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OpenMS
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Complete quantification workflow for MS1-labeled (SILAC, Dimethyl, ...) LC-MS/MS experiments.
| pot. predecessor tools | → MS1LabeledWorkflow → | pot. successor tools |
|---|---|---|
| PercolatorAdapter | MzTabExporter | |
| IDFilter |
This tool runs the complete quantification of an experiment whose samples were labeled before the LC-MS measurement so that the light and heavy forms of a peptide appear as separate MS1 features with a fixed mass shift: SILAC (Lys4/Lys6/Lys8, Arg6/Arg10), Dimethyl and ICPL labeling, in duplex, triplex or higher plex. It is the MS1-labeling counterpart of ProteomicsLFQ (label-free) and IsobaricWorkflow (TMT / iTRAQ reporter ions) and combines the standalone tools FeatureFinderMultiplex, IDMapper, IDConflictResolver, MultiplexResolver, MapAlignerIdentification, FeatureLinkerUnlabeledQT, ProteinInference and ProteinQuantifier into one run.
Input
in), with unique basenames. Native RAW input requires a build with WITH_THERMO_RAW and a .NET 8+ runtime. MS1 peaks are used for quantification; spectrum metadata are also read to recover identification FAIMS CVs. Profile and centroided data are both accepted (see algorithm:spectrum_type).ids), one file per spectra file in the same order, already filtered at PSM level (e.g. q-value < 0.01) and carrying Posterior Error Probability scores, e.g. produced with PercolatorAdapter (-score_type pep) or IDPosteriorErrorProbability, followed by IDFilter. Pass the results of one search engine rather than concatenating those of several.labels), in the syntax of FeatureFinderMultiplex, e.g. [][Lys8,Arg10] for SILAC, [][Lys4,Arg6][Lys8,Arg10] for triple SILAC, [Dimethyl0][Dimethyl6] for Dimethyl. Every bracket is one channel; the channels are numbered from 1 in this order and are the Label column of the experimental design. Channels must be in increasing mass order, and SILAC requires an explicit unlabelled first channel [].design) with the columns Fraction_Group, Fraction, Spectra_Filepath, Label and Sample (see ExperimentalDesign). One row per (file, channel). Without a design every file is an unfractionated fraction group and every (file, channel) is its own sample.fasta). The identifications are then re-indexed with PeptideIndexer, which annotates protein sequences (for coverage), the decoy status and the theoretical peptide uniqueness needed by -protein_quantification strictly_unique_peptides.FAIMS multiplets, identification mapping, blacklist checks and linking are restricted to the same compensation voltage. CVs share the physical run's RT alignment and sample columns; they contribute separate multiplet evidence to peptide quantification. Identification CVs are recovered from the original spectrum references (including the preceding acquisition CV when MS2 metadata omit it). In a multi-CV run, an ID without a resolvable spectrum reference must carry a valid FAIMS_CV annotation; it cannot be assigned using RT alone. A single-CV run allows an unambiguous fallback to that voltage. Missing references are repaired only against MS2 spectra at the known CV, within 0.01 seconds of the ID's RT.
Important: the labels have to be part of the database search as (variable) modifications, e.g. Label:13C(6)15N(2) (K) and Label:13C(6)15N(4) (R) for Lys8/Arg10. Otherwise the MS2 spectra of the labeled channels stay unidentified and the observed mass shifts cannot be reconciled with the peptide sequences, so most multiplets end up as conflicts. The tool checks the search parameters recorded in ids for the modifications implied by labels and refuses to run if they are missing (use -force to proceed anyway).
