OpenMS
ProteomicsLFQ

ProteomicsLFQ performs label-free quantification of peptides and proteins.
Input:

  • Spectra in mzML format
  • Identifications in idXML or mzIdentML format with posterior error probabilities as score type. To generate those we suggest to run:
    1. PeptideIndexer to annotate target and decoy information.
    2. PSMFeatureExtractor to annotate percolator features.
    3. PercolatorAdapter tool (score_type = 'q-value', -post-processing-tdc)
    4. IDFilter (pep:score = 0.01) to filter PSMs at 1% FDR
  • An experimental design file:
    (see ExperimentalDesign for details)
  • A protein database in with appended decoy sequences in FASTA format
    (e.g., generated by the OpenMS DecoyDatabase tool)
    Processing:
    ProteomicsLFQ has different methods to extract features: ID-based (targeted only), or both ID-based and untargeted.
  1. The first method uses targeted feature dectection using RT and m/z information derived from identification data to extract features. Note: only identifications found in a particular MS run are used to extract features in the same run. No transfer of IDs (match between runs) is performed.
  2. The second method adds untargeted feature detection to obtain quantities from unidentified features. Transfer of Ids (match between runs) is performed by transfering feature identifications to coeluting, unidentified features with similar mass and RT in other runs.

Output:

  • mzTab file with analysis results
  • MSstats file with analysis results for statistical downstream analysis in MSstats
  • ConsensusXML file for visualization and further processing in OpenMS

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: 3.2.0-pre-nightly-2024-07-19 Jul 20 2024, 02:06:43, Revision: f10e72e
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: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:

Legend:
required parameter
advanced parameter
+ProteomicsLFQA standard proteomics LFQ pipeline.
version3.2.0-pre-nightly-2024-07-19 Version of the tool that generated this parameters file.
++1Instance '1' section for 'ProteomicsLFQ'
in[] Input filesinput file*.mzML
ids[] 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)
3. IDFilter (pep:score = 0.05)
To obtain well calibrated PEPs and an initial reduction of PSMs
ID files must be provided in same order as spectra files.
input file*.idXML, *.mzId
design design fileinput file*.tsv
fasta fasta fileinput file*.fasta
out output mzTab fileoutput file*.mzTab
out_msstats output MSstats input fileoutput file*.csv
out_triqler output Triqler input fileoutput file*.tsv
out_cxml output consensusXML fileoutput file*.consensusXML
proteinFDR0.05 Protein FDR threshold (0.05=5%).0.0:1.0
picked_proteinFDRfalse Use a picked protein FDR?true, false
psmFDR1.0 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 FDR0.0:1.0
FDR_typePSM Sub-protein FDR level. PSM, PSM+peptide (best PSM q-value).PSM, PSM+peptide
protein_inferenceaggregation 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.
Note: 'bayesian' only uses and reports the best PSM per peptide.
aggregation, bayesian
protein_quantificationunique_peptides 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 only for its best group (by inference score)
unique_peptides, strictly_unique_peptides, shared_peptides
quantification_methodfeature_intensity feature_intensity: MS1 signal.
spectral_counting: PSM counts.
feature_intensity, spectral_counting
targeted_onlyfalse true: Only ID based quantification.
false: include unidentified features so they can be linked to identified ones (=match between runs).
true, false
feature_with_id_min_score0.0 The minimum probability (e.g.: 0.25) an identified (=id targeted) feature must have to be kept for alignment and linking (0=no filter).0.0:1.0
feature_without_id_min_score0.0 The minimum probability (e.g.: 0.75) an unidentified feature must have to be kept for alignment and linking (0=no filter).0.0:1.0
mass_recalibrationfalse Mass recalibration.true, false
alignment_orderstar If star, aligns all maps to the reference with most IDs.star
keep_feature_top_psm_onlytrue If false, also keeps lower ranked PSMs that have the top-scoring sequence as a candidate per feature in the same file.true, false
log Name of log file (created only when specified)
debug0 Sets the debug level
threads1 Sets the number of threads allowed to be used by the TOPP tool
no_progressfalse Disables progress logging to command linetrue, false
forcefalse Overrides tool-specific checkstrue, false
testfalse Enables the test mode (needed for internal use only)true, false
+++SeedingParameters for seeding of untargeted features
intThreshold1.0e04 Peak intensity threshold applied in seed detection.
charge2:5 Charge range considered for untargeted feature seeds.
traceRTTolerance3.0 Combines all spectra in the tolerance window to stabilize identification of isotope patterns. Controls sensitivity (low value) vs. specificity (high value) of feature seeds.
