The OpenSwathRTNormalizer will find retention time peptides in data.
This tool will find retention time normalization peptides in data and use them to generate a transformation between the experimental RT space and the normalized RT space. The output is a transformation file on how to transform the RT space into the normalized space.
Legend:
required parameter
advanced parameter
+OpenSwathRTNormalizerGenerate a transformation file on how to transform the RT space into the normalized space given a description of RT peptides and their normalized retention time.
version3.2.0
Version of the tool that generated this parameters file.
++1Instance '1' section for 'OpenSwathRTNormalizer'
in[]
Input files separated by blankinput file*.mzML
tr
transition file with the RT peptides ('TraML' or 'csv')input file*.csv, *.traML
out
output fileoutput file*.trafoXML
rt_norm
RT normalization file (how to map the RTs of this run to the ones stored in the library)input file*.trafoXML
min_rsq0.95
Minimum r-squared of RT peptides regression
min_coverage0.6
Minimum relative amount of RT peptides to keep
estimateBestPeptidesfalse
Whether the algorithms should try to choose the best peptides based on their peak shape for normalization. Use this option you do not expect all your peptides to be detected in a sample and too many 'bad' peptides enter the outlier removal step (e.g. due to them being endogenous peptides or using a less curated list of peptides).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
+++RTNormalizationParameters for the RTNormalization. RT normalization and outlier detection can be done iteratively (by default) which removes one outlier per iteration or using the RANSAC algorithm.
outlierMethoditer_residual
Which outlier detection method to use (valid: 'iter_residual', 'iter_jackknife', 'ransac', 'none'). Iterative methods remove one outlier at a time. Jackknife approach optimizes for maximum r-squared improvement while 'iter_residual' removes the datapoint with the largest residual error (removal by residual is computationally cheaper, use this with lots of peptides).iter_residual, iter_jackknife, ransac, none
useIterativeChauvenetfalse
Whether to use Chauvenet's criterion when using iterative methods. This should be used if the algorithm removes too many datapoints but it may lead to true outliers being retained.true, false
RANSACMaxIterations1000
Maximum iterations for the RANSAC outlier detection algorithm.
RANSACMaxPercentRTThreshold3
Maximum threshold in RT dimension for the RANSAC outlier detection algorithm (in percent of the total gradient). Default is set to 3% which is around +/- 4 minutes on a 120 gradient.
RANSACSamplingSize10
Sampling size of data points per iteration for the RANSAC outlier detection algorithm.
+++algorithmAlgorithm parameters section
stop_report_after_feature-1
Stop reporting after feature (ordered by quality; -1 means do not stop).
rt_extraction_window-1.0
Only extract RT around this value (-1 means extract over the whole range, a value of 500 means to extract around +/- 500 s of the expected elution). For this to work, the TraML input file needs to contain normalized RT values.
rt_normalization_factor1.0
The normalized RT is expected to be between 0 and 1. If your normalized RT has a different range, pass this here (e.g. it goes from 0 to 100, set this value to 100)
quantification_cutoff0.0
Cutoff in m/z below which peaks should not be used for quantification any more0.0:∞
write_convex_hullfalse
Whether to write out all points of all features into the featureXMLtrue, false
spectrum_addition_methodsimple
For spectrum addition, either use simple concatenation or use peak resamplingsimple, resample
add_up_spectra1
Add up spectra around the peak apex (needs to be a non-even integer)1:∞
spacing_for_spectra_resampling5.0e-03
If spectra are to be added, use this spacing to add them up0.0:∞
uis_threshold_sn-1
S/N threshold to consider identification transition (set to -1 to consider all)
uis_threshold_peak_area0
Peak area threshold to consider identification transition (set to -1 to consider all)
scoring_modeldefault
Scoring model to usedefault, single_transition
im_extra_drift0.0
Extra drift time to extract for IM scoring (as a fraction, e.g. 0.25 means 25% extra on each side)0.0:∞
stricttrue
Whether to error (true) or skip (false) if a transition in a transition group does not have a corresponding chromatogram.true, false
use_ms1_ion_mobilitytrue
Performs ion mobility extraction in MS1. Set to false if MS1 spectra do not contain ion mobility
++++TransitionGroupPicker
stop_after_feature-1
Stop finding after feature (ordered by intensity; -1 means do not stop).