Workflow
algorithm and label_mass_shifts). By default (algorithm:knock_out) partial multiplets are detected as well: a peptide seen in the heavy channel only, or whose weaker channels do not pass the detection's isotope pattern and intensity filters. A partial multiplet that merely flanks a larger multiplet of the same peptide (its channels at the same m/z, eluting next to it) is merged into the larger one.id_mapping). An identification matches a multiplet when it lies within the elution region of one of its channels (the RT range of its mass traces, plus id_mapping:rt_tolerance) and its m/z matches the channel - by default the theoretical m/z of the identified peptidoform, which carries the label, since the precursor m/z can be that of another isotopic peak. An identification of a channel the detection did not find (too weak) joins a partial multiplet detected in the peptide's other channels, at the m/z the peptide has there; its own channel is then re-quantified. With join_channels (default), the channels of an identified peptide that the detection found as separate partial multiplets (no spectrum held its complete pattern) are joined into one multiplet, and re-measured against its most intense channel from paired peaks over their common elution region, because each partial multiplet was quantified over other spectra.resolver): multiplets whose mass shifts contradict the labels found in the annotated sequence are removed from quantification (their identifications are kept for protein inference), partial multiplets are completed with dummy features (intensity 0 = absent, NaN = not quantifiable, i.e. another detected multiplet overlaps). With requantify (default), a missing channel is then measured at its expected position over the elution region of the multiplet, like MaxQuant's re-quantify, and reported as absent only if it shows no signal there (its first two isotopes are not seen together in at least two spectra), and otherwise as not quantifiable if its peaks there do not show its isotope pattern in at least two spectra. The detection records which feature reserved each peak: only peaks reserved by another multiplet (also one of the same peptide, which is quantified on its own) make such a channel not quantifiable without measuring it, and of the reserved peaks it uses only those of patterns the detection dropped, not the isotopes of the multiplet's other channels; a channel that only the peaks it may not use would give a value is not quantifiable either, and so is a weak one (below requantify_correlation_ratio times the reference) whose elution profile correlates with the reference's by less than requantify_min_correlation (another species at its position; at moderate ratios real re-quantified channels correlate less well and are not checked). Re-quantified values are less accurate at extreme ratios than detected ones, so the ratios count the evidences that rest on them (see Ratios below). With targeted_quantification, an identified peptide that has no multiplet in the run (the detection missed it, or no multiplet fits its identifications) is measured at the m/z of its channels over the elution peak at its identification: the identified channel is integrated (if its isotope pattern fits the peptide), the other channels are measured against it from paired peaks (a channel is absent only if no peak is seen at its m/z). Such a feature and its identifications carry the meta value quantification_method = targeted. Its values are less accurate at extreme ratios than those of a detected multiplet, so they only add ratios and never change one that is reported without them (see Ratios below), and one whose other channel is not quantifiable is left out of the abundances. They take no part in the alignment, and they are linked among themselves only, after the detected multiplets, so that alignment, linking and match between runs stay as they are without them. One exception, with match_between_runs: a targeted feature replaces the unidentified multiplet kept for match between runs that it is measured from, so that run gets the targeted value instead of the one that linking would have given the multiplet (and that value no longer enters the run's normalization factors). Multiplets without identification are dropped, unless match_between_runs is set (see below). Once the resolver has used the labels, every identification is reduced to the peptide identity: the label modifications of labels are removed from the sequence, because the label belongs to the channel, not to the peptide, and the light and the heavy spectrum of one peptide have to name one peptide for linking, match between runs, inference and quantification (the convention MaxQuant uses as well). The label state stays documented on every identification as the meta values MS1Label:labeled_sequence (the peptidoform as searched), MS1Label:removed_labels (e.g. Lys8, or none) and MS1Label:channel (the 1-based channel the spectrum belongs to, i.e. the Label of the experimental design). The values also sit on every quantified consensus feature, for the identification it is quantified under. mzTab