+++Centroiding
signal_to_noise0.0 Minimal signal-to-noise ratio for a peak to be picked (0.0 disables SNT estimation!)0.0:∞
spacing_difference_gap4.0 The extension of a peak is stopped if the spacing between two subsequent data points exceeds 'spacing_difference_gap * min_spacing'. 'min_spacing' is the smaller of the two spacings from the peak apex to its two neighboring points. '0' to disable the constraint. Not applicable to chromatograms.0.0:∞
spacing_difference1.5 Maximum allowed difference between points during peak extension, in multiples of the minimal difference between the peak apex and its two neighboring points. If this difference is exceeded a missing point is assumed (see parameter 'missing'). A higher value implies a less stringent peak definition, since individual signals within the peak are allowed to be further apart. '0' to disable the constraint. Not applicable to chromatograms.0.0:∞
missing1 Maximum number of missing points allowed when extending a peak to the left or to the right. A missing data point occurs if the spacing between two subsequent data points exceeds 'spacing_difference * min_spacing'. 'min_spacing' is the smaller of the two spacings from the peak apex to its two neighboring points. Not applicable to chromatograms.0:∞
ms_levels[] 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.1:∞
report_FWHMfalse Add metadata for FWHM (as floatDataArray named 'FWHM' or 'FWHM_ppm', depending on param 'report_FWHM_unit') for each picked peak.true, false
report_FWHM_unitrelative Unit of FWHM. Either absolute in the unit of input, e.g. 'm/z' for spectra, or relative as ppm (only sensible for spectra, not chromatograms).relative, absolute
++++SignalToNoise
max_intensity-1 maximal intensity considered for histogram construction. By default, it will be calculated automatically (see auto_mode). Only provide this parameter if you know what you are doing (and change 'auto_mode' to '-1')! All intensities EQUAL/ABOVE 'max_intensity' will be added to the LAST histogram bin. If you choose 'max_intensity' too small, the noise estimate might be too small as well. If chosen too big, the bins become quite large (which you could counter by increasing 'bin_count', which increases runtime). In general, the Median-S/N estimator is more robust to a manual max_intensity than the MeanIterative-S/N.-1:∞
auto_max_stdev_factor3.0 parameter for 'max_intensity' estimation (if 'auto_mode' == 0): mean + 'auto_max_stdev_factor' * stdev0.0:999.0
auto_max_percentile95 parameter for 'max_intensity' estimation (if 'auto_mode' == 1): auto_max_percentile th percentile0:100
auto_mode0 method to use to determine maximal intensity: -1 --> use 'max_intensity'; 0 --> 'auto_max_stdev_factor' method (default); 1 --> 'auto_max_percentile' method-1:1
win_len200.0 window length in Thomson1.0:∞
bin_count30 number of bins for intensity values3:∞
min_required_elements10 minimum number of elements required in a window (otherwise it is considered sparse)1:∞
noise_for_empty_window1.0e20 noise value used for sparse windows
write_log_messagestrue Write out log messages in case of sparse windows or median in rightmost histogram bintrue, false
+++PeptideQuantification
candidates_out Optional output file with feature candidates.output file
debug0 Debug level for feature detection.0:∞
quantify_decoysfalse Whether decoy peptides should be quantified (true) or skipped (false).true, false
min_psm_cutoffnone Minimum score for the best PSM of a spectrum to be used as seed. Use 'none' for no cutoff.
add_mass_offset_peptides0.0 If for every peptide (or seed) also an offset peptide is extracted (true). Can be used to downstream to determine MBR false transfer rates. (0.0 = disabled)0.0:∞
++++extractParameters for ion chromatogram extraction
batch_size5000 Nr of peptides used in each batch of chromatogram extraction. Smaller values decrease memory usage but increase runtime.1:∞
mz_window10.0 m/z window size for chromatogram extraction (unit: ppm if 1 or greater, else Da/Th)0.0:∞
n_isotopes2 Number of isotopes to include in each peptide assay.2:∞
isotope_pmin0.0 Minimum probability for an isotope to be included in the assay for a peptide. If set, this parameter takes precedence over 'extract:n_isotopes'.0.0:1.0
rt_quantile0.95 Quantile of the RT deviations between aligned internal and external IDs to use for scaling the RT extraction window0.0:1.0
rt_window0.0 RT window size (in sec.) for chromatogram extraction. If set, this parameter takes precedence over 'extract:rt_quantile'.0.0:∞
++++detectParameters for detecting features in extracted ion chromatograms
min_peak_width0.2 Minimum elution peak width. Absolute value in seconds if 1 or greater, else relative to 'peak_width'.0.0:∞
signal_to_noise0.8 Signal-to-noise threshold for OpenSWATH feature detection0.1:∞
mapping_tolerance0.0 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.0.0:∞
++++svmParameters for scoring features using a support vector machine (SVM)
samples10000 Number of observations to use for training ('0' for all)0:∞
no_selectionfalse By default, roughly the same number of positive and negative observations, with the same intensity distribution, are selected for training. This aims to reduce biases, but also reduces the amount of training data. Set this flag to skip this procedure and consider all available observations (subject to 'svm:samples').true, false
xval_out Output file: SVM cross-validation (parameter optimization) resultsoutput file*.csv
kernelRBF SVM kernelRBF, linear
xval5 Number of partitions for cross-validation (parameter optimization)1:∞
log2_C[-2.0, 5.0, 15.0] Values to try for the SVM parameter 'C' during parameter optimization. A value 'x' is used as 'C = 2^x'.
