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OpenMS
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ProteomicsLFQ performs label-free quantification of peptides and proteins.
Input:
Output:
Potential scripts to perform the search can be found under src/tests/topp/ProteomicsLFQTestScripts
The command line parameters of this tool are:
ProteomicsLFQ -- A standard proteomics LFQ pipeline.
Full documentation: http://www.openms.de/doxygen/nightly/html/TOPP_ProteomicsLFQ.html
Version: 3.5.0-pre-nightly-2025-10-24 Oct 25 2025, 02:41:38, Revision: 0291b6e
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:
ProteomicsLFQ <options>
Options (mandatory options marked with '*'):
-in <file list>* Input files (valid formats: 'mzML')
-ids <file list>* Identifications filtered at PSM level (e.g.,
q-value < 0.01).And annotated with PEP as main
score.
We suggest using:
1. PSMFeatureExtractor to annotate percolator
features.
2. PercolatorAdapter tool (score_type = 'q-value
', -post-processing-tdc)
...
ra files. (valid formats: 'idXML', 'mzId')
-design <file> Design file (valid formats: 'tsv')
-fasta <file> Fasta file (valid formats: 'fasta')
-out <file>* Output mzTab file (valid formats: 'mzTab')
-out_msstats <file> Output MSstats input file (valid formats: 'csv')
-out_triqler <file> Output Triqler input file (valid formats: 'tsv')
-out_cxml <file> Output consensusXML file (valid formats: 'consen
susXML')
-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. If Bayesian
inference was chosen, it is equivalent with a
peptide FDR (default: '1.0') (min: '0.0' max:
'1.0')
-FDR_type <threshold> Sub-protein FDR level. PSM, PSM+peptide (best
PSM q-value). (default: 'PSM') (valid: 'PSM',
'PSM+peptide')
-quantification_method <option> Feature_intensity: MS1 signal.
spectral_counting: PSM counts. (default: 'featur
e_intensity') (valid: 'feature_intensity', 'spec
tral_counting')
-targeted_only <option> True: Only ID based quantification.
false: include unidentified features so they
can be linked to identified ones (=match between
runs). (default: 'false') (valid: 'true', 'fals
e')
Centroiding:
-Centroiding:signal_to_noise <value> Minimal signal-to-noise ratio for a peak to be
picked (0.0 disables SNT estimation!) (default:
'0.0') (min: '0.0')
-Centroiding:ms_levels <numbers> List of MS levels for which the peak picking is
applied. If empty, auto mode is enabled, all
peaks which aren't picked yet will get picked.
Other scans are copied to the output without
changes. (min: '1')
PeptideQuantification:
-PeptideQuantification:quantify_decoys Whether decoy peptides should be quantified (tru
e) or skipped (false).
-PeptideQuantification:min_psm_cutoff <text> Minimum score for the best PSM of a spectrum to
be used as seed. Use 'none' for no cutoff. (defa
ult: 'none')
-PeptideQuantification:add_mass_offset_peptides <value> If for every peptide (or seed) also an offset
peptide is extracted (true). Can be used to down
stream to determine MBR false transfer rates.
(0.0 = disabled) (default: '0.0') (min: '0.0')
Parameters for ion chromatogram extraction:
-PeptideQuantification:extract:batch_size <number> Nr of peptides used in each batch of chromatogra
m extraction. Smaller values decrease memory
usage but increase runtime. (default: '5000')
(min: '1')
-PeptideQuantification:extract:mz_window <value> M/z window size for chromatogram extraction (uni
t: ppm if 1 or greater, else Da/Th) (default:
'10.0') (min: '0.0')
Parameters for detecting features in extracted ion chromatograms:
-PeptideQuantification:detect:mapping_tolerance <value> RT tolerance (plus/minus) for mapping peptide
IDs to features. Absolute value in seconds if 1
or greater, else relative to the RT span of the
feature. (default: '0.0') (min: '0.0')
Parameters for scoring features using a support vector machine (SVM):
-PeptideQuantification:svm:log2_p <values> Values to try for the SVM parameter 'epsilon'
during parameter optimization (epsilon-SVR only)
. A value 'x' is used as 'epsilon = 2^x'. (defau
lt: '[-15.0 -12.0 -9.0 -6.0 -3.32192809489 0.0
3.32192809489 6.0 9.0 12.0 15.0]')
Parameters for fitting exp. mod. Gaussians to mass traces.:
-PeptideQuantification:EMGScoring:max_iteration <number> Maximum number of iterations for EMG fitting.
