nf-core/quantms
Quantitative mass spectrometry workflow. Currently supports proteomics experiments with complex experimental designs for DDA-LFQ, DDA-Isobaric and DIA-LFQ quantification.
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)
string
^\S+\.(?:tsv|sdrf)$
The output directory where the results will be saved.
string
./results
Email address for completion summary.
string
^([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.
string
Root folder in which the spectrum files specified in the SDRF/design are searched
string
Overwrite the file type/extension of the filename as specified in the SDRF/design
string
mzML
Proteomics data acquisition method
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.
string
^\S+\.(?:fasta|fa)$
Generate and append decoys to the given protein database
boolean
Pre- or suffix of decoy proteins in their accession
string
DECOY_
Location of the decoy marker string in the fasta
accession. Before (prefix) or after (suffix)
string
prefix
Choose the method to produce decoys from input target database.
string
Maximum nr. of attempts to lower the amino acid sequence identity between target and decoy for the shuffle algorithm
integer
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.
number
0.5
Debug level for DecoyDatabase step. Increase for verbose logging.
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
boolean
Perform peakpicking in memory
boolean
Which MS levels to pick as comma separated list. Leave empty for auto-detection.
string
Convert bruker .d files to mzML
boolean
Force initial re-indexing of input mzML files. Also fixes some common mistakes in slightly incomplete/outdated mzMLs. (Default: true for safety)
boolean
true
A comma separated list of search engines to use (and combine). Valid: comet, msgf, sage
string
comet
Number of sage processes to be spawned.
integer
1
The enzyme to be used for in-silico digestion, in ‘OpenMS format’
string
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
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.
integer
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
.
integer
5
Precursor mass tolerance unit used for database search. Possible values are ‘ppm’ (default) and ‘Da’.
string
Fragment mass tolerance used for database search. The default of 0.03 Da is for high-resolution instruments.
number
0.03
Fragment mass tolerance unit used for database search. Possible values are ‘ppm’ (default) and ‘Da’.
string
A comma-separated list of fixed modifications with their Unimod name to be searched during database search
string
Carbamidomethyl (C)
A comma-separated list of variable modifications with their Unimod name to be searched during database search
string
Oxidation (M)
The fragmentation method used during tandem MS. (MS/MS or MS2).
string
HCD
Comma-separated range of integers with allowed isotope peak errors for precursor tolerance (like MS-GF+ parameter ‘-ti’). E.g. -1,3
string
0,1
Type of instrument that generated the data. ‘low_res’ or ‘high_res’ (default; refers to LCQ and LTQ instruments)
string
high_res
MSGF only: Labeling or enrichment protocol used, if any. Default: automatic
string
automatic
Minimum precursor ion charge. Omit the ’+’
integer
2
Maximum precursor ion charge. Omit the ’+’
integer
4
Minimum peptide length to consider (works with MSGF and in newer Comet versions)
integer
6
Maximum peptide length to consider (works with MSGF and in newer Comet versions)
integer
40
Specify the maximum number of top peptide candidates per spectrum to be reported by the search engine. Default: 1
integer
1
Maximum number of modifications per peptide. If this value is large, the search may take very long.
integer
3
The minimum precursor m/z for the in silico library generation or library-free search
number
The maximum precursor m/z for the in silico library generation or library-free search
number
The minimum fragment m/z for the in silico library generation or library-free search
number
The maximum fragment m/z for the in silico library generation or library-free search
number
Debug level when running the database search. Logs become more verbose and at ‘>5’ temporary files are kept.
integer
Settings for calculating a localization probability with LucXor for modifications with multiple candidate amino acids in a peptide.
Turn the mechanism on.
boolean
Which variable modifications to use for scoring their localization.
string
Phospho (S),Phospho (T),Phospho (Y)
List of neutral losses to consider for mod. localization.
string
How much to add to an amino acid to make it a decoy for mod. localization.
number
List of neutral losses to consider for mod. localization from an internally generated decoy sequence.
string
Debug level for Luciphor step. Increase for verbose logging and keeping temp files.
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).
string
Should isoleucine and leucine be treated interchangeably when mapping search engine hits to the database? Default: true
boolean
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)
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
number
0.01
Debug level when running the IDFilter step. Increase for verbose logging
integer
Debug level when running the re-scoring. Logs become more verbose and at ‘>5’ temporary files are kept.
integer
Debug level when running the re-scoring. Logs become more verbose and at ‘>5’ temporary files are kept.
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’)?
string
The FDR cutoff to be used during training of the SVM.
number
0.05
The FDR cutoff to be used during testing of the SVM.
number
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
integer
300000
Retention time features are calculated as in Klammer et al. instead of with Elude. Default: false
boolean
Use additional features whose values are learnt by correct entries. See help text. Default: 0 = none
integer
Debug level for Percolator step. Increase for verbose logging
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
string
Debug level for IDPEP step. Increase for verbose logging
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.
string
Only use the top N hits per search engine and spectrum for combination. Default: 0 = all
integer
A threshold for the ratio of occurrence/similarity scores of a peptide in other runs, to be reported. See help.
number
Debug level for ConsensusID. Increase for verbose logging
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.
string
Allowed shift (left to right) in Th from the expected position
number
0.002
Minimum intensity of the precursor to be extracted
number
1
Minimum fraction of the total intensity. 0.0:1.0
number
Minimum intensity of the individual reporter ions to be extracted.
number
Maximum allowed deviation (in ppm) between theoretical and observed isotopic peaks of the precursor peak
number
10
Enable isotope correction (highly recommended)
boolean
true
Enable normalization of the channel intensities
boolean
The reference channel, e.g. for calculating ratios.