log2_gamma[-3.0, -1.0, 2.0] Values to try for the SVM parameter 'gamma' during parameter optimization (RBF kernel only). A value 'x' is used as 'gamma = 2^x'.
log2_p[-15.0, -12.0, -9.0, -6.0, -3.32192809489, 0.0, 3.32192809489, 6.0, 9.0, 12.0, 15.0] Values to try for the SVM parameter 'epsilon' during parameter optimization (epsilon-SVR only). A value 'x' is used as 'epsilon = 2^x'.
epsilon1.0e-03 Stopping criterion0.0:∞
cache_size100.0 Size of the kernel cache (in MB)1.0:∞
no_shrinkingfalse Disable the shrinking heuristicstrue, false
predictorspeak_apices_sum,var_xcorr_coelution,var_xcorr_shape,var_library_sangle,var_intensity_score,sn_ratio,var_log_sn_score,var_elution_model_fit_score,xx_lda_prelim_score,var_ms1_isotope_correlation_score,var_ms1_isotope_overlap_score,var_massdev_score,main_var_xx_swath_prelim_score Names of OpenSWATH scores to use as predictors for the SVM (comma-separated list)
min_prob0.9 Minimum probability of correctness, as predicted by the SVM, required to retain a feature candidate0.0:1.0
++++modelParameters for fitting elution models to features
typesymmetric Type of elution model to fit to featuressymmetric, asymmetric, none
add_zeros0.2 Add zero-intensity points outside the feature range to constrain the model fit. This parameter sets the weight given to these points during model fitting; '0' to disable.0.0:∞
unweighted_fitfalse Suppress weighting of mass traces according to theoretical intensities when fitting elution modelstrue, false
no_imputationfalse If fitting the elution model fails for a feature, set its intensity to zero instead of imputing a value from the initial intensity estimatetrue, false
each_tracefalse Fit elution model to each individual mass tracetrue, false
+++++checkParameters for checking the validity of elution models (and rejecting them if necessary)
min_area1.0 Lower bound for the area under the curve of a valid elution model0.0:∞
boundaries0.5 Time points corresponding to this fraction of the elution model height have to be within the data region used for model fitting0.0:1.0
width10.0 Upper limit for acceptable widths of elution models (Gaussian or EGH), expressed in terms of modified (median-based) z-scores. '0' to disable. Not applied to individual mass traces (parameter 'each_trace').0.0:∞
asymmetry10.0 Upper limit for acceptable asymmetry of elution models (EGH only), expressed in terms of modified (median-based) z-scores. '0' to disable. Not applied to individual mass traces (parameter 'each_trace').0.0:∞
++++EMGScoringParameters for fitting exp. mod. Gaussians to mass traces.
max_iteration100 Maximum number of iterations for EMG fitting.1:∞
init_momfalse Alternative initial parameters for fitting through method of moments.true, false
+++Alignment
model_typeb_spline Options to control the modeling of retention time transformations from datalinear, b_spline, lowess, interpolated
++++model
typeb_spline Type of modellinear, b_spline, lowess, interpolated
+++++linearParameters for 'linear' model
symmetric_regressionfalse Perform linear regression on 'y - x' vs. 'y + x', instead of on 'y' vs. 'x'.true, false
x_weightx Weight x values1/x, 1/x2, ln(x), x
y_weighty Weight y values1/y, 1/y2, ln(y), y
x_datum_min1.0e-15 Minimum x value
x_datum_max1.0e15 Maximum x value
y_datum_min1.0e-15 Minimum y value
y_datum_max1.0e15 Maximum y value
+++++b_splineParameters for 'b_spline' model
wavelength0.0 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.0.0:∞
num_nodes5 Number of nodes for B-spline fitting. Overrides 'wavelength' if set (to two or greater). A lower value means more smoothing.0:∞
extrapolatelinear Method to use for extrapolation beyond the original data range. 'linear': Linear extrapolation using the slope of the B-spline at the corresponding endpoint. 'b_spline': Use the B-spline (as for interpolation). 'constant': Use the constant value of the B-spline at the corresponding endpoint. 'global_linear': Use a linear fit through the data (which will most probably introduce discontinuities at the ends of the data range).linear, b_spline, constant, global_linear
boundary_condition2 Boundary condition at B-spline endpoints: 0 (value zero), 1 (first derivative zero) or 2 (second derivative zero)0:2
+++++lowessParameters for 'lowess' model
span0.666666666666667 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.0.0:1.0
num_iterations3 Number of robustifying iterations for lowess fitting.0:∞
delta-1.0 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 automatically do this.
