single job.

hidden
type: string
default: 240.h
pattern: ^(\d+\.?\s*(s|m|h|d|day)\s*)+$

Less common options for the pipeline, typically set in a config file.

Display help text.

hidden
type: boolean

Display version and exit.

hidden
type: boolean

Method used to save pipeline results to output directory.

hidden
type: string

Email address for completion summary, only when pipeline fails.

hidden
type: string
pattern: ^([a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$

Send plain-text email instead of HTML.

hidden
type: boolean

File size limit when attaching MultiQC reports to summary emails.

hidden
type: string
default: 25.MB
pattern: ^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$

Do not use coloured log outputs.

hidden
type: boolean

Incoming hook URL for messaging service

hidden
type: string

Custom config file to supply to MultiQC.

hidden
type: string

Skip protein/peptide table plots with pmultiqc for large dataset.

type: boolean

Custom logo file to supply to MultiQC. File name must also be set in the MultiQC config file

hidden
type: string

Custom MultiQC yaml file containing HTML including a methods description.

type: string

Boolean whether to validate parameters against the schema at runtime

hidden
type: boolean
default: true

Show all params when using --help

hidden
type: boolean

Validation of parameters fails when an unrecognised parameter is found.

hidden
type: boolean

Validation of parameters in lenient more.

hidden
type: boolean