reports them as opt_global_* columns of the peptide and PSM sections, the QPX feature and psm views as cv_params. PSM-level output describes the spectrum match and therefore reports the peptidoform as searched (mzTab PSM section, QPX psm view), feature-level output the peptide identity (see OpenMS::MS1LabelState). The column headers describe every channel's labels in channel_description. A spectrum match that was mapped onto several multiplets stays on the one whose matched channel is closest to the identified peptidoform in m/z; distinct spectra of one peptide on distinct multiplets are all kept, and their channel values add up per peptide and charge in the quantification.alignment, identification-based, aligned to the run that shares the most identifications with every other run) and linking of the multiplets across runs (linking); the channels of every run are kept as sub-features, so the linked map has one column per (run, channel). Fractions are linked separately and then combined column-wise, exactly like ProteomicsLFQ does; a fraction measured in a single run is passed through. With match_between_runs, unidentified complete multiplets take part in the linking and take over the identification of a multiplet at the same position in another run (the SILAC equivalent of ProteomicsLFQ's -targeted_only false); multiplets that stay unidentified are not quantified. Unidentified partial multiplets are not kept: only an identification tells which channels they are.protein_inference), protein (and optionally PSM/peptide) FDR filtering, and peptide and protein quantification (ProteinQuantification), where the fractions of a fraction group are aggregated according to the design.Ratios. A labeled experiment measures its channels in one run, so its quantity is their ratio, and the tool computes it the way MaxQuant does, as a median of ratios rather than as a ratio of aggregated intensities (ratios, computed by MS1LabeledRatioQuantifier next to this tool):
ratios:reference_channel, the light one by default). Only positive, finite channels take part: an absent (dummy, intensity 0) or not-quantifiable channel is no measurement of a ratio.ratios:min_ratio_count peptides upwards (MaxQuant's "min. ratio count", 2 by default), with the number of contributing peptides next to it (and, with targeted_quantification, the number of those from targeted quantification). Every ratio is also reported divided by the median peptide ratio of its (fraction group, channel), i.e. normalized on the assumption that most peptides do not change.targeted_quantification only stand in where nothing else was measured: a peptide ratio uses them only if the peptide has no other evidence of that channel in that fraction group, a protein group ratio only if its other peptide ratios do not reach ratios:min_ratio_count, and the normalization factors only if a (fraction group, channel) has no other peptide ratio.The reference channel is reported with the ratio 1.0 it has by construction, wherever another channel was measured against it, so that every annotation covers the complete set of channels.
The ratios are annotated on the consensus features (MS1Label:evidence_ratio*, MS1Label:peptide_ratio*) and on the protein groups, so they reach the consensusXML and the mzTab peptide section (as opt_global_* columns). In the QPX pg view, whose rows are one per (protein group, fraction group, channel), they are written as that row's additional_intensities, named ratio and ratio_normalized under the row's own channel label; the number of contributing peptides sits in cv_params as ratio_count, being a count rather than an intensity, with targeted_quantification next to the number of those from targeted quantification (ratio_targeted_count, MS1Label:peptide_ratio_targeted_count on the features, fraction_group_level_ratio_targeted_count on the protein groups). Where requantify measured channels, the number of contributing ratios that rest on them is reported as well (ratio_requantified_count, MS1Label:peptide_ratio_requantified_count: the evidences whose channel or reference channel was re-quantified; fraction_group_level_ratio_requantified_count: the peptide ratios all of whose evidences were), so that they can be filtered at extreme ratios. No separate ProteinQuantifier run is needed for any of it.
Next to the ratios, the per-channel abundances are reported as before (mzTab peptide and protein sections, QPX intensities): the summed peptide intensities per channel, like MaxQuant's Intensity columns. Dividing two of those is a ratio of aggregates, a different statistic from the ratios above, which weights peptides by their intensity. ProteinQuantification:consensus:normalize scales every assay to the overall median, which for a labeled experiment forces the median channel ratio to 1; leave it off unless that is intended.
max_nr_labelled_aas is used for both the feature detection and the resolver: it is the maximum number of labelled amino acids per peptide minus one, i.e. for tryptic SILAC the number of allowed missed cleavages. It should agree with the missed-cleavage setting of the search.
