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Generic Workflow Nodes for KNIME: OpenMS version by Freie Universitaet Berlin, Universitaet Tuebingen, and the OpenMS Team

Corrects retention time distortions between maps based on common peptide identifications.

Web Documentation for MapAlignerIdentification


Version of the tool that generated this parameters file.
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
Overrides tool-specific checks
Enables the test mode (needed for internal use only)
Use one of the input files as reference ('1' for the first file, etc.). If '0', no explicit reference is set - the algorithm will select a reference.
If only IDs above a score cutoff should be used. Used together with min_score.
Minimum score for an ID to be considered. Applies to the last score calculated. Unless you have very few runs or identifications, increase this value to focus on more informative peptides.
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.
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.
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.
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'.
Type of model
Perform linear regression on 'y - x' vs. 'y + x', instead of on 'y' vs. 'x'.
Weight x values
Weight y values
Minimum x value
Maximum x value
Minimum y value
Maximum y 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.
Number of nodes for B-spline fitting. Overrides 'wavelength' if set (to two or greater). A lower value means more smoothing.
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).
Boundary condition at B-spline endpoints: 0 (value zero), 1 (first derivative zero) or 2 (second derivative zero)
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.
Number of robustifying iterations for lowess fitting.
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.
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 interpolation
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.
Type of interpolation to apply.
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.

Input Ports

Input files to align (all must have the same file type) [featureXML,consensusXML,idXML]
input file containing the experimental design [tsv,opt.]
File to use as reference [featureXML,consensusXML,idXML,opt.]

Output Ports

Output files (same file type as 'in'). This option or 'trafo_out' has to be provided; they can be used together. [featureXML,consensusXML,idXML]
Transformation output files. This option or 'out' has to be provided; they can be used together. [trafoXML]


MapAlignerIdentification Std Output
The text sent to standard out during the execution of MapAlignerIdentification.
MapAlignerIdentification Error Output
The text sent to standard error during the execution of MapAlignerIdentification. (If it appears in gray, it's the output of a previously failing run which is preserved for your trouble shooting.)

Best Friends (Incoming)

Best Friends (Outgoing)



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