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FeatureFinderIdentification

Generic Workflow Nodes for KNIME: OpenMS version 2.6.0.202009301213 by Freie Universitaet Berlin, Universitaet Tuebingen, and the OpenMS Team

Detects features in MS1 data based on peptide identifications.

Web Documentation for FeatureFinderIdentification

Options

version
Version of the tool that generated this parameters file.
debug
Sets the debug level
log
Name of log file (created only when specified)
threads
Sets the number of threads allowed to be used by the TOPP tool
no_progress
Disables progress logging to command line
force
Overrides tool-specific checks
test
Enables the test mode (needed for internal use only)
batch_size
Nr of peptides used in each batch of chromatogram extraction. Smaller values decrease memory usage but increase runtime.
mz_window
m/z window size for chromatogram extraction (unit: ppm if 1 or greater, else Da/Th)
n_isotopes
Number of isotopes to include in each peptide assay.
isotope_pmin
Minimum probability for an isotope to be included in the assay for a peptide. If set, this parameter takes precedence over 'extract:n_isotopes'.
rt_quantile
Quantile of the RT deviations between aligned internal and external IDs to use for scaling the RT extraction window
rt_window
RT window size (in sec.) for chromatogram extraction. If set, this parameter takes precedence over 'extract:rt_quantile'.
peak_width
Expected elution peak width in seconds, for smoothing (Gauss filter). Also determines the RT extration window, unless set explicitly via 'extract:rt_window'.
min_peak_width
Minimum elution peak width. Absolute value in seconds if 1 or greater, else relative to 'peak_width'.
signal_to_noise
Signal-to-noise threshold for OpenSWATH feature detection
mapping_tolerance
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.
samples
Number of observations to use for training ('0' for all)
no_selection
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').
kernel
SVM kernel
xval
Number of partitions for cross-validation (parameter optimization)
log2_C
Values to try for the SVM parameter 'C' during parameter optimization. A value 'x' is used as 'C = 2^x'.
log2_gamma
Values to try for the SVM parameter 'gamma' during parameter optimization (RBF kernel only). A value 'x' is used as 'gamma = 2^x'.
epsilon
Stopping criterion
cache_size
Size of the kernel cache (in MB)
no_shrinking
Disable the shrinking heuristics
predictors
Names of OpenSWATH scores to use as predictors for the SVM (comma-separated list)
min_prob
Minimum probability of correctness, as predicted by the SVM, required to retain a feature candidate
type
Type of elution model to fit to features
add_zeros
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.
unweighted_fit
Suppress weighting of mass traces according to theoretical intensities when fitting elution models
no_imputation
If fitting the elution model fails for a feature, set its intensity to zero instead of imputing a value from the initial intensity estimate
each_trace
Fit elution model to each individual mass trace
min_area
Lower bound for the area under the curve of a valid elution model
boundaries
Time points corresponding to this fraction of the elution model height have to be within the data region used for model fitting
width
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').
asymmetry
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').

Input Ports

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Input file: LC-MS raw data [mzML]
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Input file: Peptide identifications derived directly from 'in' [idXML]
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Input file: 'External' peptide identifications (e.g. from aligned runs) [idXML,opt.]
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Input file: Feature candidates from a previous run. If set, only feature classification and elution model fitting are carried out, if enabled. Many parameters are ignored. [featureXML,opt.]

Output Ports

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Output file: Features [featureXML]
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Output file: Assay library [traML]
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Output file: Chromatograms [mzML]
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Output file: Feature candidates (before filtering and model fitting) [featureXML]
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Output file: SVM cross-validation (parameter optimization) results [csv]

Views

FeatureFinderIdentification Std Output
The text sent to standard out during the execution of FeatureFinderIdentification.
FeatureFinderIdentification Error Output
The text sent to standard error during the execution of FeatureFinderIdentification. (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)

Workflows

Installation

To use this node in KNIME, install OpenMS from the following update site:

KNIME 4.3

A zipped version of the software site can be downloaded here.

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Developers

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