Output (at least one required; each output is optional individually)
out_cxml)out)out_qpx): quantms.feature.parquet, quantms.psm.parquet, quantms.pg.parquet. The channels are reported with the canonical SDRF/QPX labels (SILAC light, SILAC medium, SILAC heavy, DIMETHYL0, ...). Labels outside this vocabulary (ICPL, Leu3, plain mass shifts) cannot be exported to QPX; the tool refuses out_qpx for them up front.The command line parameters of this tool are:
MS1LabeledWorkflow -- Quantification workflow for MS1-labeled (SILAC, Dimethyl, ...) LC-MS/MS experiments.
Full documentation: http://www.openms.de/doxygen/nightly/html/TOPP_MS1LabeledWorkflow.html
Version: 3.7.0-pre-nightly-2026-11-09 Nov 10 2026, 01:18:21, Revision: 0707508
To cite OpenMS:
+ Pfeuffer, J., Bielow, C., Wein, S. et al.. OpenMS 3 enables reproducible analysis of large-scale mass spec
trometry data. Nat Methods (2024). doi:10.1038/s41592-024-02197-7.
Usage:
MS1LabeledWorkflow <options>
Options (mandatory options marked with '*'):
-in <file list>* Input: spectra files (mzML, or Thermo RAW with WITH_TH
ERMO_RAW and .NET 8+), one per LC-MS run. Only MS1
peaks are used; profile and centroided data are accept
ed. (valid formats: 'mzML', 'raw')
-ids <file list>* Identifications filtered at PSM level (e.g., q-value
< 0.01), one per spectra file in the same order.
The identifications must carry Posterior Error Probabi
lity scores (e.g. PercolatorAdapter with -score_type
pep,
or IDPosteriorErrorProbability) and the labels must
have been searched as (variable) modifications.
Pass the results of one search engine rather than conc
atenating those of several. (valid formats: 'idXML',
'mzId', 'idparquet')
-design <file> Experimental design (Fraction_Group, Fraction, Spectra
_Filepath, Label, Sample), one row per (file, channel)
.
'Label' is the 1-based position of the channel in '-la
bels'. If not given, every file is an unfractionated
fraction group and every (file, channel) is a separate
sample. (valid formats: 'tsv')
-fasta <file> Protein database. If given, the identifications are
re-indexed (PeptideIndexer): protein sequences (for
coverage),
decoy annotation and theoretical peptide uniqueness
(needed by 'strictly_unique_peptides') are taken from
it. (valid formats: 'fasta', 'fa', 'faa')
-labels <text> Labels used for labelling the samples, one bracket
per channel. [...] specifies the labels for a single
sample. For example
[][Lys8,Arg10] ... SILAC
[][Lys4,Arg6][Lys8,Arg10] ... triple-SILAC
[Dimethyl0][Dimethyl6] ... Dimethyl
[Dimethyl0][Dimethyl4][Dimethyl8] ... triple
...
' column of the experimental design). (default: '[][Ly
s8,Arg10]')
-max_nr_labelled_aas <int> Maximum number of labelled amino acids per peptide,
minus one. Peptides with up to (this value + 1) labell
ed amino acids
are considered by feature detection and resolver. For
SILAC with trypsin digestion, this is the maximum numb
er of missed cleavages. (default: '0') (min: '0')
-out <file> Optional output mzTab file. At least one output must
be specified. (valid formats: 'mzTab')
-out_cxml <file> Optional output consensusXML file. At least one output
must be specified. (valid formats: 'consensusXML')
-out_qpx <directory> Optional output directory for QPX Parquet files (quant
ms.feature.parquet, quantms.psm.parquet, quantms.pg.pa
rquet). At least one output must be specified.
