| OpenMS 2.5.0

OpenSwathRTNormalizer

The OpenSwathRTNormalizer will find retention time peptides in data.

potential predecessor tools | OpenSwathRTNormalizer | potential successor tools
| OpenSwathAnalyzer
OpenSwathWorkflow

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.

The command line parameters of this tool are:

    OpenSwathRTNormalizer -- This tool will take a description of RT peptides and their normalized retention time

    to write out a transformation file on how to transform the RT space into the normalized space.

    Full documentation: http://www.openms.de/documentation/TOPP_OpenSwathRTNormalizer.html

    Version: 2.5.0-GIT-NOTFOUND-GIT-NOTFOUND Sep 10 2020, 12:20:39, Revision: GIT-NOTFOUND

    To cite OpenMS:

      Rost HL, Sachsenberg T, Aiche S, Bielow C et al.. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Meth. 2016; 13, 9: 741-748. doi:10.1038/nmeth.3959.

    Usage:

      OpenSwathRTNormalizer <options>

    This tool has algorithm parameters that are not shown here! Please check the ini file for a detailed descript

    ion or use the --helphelp option.

    Options (mandatory options marked with '*'):

      -in <files>*            Input files separated by blank (valid formats: 'mzML')

      -tr <file>*             Transition file with the RT peptides ('TraML' or 'csv') (valid formats: 'csv', 'tra

                              ML')

      -out <file>*            Output file (valid formats: 'trafoXML')

      -rt_norm <file>         RT normalization file (how to map the RTs of this run to the ones stored in the

                              library) (valid formats: 'trafoXML')

      -min_rsq <double>       Minimum r-squared of RT peptides regression (default: '0.95')

      -min_coverage <double>  Minimum relative amount of RT peptides to keep (default: '0.6')

      -estimateBestPeptides   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 peptide

                              s).

    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)

    The following configuration subsections are valid:

     - RTNormalization     Parameters 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.

     - algorithm           Algorithm parameters section

     - peptideEstimation   Parameters for the peptide estimation (use -estimateBestPeptides to enable).

    You can write an example INI file using the '-write_ini' option.

    Documentation of subsection parameters can be found in the doxygen documentation or the INIFileEditor.

    For more information, please consult the online documentation for this tool:

      - http://www.openms.de/documentation/TOPP_OpenSwathRTNormalizer.html

The algorithm parameters for the Analyzer filter are:

Legend:
required parameter
advanced parameter
+OpenSwathRTNormalizer This tool will take a description of RT peptides and their normalized retention time to write out a transformation file on how to transform the RT space into the normalized space.
version 2.5.0-GIT-NOTFOUND-GIT-NOTFOUND Version of the tool that generated this parameters file.
++1 Instance '1' section for 'OpenSwathRTNormalizer'
in[] Input files separated by blank input file*.mzML
tr transition file with the RT peptides ('TraML' or 'csv')input file*.csv,*.traML
out output file output 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_rsq 0.95 Minimum r-squared of RT peptides regression
min_coverage 0.6 Minimum relative amount of RT peptides to keep
estimateBestPeptides false 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)
debug 0 Sets the debug level
threads 1 Sets the number of threads allowed to be used by the TOPP tool
no_progress false Disables progress logging to command line true,false
force false Overwrite tool specific checks.true,false
test false Enables the test mode (needed for internal use only)true,false
+++RTNormalization Parameters 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.
outlierMethod iter_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
useIterativeChauvenet false 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
RANSACMaxIterations 1000 Maximum iterations for the RANSAC outlier detection algorithm.
RANSACMaxPercentRTThreshold 3 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.
RANSACSamplingSize 10 Sampling size of data points per iteration for the RANSAC outlier detection algorithm.
+++algorithm Algorithm 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_factor 1.0 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_cutoff 0.0 Cutoff in m/z below which peaks should not be used for quantification any more 0.0:∞
write_convex_hull false Whether to write out all points of all features into the featureXML true,false
spectrum_addition_method simple For spectrum addition, either use simple concatenation or use peak resampling simple,resample
add_up_spectra 1 Add up spectra around the peak apex (needs to be a non-even integer)1:∞
spacing_for_spectra_resampling 0.005 If spectra are to be added, use this spacing to add them up 0.0:∞
uis_threshold_sn-1 S/N threshold to consider identification transition (set to -1 to consider all)
uis_threshold_peak_area 0 Peak area threshold to consider identification transition (set to -1 to consider all)
scoring_model default Scoring model to use default,single_transition
im_extra_drift 0.0 Extra drift time to extract for IM scoring (as a fraction, e.g. 0.25 means 25% extra on each side)0.0:∞
++++TransitionGroupPicker
stop_after_feature-1 Stop finding after feature (ordered by intensity; -1 means do not stop).
stop_after_intensity_ratio 1.0e-04 Stop after reaching intensity ratio
min_peak_width-1.0 Minimal peak width (s), discard all peaks below this value (-1 means no action).
peak_integration original Calculate the peak area and height either the smoothed or the raw chromatogram data.original,smoothed
background_subtraction none 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_peaks false 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_precursors false Use precursor chromatogram for peak picking (note that this may lead to precursor signal driving the peak picking)true,false
use_consensus true Use consensus peak boundaries when computing transition group picking (if false, compute independent peak boundaries for each transition)true,false
recalculate_peaks_max_z 1.0 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-10000.0 Only if compute_peak_quality is set, this parameter will not consider peaks below this quality threshold
resample_boundary 15.0 For computing peak quality, how many extra seconds should be sample left and right of the actual peak
compute_peak_quality false 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_metrics false Calculates various peak shape metrics (e.g., tailing) that can be used for downstream QC/QA.true,false
compute_total_mi false Compute mutual information metrics for individual transitions that can be used for OpenSWATH/IPF scoring.true,false
boundary_selection_method largest Method to use when selecting the best boundaries for peaks.largest,widest
+++++PeakPickerMRM
sgolay_frame_length 15 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_order 3 Order of the polynomial that is fitted.
gauss_width 50.0 Gaussian width in seconds, estimated peak size.
use_gauss true 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_noise 1.0 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_len 1000.0 Signal to noise window length.
sn_bin_count 30 Signal to noise bin count.
write_sn_log_messages false Write out log messages of the signal-to-noise estimator in case of sparse windows or median in rightmost histogram bin true,false
remove_overlapping_peaks false Try to remove overlapping peaks during peak picking false,true
method corrected 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_type intensity_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_type base_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_EMG false Fit the chromatogram/spectrum to the EMG peak model.false,true
++++DIAScoring
dia_extraction_window 0.05 DIA extraction window in Th or ppm.0.0:∞
dia_extraction_unit Th DIA extraction window unit Th,ppm
dia_centroided false Use centroided DIA data.true,false
dia_byseries_intensity_min 300.0 DIA b/y series minimum intensity to consider.0.0:∞
dia_byseries_ppm_diff 10.0 DIA b/y series minimal difference in ppm to consider.0.0:∞
dia_nr_isotopes 4 DIA number of isotopes to consider.0:∞
dia_nr_charges 4 DIA number of charges to consider.0:∞
peak_before_mono_max_ppm_diff 20.0 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_step 0.2 Sampling rate for the interpolation of the model function.
tolerance_stdev_bounding_box 3.0 Bounding box has range [minimim of data, maximum of data] enlarged by tolerance_stdev_bounding_box times the standard deviation of the data.
max_iteration 500 Maximum number of iterations using by Levenberg-Marquardt algorithm.
+++++statistics
mean 1.0 Centroid position of the model.
variance 1.0 Variance of the model.
++++Scores
use_shape_score true Use the shape score (this score measures the similarity in shape of the transitions using a cross-correlation)true,false
use_coelution_score true Use the coelution score (this score measures the similarity in coelution of the transitions using a cross-correlation)true,false
use_rt_score true Use the retention time score (this score measure the difference in retention time)true,false
use_library_score true Use the library score true,false
use_elution_model_score true Use the elution model (EMG) score (this score fits a gaussian model to the peak and checks the fit)true,false
use_intensity_score true Use the intensity score true,false
use_nr_peaks_score true Use the number of peaks score true,false
use_total_xic_score true Use the total XIC score true,false
use_total_mi_score false Use the total MI score true,false
use_sn_score true Use the SN (signal to noise) score true,false
use_mi_score false Use the MI (mutual information) score true,false
use_dia_scores true Use the DIA (SWATH) scores. If turned off, will not use fragment ion spectra for scoring.true,false
use_ms1_correlation false Use the correlation scores with the MS1 elution profiles true,false
use_sonar_scores false Use the scores for SONAR scans (scanning swath)true,false
use_ion_mobility_scores false Use the scores for Ion Mobility scans true,false
use_ms1_fullscan false Use the full MS1 scan at the peak apex for scoring (ppm accuracy of precursor and isotopic pattern)true,false
use_ms1_mi false Use the MS1 MI score true,false
use_uis_scores false Use UIS scores for peptidoform identification true,false
+++peptideEstimation Parameters for the peptide estimation (use -estimateBestPeptides to enable).
InitialQualityCutoff 0.5 The initial overall quality cutoff for a peak to be scored (range ca. -2 to 2)
OverallQualityCutoff 5.5 The overall quality cutoff for a peak to go into the retention time estimation (range ca. 0 to 10)
NrRTBins 10 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)
MinPeptidesPerBin 1 Minimal number of peptides that are required for a bin to counted as 'covered'
MinBinsFilled 8 Minimal number of bins required to be covered