integer
126
Set the debug level
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
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’.
string
[Ignored in Bayesian] How to aggregate scores of peptides matching to the same protein
string
[Ignored in Bayesian] Also use shared peptides during score aggregation to protein level
boolean
true
[Ignored in Bayesian] Minimum number of peptides needed for a protein identification
integer
1
Consider only the top X PSMs per spectrum to find the best PSM per peptide. 0 considers all.
integer
1
[Bayesian-only; Experimental] Update PSM probabilities with their posteriors under consideration of the protein probabilities.
boolean
The experiment-wide protein (group)-level FDR cutoff. Default: 0.01
number
0.01
Use picked protein FDRs
boolean
true
The experiment-wide PSM-level FDR cutoff. Default: 0.01
number
0.01
Debug level for the protein inference step. Increase for verbose logging
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
string
Calculate protein abundance from this number of proteotypic peptides (most abundant first; ‘0’ for all, Default 3)
integer
3
Averaging method used to compute protein abundances from peptide abundances.
string
Distinguish between fraction and charge states of a peptide. (default: ‘false’)
boolean
Add the log2 ratios of the abundance values to the output.
boolean
false
Scale peptide abundances so that medians of all samples are equal.(Default false)
boolean
false
Use the same peptides for protein quantification across all samples.(Default false)
boolean
false
Include results for proteins with fewer proteotypic peptide than indicated by top.
boolean
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)
string
Export the results in mzTab format.
boolean
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.
string
Recalibrates masses based on precursor mass deviations to correct for instrument biases. (default: ‘false’)
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’)
boolean
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).
number
The minimum probability (e.g.: 0.75) an unidentified feature must have to be kept for alignment and linking (0=no filter).
number
The minimum intensity for a feature to be considered for quantification. (default: ‘10000’)
number
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.
string
Also quantify decoys? (Usually only needed for Triqler post-processing output with --add_triqler_output
, where it is auto-enabled)
boolean
Debug level when running the re-scoring. Logs become more verbose and at ‘>666’ potentially very large temporary files are kept.
integer
Settings for DIA-NN - a universal software for data-independent acquisition (DIA) proteomics data processing.
Choosing the MS2 mass accuracy setting automatically
boolean
true
Choosing scan_window setting automatically
boolean
true
Set the scan window radius to a specific value
integer
7
Only peaks with correlation sum exceeding min_corr will be considered
number
2
Peaks with correlation sum below corr_diff from maximum will not be considered
number
1
A single score will be used until RT alignment to save memory
boolean
true
Controls the protein inference mode
number
Instructs DIA-NN to add the organism identifier to the gene names
boolean
The spectral library to use for DIA-NN
string
Debug level
integer
Enable cross-run normalization between runs by diann.
boolean
true
Skip MSstats/MSstatsTMT for statistical post-processing?
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.
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
.
string
The threshold value for differential expressed proteins in MSstats plots based on adjusted p-value
number
0.05
Also create an output in Triqler’s format for an alternative manual post-processing with that tool
boolean
Which features to use for quantification per protein: ‘top3’ or ‘highQuality’ which removes outliers only
string
which summary method to use: ‘TMP’ (Tukey’s median polish) or ‘linear’ (linear mixed model)
string
Omit proteins with only one quantified feature?
boolean
true
Keep features with only one or two measurements across runs?
boolean
true
Use unique peptide for each protein
boolean
true
Remove the features that have 1 or 2 measurements within each run
boolean
true
select the feature with the largest summmation or maximal value
string
summarization methods to protein-level can be perfomed
string
Reference channel based normalization between MS runs on protein level data?
boolean
true
Remove ‘Norm’ channels from protein level data
boolean
true
Reference channel based normalization between MS runs on protein level data
boolean
true
Export MSstats profile QC plots including all proteins
boolean
Enable generation of pmultiqc report? default: ‘false’
boolean
Skip idXML files (do not generate search engine scores) in pmultiqc report? default: ‘true’
boolean
Parameters used to describe centralised config profiles. These should not be edited.
Git commit id for Institutional configs.
string
master
Base directory for Institutional configs.
string
https://raw.githubusercontent.com/nf-core/configs/master
Institutional config name.
string
Institutional config description.
string
Institutional config contact information.
string
Institutional config URL link.
string
Set the top limit for requested resources for any single job.
Maximum number of CPUs that can be requested for any single job.
integer
16
Maximum amount of memory that can be requested for any single job.
string
128.GB
^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$
Maximum amount of time that can be requested for any single job.
string
240.h
^(\d+\.?\s*(s|m|h|d|day)\s*)+$
Less common options for the pipeline, typically set in a config file.
Display help text.
boolean
Display version and exit.
boolean
Method used to save pipeline results to output directory.
string
Email address for completion summary, only when pipeline fails.
string
^([a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$
Send plain-text email instead of HTML.
boolean
File size limit when attaching MultiQC reports to summary emails.
string
25.MB
^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$
Do not use coloured log outputs.
boolean
Incoming hook URL for messaging service
string
Custom config file to supply to MultiQC.
string
Skip protein/peptide table plots with pmultiqc for large dataset.
boolean
Custom logo file to supply to MultiQC. File name must also be set in the MultiQC config file
string
Custom MultiQC yaml file containing HTML including a methods description.
string
Boolean whether to validate parameters against the schema at runtime
boolean
true
Show all params when using --help
boolean
Validation of parameters fails when an unrecognised parameter is found.
boolean
Validation of parameters in lenient more.
boolean