nf-core/proteomicslfq
Proteomics label-free quantification (LFQ) analysis pipeline
22.10.6
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Learn more.
Define where the pipeline should find input data and save output data.
URI/path to an SDRF file OR globbing pattern for URIs/paths of mzML or Thermo RAW files
string
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})$
Less common options for the pipeline, typically set in a config file.
Display help text.
boolean
Method used to save pipeline results to output directory.
string
Workflow name.
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 QC reports to summary emails.
string
25 MB
Do not use coloured log outputs.
boolean
Directory to keep pipeline Nextflow logs and reports.
string
${params.outdir}/pipeline_info
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
Maximum amount of time that can be requested for any single job.
string
240.h
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 configs hostname.
string
Institutional config description.
string
Institutional config contact information.
string
Institutional config URL link.
string
In case your input was an SDRF files, the following optional parameters can be set.
Root folder in which the spectrum files specified in the SDRF are searched
string
Overwrite the file type/extension of the filename as specified in the SDRF
string
In case your input was a globbing pattern to spectrum files in Thermo RAW or mzML format you can specify a manual OpenMS-style experimental design file here.
A tab-separated experimental design file in OpenMS’ own format (TODO link). All input files need to be present as a row with exactly the same names. If no design is given, unrelated, unfractionated runs are assumed.
string
Settings that relate to the mandatory protein database and the optional generation of decoy entries.
The fasta
protein database used during database search.
string
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
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
A comma separated list of search engines. Valid: comet, msgf
string
comet
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 (MS-GF+ parameter ‘-ti’). E.g. -1,2
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
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
Do not fail if there are some unmatched peptides. Only activate as last resort, if you know that the rest of your settings are fine!
string
Should isoleucine and leucine be treated interchangeably when mapping search engine hits to the database? Default: true
string
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 potential peptide level; see Percolator options) before going into feature finding, map alignment and inference.
number
0.1
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. discriminability 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
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
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 occurence/similarity scores of a peptide in other runs, to be reported. See help.
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
The experiment-wide protein (group)-level FDR cutoff. Default: 0.05
number
0.05
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’ = 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)
string
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
Tries a targeted requantification in files where an ID is missing, based on aggregate properties (i.e. RT) of the features in other aligned files (e.g. ‘mean’ of RT). (WARNING: increased memory consumption and runtime). ‘false’ turns this feature off. (default: ‘false’)
string
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
Debug level when running the re-scoring. Logs become more verbose and at ‘>666’ potentially very large temporary files are kept.
integer
Skip MSstats for statistical post-processing?
boolean
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. (TODO not yet fully implemented)
string
Allows full control over contrasts by specifying a set of contrasts in a semicolon seperated list of R-compatible contrasts with the condition names/numbers as variables (e.g. 1-2;1-3;2-3
). Overwrites ‘—ref_condition’ (TODO not yet fully implemented)
string
Enable generation of quality control report by PTXQC? default: ‘false’ since it is still unstable
boolean
Specify a yaml file for the report layout (see PTXQC documentation) (TODO not yet fully implemented)
string