-proteinFDR <threshold> Protein FDR threshold (0.05=5%). (default: '0.05')
(min: '0.0' max: '1.0')
-picked_proteinFDR <choice> Use a picked protein FDR? (default: 'false') (valid:
'true', 'false')
-psmFDR <threshold> FDR threshold for sub-protein level (e.g. 0.05=5%).
Use -FDR_type to choose the level. Cutoff is applied
at the highest level. (default: '1.0') (min: '0.0'
max: '1.0')
-FDR_type <option> Sub-protein FDR level. PSM, PSM+peptide (best PSM q-va
lue). (default: 'PSM') (valid: 'PSM', 'PSM+peptide')
-protein_inference <option> Infer proteins:
aggregation = aggregates all peptide scores across a
protein (using the best score)
bayesian = computes a posterior probability for
every protein based on a Bayesian network. (default:
'aggregation') (valid: 'aggregation', 'bayesian')
-match_between_runs <option> True: keep multiplets without an identification, so
that linking can hand them the identification of a
multiplet at the
same position in another run (the counterpart of Prote
omicsLFQ's '-targeted_only false').
false: only identified multiplets are quantified.
Only complete unidentified multiplets are kept: the
channels of a partial one (see 'algorithm:knock_out')
are only known from its identification. (default: 'fal
se') (valid: 'true', 'false')
-requantify <option> True: a channel of an identified multiplet that the
detection did not find (see 'algorithm:knock_out') is
re-quantified from the
MS1 peaks at its expected position over the elution
region of the multiplet, relative to the most intense
detected channel (like
MaxQuant's 're-quantify'). It is reported as absent
(0) only if its first two isotopic traces are not seen
...
false: such channels are reported as absent. (default:
'true') (valid: 'true', 'false')
-join_channels <option> True: when the detection found the channels of an iden
tified peptide as separate partial multiplets (see
'algorithm:knock_out'),
they are joined into one multiplet: partial multiplets
at the m/z the peptide has in its missing channels
(within 'algorithm:mz_tolerance'),
of the same charge and CV, eluting at the identificati
on and identified as the same peptide or not at all.
...
then not quantifiable). (default: 'true') (valid: 'tru
e', 'false')
-targeted_quantification <option> True: an identified peptide that has no multiplet in
the run (none was detected, or its identifications
fit none) is quantified from the MS1 peaks at
the m/z it has in every channel: over the elution peak
at the identification, the identified channel is inte
grated, and every other channel is
measured against it from paired peaks (absent if no
peak is seen at its m/z, not quantifiable if another
...
ence only. (default: 'true') (valid: 'true', 'false')
Parameters of the multiplet detection (FeatureFinderMultiplex):
-algorithm:charge <text> Range of charge states in the sample, i.e. min charge
: max charge. (default: '1:4')
-algorithm:rt_typical <value> Typical retention time [s] over which a characteristic
peptide elutes. (This is not an upper bound. Peptides
that elute for longer will be reported.) (default:
'40.0') (min: '0.0')
-algorithm:rt_band <value> The algorithm searches for characteristic isotopic
peak patterns, spectrum by spectrum. For some low-inte
nsity peptides, an important peak might be missing in
one spectrum but be present in one of the neighbouring
ones. The algorithm takes a bundle of neighbouring
spectra with width rt_band into account. For example
with rt_band = 0, all characteristic isotopic peaks
have to be present in one and the same spectrum. As
rt_band increases, the sensitivity of the algorithm
but also the likelihood of false detections increases.