(default: '100') (min: '1')
-PeptideQuantification:EMGScoring:init_mom Alternative initial parameters for fitting throu
gh method of moments.
Alignment:
-Alignment:model_type <choice> Options to control the modeling of retention
time transformations from data (default: 'b_spli
ne') (valid: 'linear', 'b_spline', 'lowess',
'interpolated')
Alignment:model:
-Alignment:model:type <choice> Type of model (default: 'b_spline') (valid: 'lin
ear', 'b_spline', 'lowess', 'interpolated')
Parameters for 'linear' model:
-Alignment:model:linear:symmetric_regression Perform linear regression on 'y - x' vs. 'y +
x', instead of on 'y' vs. 'x'.
-Alignment:model:linear:x_weight <choice> Weight x values (default: 'x') (valid: '1/x',
'1/x2', 'ln(x)', 'x')
-Alignment:model:linear:y_weight <choice> Weight y values (default: 'y') (valid: '1/y',
'1/y2', 'ln(y)', 'y')
-Alignment:model:linear:x_datum_min <value> Minimum x value (default: '1.0e-15')
-Alignment:model:linear:x_datum_max <value> Maximum x value (default: '1.0e15')
-Alignment:model:linear:y_datum_min <value> Minimum y value (default: '1.0e-15')
-Alignment:model:linear:y_datum_max <value> Maximum y value (default: '1.0e15')
Parameters for 'b_spline' model:
-Alignment:model:b_spline:wavelength <value> Determines the amount of smoothing by setting
the number of nodes for the B-spline. The number
is chosen so that the spline approximates a
low-pass filter with this cutoff wavelength.
The wavelength is given in the same units as
the data; a higher value means more smoothing.
'0' sets the number of nodes to twice the number
of input points. (default: '0.0') (min: '0.0')
-Alignment:model:b_spline:num_nodes <number> Number of nodes for B-spline fitting. Overrides
'wavelength' if set (to two or greater). A lower
value means more smoothing. (default: '5') (min
: '0')
-Alignment:model:b_spline:extrapolate <choice> Method to use for extrapolation beyond the origi
nal data range. 'linear': Linear extrapolation
using the slope of the B-spline at the correspon
ding endpoint. 'b_spline': Use the B-spline (as
for interpolation). 'constant': Use the constant
value of the B-spline at the corresponding endp
oint. 'global_linear': Use a linear fit through
the data (which will most probably introduce
discontinuities at the ends of the data range).
(default: 'linear') (valid: 'linear', 'b_spline'
, 'constant', 'global_linear')
-Alignment:model:b_spline:boundary_condition <number> Boundary condition at B-spline endpoints: 0 (val
ue zero), 1 (first derivative zero) or 2 (second
derivative zero) (default: '2') (min: '0' max:
'2')
Parameters for 'lowess' model:
-Alignment:model:lowess:span <value> Fraction of datapoints (f) to use for each local
regression (determines the amount of smoothing)
. Choosing this parameter in the range .2 to .8
usually results in a good fit. (default: '0.6666
66666666667') (min: '0.0' max: '1.0')
-Alignment:model:lowess:auto_span If true, or if 'span' is 0, automatically select
LOWESS span by cross-validation.
-Alignment:model:lowess:auto_span_min <value> Lower bound for auto-selected span. (default:
'0.15') (min: '1.0e-03')
-Alignment:model:lowess:auto_span_max <value> Upper bound for auto-selected span. (default:
'0.8') (max: '0.99')
-Alignment:model:lowess:auto_min_neighbors <number> Minimum number of neighbors (span*n) enforced
in auto mode. (default: '5') (min: '3')
-Alignment:model:lowess:auto_k_folds <number> K-folds for CV when n>50 (else LOO is used).
(default: '5') (min: '2')
-Alignment:model:lowess:auto_metric <choice> Metric for CV selection: one of {'p90','p95','p9
9','rmse','mae'}. (default: 'mae') (valid: 'p90'
, 'p95', 'p99', 'rmse', 'mae')
-Alignment:model:lowess:auto_span_grid <text> Optional explicit grid of span candidates in
(0,1]. Comma-separated list, e.g. '0.2,0.3,0.5'.
If empty, a default grid is used.