interpolation_typecspline Method to use for interpolation between datapoints computed by lowess. 'linear': Linear interpolation. 'cspline': Use the cubic spline for interpolation. 'akima': Use an akima spline for interpolationlinear, cspline, akima
extrapolation_typefour-point-linear Method to use for extrapolation outside the data range. 'two-point-linear': Uses a line through the first and last point to extrapolate. 'four-point-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 regression to fit a line through all data points and use it for interpolation.two-point-linear, four-point-linear, global-linear
+++++interpolatedParameters for 'interpolated' model
interpolation_typecspline Type of interpolation to apply.linear, cspline, akima
extrapolation_typetwo-point-linear 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 border.two-point-linear, four-point-linear, global-linear
++++align_algorithm
score_type Name of the score type to use for ranking and filtering (.oms input only). If left empty, a score type is picked automatically.
score_cutofffalse Use only IDs above a score cut-off (parameter 'min_score') for alignment?true, false
min_score0.05 If 'score_cutoff' is 'true': Minimum score for an ID to be considered.
Unless you have very few runs or identifications, increase this value to focus on more informative peptides.
min_run_occur2 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 informative peptides.
2:∞
max_rt_shift0.1 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.
0.0:∞
use_unassigned_peptidesfalse Should unassigned peptide identifications be used when computing an alignment of feature or consensus maps? If 'false', only peptide IDs assigned to features will be used.true, false
use_feature_rttrue When aligning feature or consensus maps, don't use the retention time of a peptide identification directly; instead, use the retention time of the centroid of the feature (apex of the elution profile) that the peptide was matched to. If different identifications are matched to one feature, only the peptide closest to the centroid in RT is used.
Precludes 'use_unassigned_peptides'.
true, false
use_adductstrue If IDs contain adducts, treat differently adducted variants of the same molecule as different.true, false
+++Linking
use_identificationstrue Never link features that are annotated with different peptides (only the best hit per peptide identification is taken into account).true, false
nr_partitions100 How many partitions in m/z space should be used for the algorithm (more partitions means faster runtime and more memory efficient execution).1:∞
min_nr_diffs_per_bin50 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.5:∞
min_IDscore_forTolCalc1.0 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!
noID_penalty0.0 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).0.0:1.0
ignore_chargefalse false [default]: pairing requires equal charge state (or at least one unknown charge '0'); true: Pairing irrespective of charge statetrue, false
ignore_adducttrue true [default]: pairing requires equal adducts (or at least one without adduct annotation); true: Pairing irrespective of adductstrue, false
++++distance_RTDistance component based on RT differences
exponent1.0 Normalized RT differences ([0-1], relative to 'max_difference') are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)0.0:∞
weight1.0 Final RT distances are weighted by this factor0.0:∞
++++distance_MZDistance component based on m/z differences
max_difference10.0 Never pair features with larger m/z distance (unit defined by 'unit')0.0:∞
unitppm Unit of the 'max_difference' parameterDa, ppm
exponent2.0 Normalized ([0-1], relative to 'max_difference') m/z differences are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)0.0:∞
weight5.0 Final m/z distances are weighted by this factor0.0:∞
++++distance_intensityDistance component based on differences in relative intensity (usually relative to highest peak in the whole data set)
exponent1.0 Differences in relative intensity ([0-1]) are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)0.0:∞
weight0.1 Final intensity distances are weighted by this factor0.0:∞
log_transformdisabled Log-transform intensities? If disabled, d = |int_f2 - int_f1| / int_max. If enabled, d = |log(int_f2 + 1) - log(int_f1 + 1)| / log(int_max + 1))enabled, disabled
+++ProteinQuantification
methodtop - 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 intensities divided by the number of theoretically observable tryptic peptides (https://rdcu.be/cND1J). Warning: only consensusXML or featureXML input is allowed!
top, iBAQ
best_charge_and_fractionfalse 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 charge states.
true, false
++++topAdditional options for custom quantification using top N peptides.
N3 Calculate protein abundance from this number of proteotypic peptides (most abundant first; '0' for all)0:∞
aggregatemedian Aggregation method used to compute protein abundances from peptide abundancesmedian, mean, weighted_mean, sum
include_alltrue Include results for proteins with fewer proteotypic peptides than indicated by 'N' (no effect if 'N' is 0 or 1)true, false
++++consensusAdditional options for consensus maps (and identification results comprising multiple runs)
normalizefalse Scale peptide abundances so that medians of all samples are equaltrue, false
fix_peptidesfalse Use the same peptides for protein quantification across all samples.
With 'N 0',all peptides that occur in every sample 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!).
true, false