Complete workflow to run OpenSWATH
Web Documentation for OpenSwathWorkflow
Version of the tool that generated this parameters file.
input file type -- default: determined from file extension or content
Sort of input SWATH files when matching to SWATH windows from swath_windows_file
Extract the precursor ion trace(s) and use for scoring
Enable additional scoring of identification assays
TSV output file (mProphet compatible)
Minimal distance to the edge to still consider a precursor, in Thomson
Only extract RT around this value (-1 means extract over the whole range, a value of 600 means to extract around +/- 300 s of the expected elution).
Output an XIC with a RT-window that by this much larger (e.g. to visually inspect a larger area of the chromatogram)
Extraction window used (in Thomson, to use ppm see -ppm flag)
m/z extraction_window is in ppm
Minimum r-squared of RT peptides regression
Minimum relative amount of RT peptides to keep
The input files each contain one single SWATH (alternatively: all SWATH are in separate files)
Turn on elution model score (EMG fit to peak)
Whether to run OpenSWATH directly on the input data, cache data to disk first or to perform a datareduction step first. If you choose cache, make sure to also set tempDirectory
Use the retention time normalization peptide MS2 masses to perform a mass correction (linear, weighted by intensity linear or quadratic) of all spectra.
Temporary directory to store cached files for example
Function used to extract the signal
The batch size of chromatograms to process (0 means to only have one batch, sensible values are around 500-1000)
Name of log file (created only when specified)
Sets the debug level
Sets the number of threads allowed to be used by the TOPP tool
Disables progress logging to command line
Overwrite tool specific checks.
Enables the test mode (needed for internal use only)
Stop reporting after feature (ordered by quality; -1 means do not stop).
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)
Cutoff in m/z below which peaks should not be used for quantification any more
Whether to write out all points of all features into the featureXML
S/N threshold to consider identification transition (set to -1 to consider all)
Peak area threshold to consider identification transition (set to -1 to consider all)
Stop finding after feature (ordered by intensity; -1 means do not stop).
Minimal peak width (s), discard all peaks below this value (-1 means no action).
Try to apply a background subtraction to the peak (experimental). The background is estimated at the peak boundaries, either the smoothed or the raw chromatogram data can be used for that.
Tries to get better peak picking by looking at peak consistency of all picked peaks. Tries to use the consensus (median) peak border if theof variation within the picked peaks is too large.
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).
Only if compute_peak_quality is set, this parameter will not consider peaks below this quality threshold
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.
The number of subsequent data points used for smoothing.
This number has to be uneven. If it is not, 1 will be added.
Order of the polynomial that is fitted.
Gaussian width in seconds, estimated peak size.
Use Gaussian filter for smoothing (alternative is Savitzky-Golay filter)
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 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.
Write out log messages of the signal-to-noise estimator in case of sparse windows or median in rightmost histogram bin
Try to remove overlapping peaks during peak picking
Which method to choose for chromatographic peak-picking (OpenSWATH legacy, corrected picking or Crawdad).
DIA extraction window in Th.
Use centroded DIA data.
DIA b/y series minimum intensity to consider.
DIA b/y series minimal difference in ppm to consider.
DIA nr of isotopes to consider.
DIA nr of charges to consider.
DIA maximal difference in ppm to count a peak at lower m/z when searching for evidence that a peak might not be monoisotopic.
Maximum number of iterations using by Levenberg-Marquardt algorithm.
Use the shape score (this score measures the similarity in shape of the transitions using a cross-correlation)
Use the coelution score (this score measures the similarity in coelution of the transitions using a cross-correlation)
Use the retention time score (this score measure the difference in retention time)
Use the library score
Use the intensity score
Use the number of peaks score
Use the total XIC score
Use the SN (signal to noise) score
Use the DIA (SWATH) scores
Use the correlation scores with the MS1 elution profiles
Use the full MS1 scan at the peak apex for scoring (ppm accuracy of precursor and isotopic pattern)
Use UIS scores for peptidoform identification
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).
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.
Maximum iterations for the RANSAC outlier detection algorithm.
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.
Sampling size of data points per iteration for the RANSAC outlier detection algorithm.
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).
The initial overall quality cutoff for a peak to be scored (range ca. -2 to 2)
The overall quality cutoff for a peak to go into the retention time estimation (range ca. 0 to 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)
Minimal number of peptides that are required for a bin to counted as 'covered'
Minimal number of bins required to be covered