(default: '0.0') (min: '0.0')
-algorithm:rt_min <value> Lower bound for the retention time [s]. (Any peptides
seen for a shorter time period are not reported.) (def
ault: '2.0') (min: '0.0')
-algorithm:mz_tolerance <value> M/z tolerance for search of peak patterns. (default:
'6.0') (min: '0.0')
-algorithm:mz_unit <choice> Unit of the 'mz_tolerance' parameter. (default: 'ppm')
(valid: 'Da', 'ppm')
-algorithm:intensity_cutoff <value> Lower bound for the intensity of isotopic peaks. (defa
ult: '1000.0') (min: '0.0')
-algorithm:peptide_similarity <value> Two peptides in a multiplet are expected to have the
same isotopic pattern. This parameter is a lower bound
on their similarity. (default: '0.5') (min: '-1.0'
max: '1.0')
-algorithm:averagine_similarity <value> The isotopic pattern of a peptide should resemble the
averagine model at this m/z position. This parameter
is a lower bound on similarity between measured isotop
ic pattern and the averagine model. (default: '0.4')
(min: '-1.0' max: '1.0')
-algorithm:knock_out <choice> Detect partial multiplets as well, e.g. a peptide seen
in the heavy channel only, or whose light channel is
too weak to be detected.
Their missing channels are re-quantified (see 'requant
ify') or reported as absent. With 'false', only multip
lets in which every
channel was detected are quantified. Supported for up
to four channels (more: always 'false'). (default:
'true') (valid: 'true', 'false')
Parameters for mapping the identifications onto the multiplets (IDMapper):
-id_mapping:rt_tolerance <value> RT tolerance (in seconds) beyond the elution region
of a multiplet: an identification matches a multiplet
if it lies within the
region of one of its channels' mass traces (plus the
tolerances), not only near its apex. (default: '5.0')
(min: '0.0')
-id_mapping:mz_tolerance <value> M/z tolerance (in ppm or Da) for the matching (default
: '20.0') (min: '0.0')
-id_mapping:mz_measure <choice> Unit of 'mz_tolerance' (ppm or Da) (default: 'ppm')
(valid: 'ppm', 'Da')
Parameters of the identification-based retention time alignment (MapAlignerIdentification):
-alignment:min_run_occur <number> Minimum number of runs (incl. reference, if any) in
which a peptide must occur to be used for the alignmen
t.
Unless you have very few runs or identifications, incr
ease this value to focus on more informative peptides.
(default: '2') (min: '2')
-alignment:max_rt_shift <value> Maximum realistic RT difference for a peptide (median
per run vs. reference). Peptides with higher shifts
(outliers) are not used to compute the alignment.
If 0, no limit (disable filter); if > 1, the final
value in seconds; if <= 1, taken as a fraction of the
range of the reference RT scale. (default: '0.1') (min
: '0.0')
Parameters for linking the multiplets across runs (FeatureLinkerUnlabeledQT):
-linking:nr_partitions <number> How many partitions in m/z space should be used for
the algorithm (more partitions means faster runtime
and more memory efficient execution). (default: '100')
(min: '1')
-linking:min_nr_diffs_per_bin <number> If IDs are used: How many differences from matching
IDs should be used to calculate a linking tolerance
for unIDed features in an RT region. RT regions will
be extended until that number is reached. (default:
'50') (min: '5')
-linking:min_IDscore_forTolCalc <value> If IDs are used: What is the minimum score of an ID
to assume a reliable match for tolerance calculation.
Check your current score type! (default: '1.0')
-linking:noID_penalty <value> If IDs are used: For the normalized distances, how
high should the penalty for missing IDs be? 0 = no
bias, 1 = IDs inside the max tolerances always preferr
ed (even if much further away). (default: '0.0') (min:
'0.0' max: '1.0')
Distance component based on m/z differences:
-linking:distance_MZ:max_difference <value> Never pair features with larger m/z distance (unit
defined by 'unit') (default: '10.0') (min: '0.0')
-linking:distance_MZ:unit <choice> Unit of the 'max_difference' parameter (default: 'ppm'
) (valid: 'Da', 'ppm')
Parameters of the peptide and protein abundances (ProteinQuantifier):
-ProteinQuantification:method <choice> - top - quantify based on three most abundant peptides
(number can be changed in 'top').