stop_after_intensity_ratio1.0e-04
Stop after reaching intensity ratio
min_peak_width1.0e-03
Minimal peak width (s), discard all peaks below this value (-1 means no action).
peak_integrationoriginal
Calculate the peak area and height either the smoothed or the raw chromatogram data.original, smoothed
background_subtractionnone
Remove background from peak signal using estimated noise levels. The 'original' method is only provided for historical purposes, please use the 'exact' method and set parameters using the PeakIntegrator: settings. The same original or smoothed chromatogram specified by peak_integration will be used for background estimation.none, original, exact
recalculate_peaksfalse
Tries to get better peak picking by looking at peak consistency of all picked peaks. Tries to use the consensus (median) peak border if the variation within the picked peaks is too large.true, false
use_precursorsfalse
Use precursor chromatogram for peak picking (note that this may lead to precursor signal driving the peak picking)true, false
use_consensustrue
Use consensus peak boundaries when computing transition group picking (if false, compute independent peak boundaries for each transition)true, false
recalculate_peaks_max_z1.0
Determines the maximal Z-Score (difference measured in standard deviations) that is considered too large for peak boundaries. If the Z-Score is above this value, the median is used for peak boundaries (default value 1.0).
minimal_quality-1.0e04
Only if compute_peak_quality is set, this parameter will not consider peaks below this quality threshold
resample_boundary15.0
For computing peak quality, how many extra seconds should be sample left and right of the actual peak
compute_peak_qualityfalse
Tries to compute a quality value for each peakgroup and detect outlier transitions. The resulting score is centered around zero and values above 0 are generally good and below -1 or -2 are usually bad.true, false
compute_peak_shape_metricsfalse
Calculates various peak shape metrics (e.g., tailing) that can be used for downstream QC/QA.true, false
compute_total_mifalse
Compute mutual information metrics for individual transitions that can be used for OpenSWATH/IPF scoring.true, false
boundary_selection_methodlargest
Method to use when selecting the best boundaries for peaks.largest, widest
+++++PeakPickerChromatogram
sgolay_frame_length15
The number of subsequent data points used for smoothing.
This number has to be uneven. If it is not, 1 will be added.
sgolay_polynomial_order3
Order of the polynomial that is fitted.
gauss_width50.0
Gaussian width in seconds, estimated peak size.
use_gausstrue
Use Gaussian filter for smoothing (alternative is Savitzky-Golay filter)false, true
peak_width-1.0
Force a certain minimal peak_width on the data (e.g. extend the peak at least by this amount on both sides) in seconds. -1 turns this feature off.
signal_to_noise1.0
Signal-to-noise threshold at which a peak will not be extended any more. Note that setting this too high (e.g. 1.0) can lead to peaks whose flanks are not fully captured.0.0:∞
sn_win_len1000.0
Signal to noise window length.
sn_bin_count30
Signal to noise bin count.
write_sn_log_messagesfalse
Write out log messages of the signal-to-noise estimator in case of sparse windows or median in rightmost histogram bintrue, false
remove_overlapping_peaksfalse
Try to remove overlapping peaks during peak pickingfalse, true
methodcorrected
Which method to choose for chromatographic peak-picking (OpenSWATH legacy on raw data, corrected picking on smoothed chromatogram or Crawdad on smoothed chromatogram).legacy, corrected, crawdad
+++++PeakIntegrator
integration_typeintensity_sum
The integration technique to use in integratePeak() and estimateBackground() which uses either the summed intensity, integration by Simpson's rule or trapezoidal integration.intensity_sum, simpson, trapezoid
baseline_typebase_to_base
The baseline type to use in estimateBackground() based on the peak boundaries. A rectangular baseline shape is computed based either on the minimal intensity of the peak boundaries, the maximum intensity or the average intensity (base_to_base).base_to_base, vertical_division, vertical_division_min, vertical_division_max
fit_EMGfalse
Fit the chromatogram/spectrum to the EMG peak model.false, true
++++DIAScoring
dia_extraction_window0.05
DIA extraction window in Th or ppm.0.0:∞
dia_extraction_unitTh
DIA extraction window unitTh, ppm
dia_centroidedfalse
Use centroided DIA data.true, false
dia_byseries_intensity_min300.0
DIA b/y series minimum intensity to consider.0.0:∞
dia_byseries_ppm_diff10.0
DIA b/y series minimal difference in ppm to consider.0.0:∞
dia_nr_isotopes4
DIA number of isotopes to consider.0:∞
dia_nr_charges4
DIA number of charges to consider.0:∞
peak_before_mono_max_ppm_diff20.0
DIA maximal difference in ppm to count a peak at lower m/z when searching for evidence that a peak might not be monoisotopic.0.0:∞
++++EMGScoring
interpolation_step0.2
Sampling rate for the interpolation of the model function.