-Alignment:model:lowess:num_iterations <number> Number of robustifying iterations for lowess
fitting. (default: '3') (min: '0')
-Alignment:model:lowess:delta <value> Nonnegative parameter which may be used to save
computations (recommended value is 0.01 of the
range of the input, e.g. for data ranging from
1000 seconds to 2000 seconds, it could be set
to 10). Setting a negative value will automatica
lly do this. (default: '-1.0')
-Alignment:model:lowess:interpolation_type <choice> Method to use for interpolation between datapoin
ts computed by lowess. 'linear': Linear interpol
ation. 'cspline': Use the cubic spline for inter
polation. 'akima': Use an akima spline for inter
polation (default: 'cspline') (valid: 'linear',
'cspline', 'akima')
-Alignment:model:lowess:extrapolation_type <choice> Method to use for extrapolation outside the data
range. 'two-point-linear': Uses a line through
the first and last point to extrapolate. 'four-p
oint-linear': Uses a line through the first and
second point to extrapolate in front and and a
line through the last and second-to-last point
in the end. 'global-linear': Uses a linear regre
ssion to fit a line through all data points and
use it for interpolation. (default: 'four-point-
linear') (valid: 'two-point-linear', 'four-point
-linear', 'global-linear')
Parameters for 'interpolated' model:
-Alignment:model:interpolated:interpolation_type <choice> Type of interpolation to apply. (default: 'cspli
ne') (valid: 'linear', 'cspline', 'akima')
-Alignment:model:interpolated:extrapolation_type <choice> Type of extrapolation to apply: two-point-linear
: use the first and last data point to build a
single linear model, four-point-linear: build
two linear models on both ends using the first
two / last two points, global-linear: use all
points to build a single linear model. Note that
global-linear may not be continuous at the bord
er. (default: 'two-point-linear') (valid: 'two-p
oint-linear', 'four-point-linear', 'global-linea
r')
Alignment:align_algorithm:
-Alignment:align_algorithm:score_type <text> Name of the score type to use for ranking and
filtering (.oms input only). If left empty, a
score type is picked automatically.
-Alignment:align_algorithm:min_run_occur <number> Minimum number of runs (incl. reference, if any)
in which a peptide must occur to be used for
the alignment.
Unless you have very few runs or identifications
, increase this value to focus on more informati
ve peptides. (default: '2') (min: '2')
-Alignment:align_algorithm: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 frac
tion of the range of the reference RT scale.
(default: '0.1') (min: '0.0')
-Alignment:align_algorithm:use_adducts <choice> If IDs contain adducts, treat differently adduct
ed variants of the same molecule as different.
(default: 'true') (valid: 'true', 'false')
Linking:
-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 match
ing 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! (def
ault: '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 preferred (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')
ProteinQuantification:
-ProteinQuantification:method <choice> - top - quantify based on three most abundant
peptides (number can be changed in 'top').
- iBAQ (intensity based absolute quantification)
, calculate the sum of all peptide peak intensit
ies divided by the number of theoretically obser
vable tryptic peptides (https://rdcu.be/cND1J).
Warning: only consensusXML or featureXML input
is allowed! (default: 'top') (valid: 'top', 'iBA
Q')
-ProteinQuantification:best_charge_and_fraction Distinguish between fraction and charge states
of a peptide. For peptides, abundances will be
reported separately for each fraction and charge
;
for proteins, abundances will be computed based
only on the most prevalent charge observed of
each peptide (over all fractions).
By default, abundances are summed over all charg
e states.
Additional options for custom quantification using top N peptides.:
-ProteinQuantification:top:N <number> Calculate protein abundance from this number of
proteotypic peptides (most abundant first; '0'
for all) (default: '3') (min: '0')
-ProteinQuantification:top:aggregate <choice> Aggregation method used to compute protein abund
ances from peptide abundances (default: 'median'
) (valid: 'median', 'mean', 'weighted_mean',
'sum')
Additional options for consensus maps (and identification results comprising multiple runs):
-ProteinQuantification:consensus:normalize Scale peptide abundances so that medians of all
samples are equal
-ProteinQuantification:consensus:fix_peptides Use the same peptides for protein quantification
across all samples.
With 'N 0',all peptides that occur in every samp
le are considered.
Otherwise ('N'), the N peptides that occur in
the most samples (independently of each other)
are selected,
breaking ties by total abundance (there is no
guarantee that the best co-ocurring peptides
are chosen!).
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 (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:
1.9.1