- iBAQ (intensity based absolute quantification), calc
ulate the sum of all peptide peak intensities divided
by the number of theoretically observable tryptic pept
ides (https://rdcu.be/cND1J). Warning: only consensusX
ML or featureXML input is allowed! (default: 'top')
(valid: 'top', 'iBAQ')
-ProteinQuantification:best_charge Distinguish between fraction and charge states in deta
iled peptide output. For protein quantification, selec
t one charge per modified peptide globally: maximize
the number of (fraction group, label) assays with a
positive abundance, then break ties by total abundance
; retain that charge's values in every assay.
By default, protein abundances are summed over all
charge states. How the retained values of several frac
tions are combined is governed by 'fractions:aggregate
', not by this flag.
Additional options for custom quantification using top N peptides.:
-ProteinQuantification:top:N <number> Calculate protein abundance from this number of proteo
typic peptides (most abundant first; '0' for all) (def
ault: '0') (min: '0')
-ProteinQuantification:top:aggregate <choice> Aggregation method used to compute protein abundances
from peptide abundances (default: 'sum') (valid: 'medi
an', 'mean', 'weighted_mean', 'sum')
Options for combining the fractions of a fraction group.:
-ProteinQuantification:fractions:aggregate <choice> How the fractions of one fraction group are combined
into that group's (fraction group, label) assay values
.
- sum - add up every fraction, i.e. treat them as the
parts of one separated sample that they are.
- best - keep a single fraction per peptide and fracti
on group and discard the others. The fraction is chose
n ONCE per peptide, ranked by the number of labels in
...
report every file. (default: 'sum') (valid: 'sum',
'best')
Additional options for consensus maps (and identification results comprising multiple runs):
-ProteinQuantification:consensus:normalize Scale peptide abundances so that the median of each
(fraction group, label) assay matches the overall medi
an.
Abundances of zero count as 'not detected' and are
left out of the medians; an assay without any positive
abundance takes no part in the normalization.
-ProteinQuantification:consensus:fix_peptides Use the same peptides for protein quantification acros
s all (fraction group, label) assays.
With 'N 0',all peptides that occur in every assay are
considered.
Otherwise ('N'), the N peptides that occur in the most
assays (independently of each other) are selected,
breaking ties by total abundance (there is no guarante
e that the best co-ocurring peptides are chosen!).
...
ce.
Parameters of the channel ratios, the reported quantity of a labeled experiment:
-ratios:reference_channel <number> Channel the ratios are formed against, as the 'Label'
of the experimental design (1 = the light channel of
'-labels'). (default: '1') (min: '1')
-ratios:min_ratio_count <number> Minimum number of peptide ratios a protein group needs
before a ratio is reported for it (MaxQuant's 'min.
ratio count'). Groups below it are reported without a
ratio, not with a less certain one. (default: '2')
(min: '1')
-ratios:normalize <choice> Additionally report every ratio divided by the median
peptide ratio of its (fraction group, channel), i.e.
assuming that most peptides do not change. The unnorma
lized ratios are reported either way. (default: 'true'
) (valid: 'true', 'false')
Common TOPP options:
-ini <file> Use the given TOPP INI file
-threads <n> Sets the number of threads allowed to be used by the
TOPP tool (0 = all available cores) (default: '1')
-write_ini <file> Writes the default configuration file
--help Shows options
--helphelp Shows all options (including advanced)
INI file documentation of this tool:
This section lists all parameters supported by the tool. Parameters are organized into hierarchical subsections that group related settings together. Subsections may contain further subsections or individual parameters.
Each parameter entry contains the following information:
Parameter tags provide additional information about how a parameter is used. Some tags indicate whether a parameter is required or intended for advanced configuration, while others may be used internally by OpenMS or workflow tools.
Parameters highlighted as required must be specified for the tool to run successfully. Parameters marked as advanced allow fine-tuning of algorithm behavior and are typically not needed for standard workflows.
1.9.8