tolerance_stdev_bounding_box3.0
Bounding box has range [minimim of data, maximum of data] enlarged by tolerance_stdev_bounding_box times the standard deviation of the data.
max_iteration500
Maximum number of iterations using by Levenberg-Marquardt algorithm.
init_momfalse
Initialize parameters using method of moments estimators.true, false
+++++statistics
mean1.0
Centroid position of the model.
variance1.0
Variance of the model.
++++Scores
use_shape_scoretrue
Use the shape score (this score measures the similarity in shape of the transitions using a cross-correlation)true, false
use_coelution_scoretrue
Use the coelution score (this score measures the similarity in coelution of the transitions using a cross-correlation)true, false
use_rt_scoretrue
Use the retention time score (this score measure the difference in retention time)true, false
use_library_scoretrue
Use the library scoretrue, false
use_elution_model_scoretrue
Use the elution model (EMG) score (this score fits a gaussian model to the peak and checks the fit)true, false
use_intensity_scoretrue
Use the intensity scoretrue, false
use_nr_peaks_scoretrue
Use the number of peaks scoretrue, false
use_total_xic_scoretrue
Use the total XIC scoretrue, false
use_total_mi_scorefalse
Use the total MI scoretrue, false
use_sn_scoretrue
Use the SN (signal to noise) scoretrue, false
use_mi_scorefalse
Use the MI (mutual information) scoretrue, false
use_dia_scorestrue
Use the DIA (SWATH) scores. If turned off, will not use fragment ion spectra for scoring.true, false
use_ms1_correlationfalse
Use the correlation scores with the MS1 elution profilestrue, false
use_sonar_scoresfalse
Use the scores for SONAR scans (scanning swath)true, false
use_ion_mobility_scoresfalse
Use the scores for Ion Mobility scanstrue, false
use_ms1_fullscanfalse
Use the full MS1 scan at the peak apex for scoring (ppm accuracy of precursor and isotopic pattern)true, false
use_ms1_mifalse
Use the MS1 MI scoretrue, false
use_uis_scoresfalse
Use UIS scores for peptidoform identificationtrue, false
use_peak_shape_metricsfalse
Use peak shape metrics for scoring
use_ionseries_scorestrue
Use MS2-level b/y ion-series scores for peptidoform identificationtrue, false
use_ms2_isotope_scorestrue
Use MS2-level isotope scores (pearson & manhattan) across product transitions (based on ID if annotated or averagine)true, false
+++peptideEstimationParameters for the peptide estimation (use -estimateBestPeptides to enable).
InitialQualityCutoff0.5
The initial overall quality cutoff for a peak to be scored (range ca. -2 to 2)
OverallQualityCutoff5.5
The overall quality cutoff for a peak to go into the retention time estimation (range ca. 0 to 10)
NrRTBins10
Number of RT bins to use to compute coverage. This option should be used to ensure that there is a complete coverage of the RT space (this should detect cases where only a part of the RT gradient is actually covered by normalization peptides)
MinPeptidesPerBin1
Minimal number of peptides that are required for a bin to counted as 'covered'
MinBinsFilled8
Minimal number of bins required to be covered
1.9.1