nf-core/proteomicslfq

URI/path to an SDRF file (with ending .sdrf or .sdrf.tsv) OR a tab-separated experimental design file (.tsv) in OpenMS’ own format. All input files need to be specified with full paths in the corresponding columns. Those can be any URIs or local paths with schemata supported by nextflow (e.g. http/ftp/s3)

The output directory where the results will be saved.

Email address for completion summary.

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

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

The fasta protein database used during database search.

Generate and append decoys to the given protein database

Pre- or suffix of decoy proteins in their accession

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

Choose the method to produce decoys from the input target database.

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

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 different amino acid sequence.

Activate OpenMS-internal peak picking

Perform peakpicking in memory

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

A comma separated list of search engines. Valid: comet, msgf

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

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

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

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.

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

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

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

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

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

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

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

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

Minimum precursor ion charge. Omit the ’+’

Maximum precursor ion charge. Omit the ’+’

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

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

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

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

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

Turn the mechanism on.

Which variable modifications to use for scoring their localization.

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

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)

FDR cutoff on PSM level (or potential peptide level; see Percolator options) before going into feature finding, map alignment and inference.

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

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

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

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

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

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

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

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.

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

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

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

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

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.

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

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. Only useful with multiple fraction groups/samples). ‘false’ turns this feature off. (default: ‘false’)

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’)

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.

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

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

Skip the MSstats Rscripts for automated statistical post-processing?

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

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

Omit proteins with only one quantified feature?

Keep features with only one or two measurements across runs?

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.

Allows full control over contrasts by specifying a set of contrasts in a semicolon seperated list of R-compatible limma-style contrasts with the condition names/numbers as variables (e.g. 1-2;1-3;2-3). Overwrites ‘—ref_condition Default is ‘pairwise’, a keyword to create all pairwise contrasts.

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

Enable generation of quality control report by PTXQC? default: ‘false’ since it is still unstable

Specify a yaml file for the report layout (see PTXQC documentation) (TODO not yet fully implemented)