User: Sonja Holl

RTCalc Retention Time Prediction and Outli... (1)

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This workflow takes as input a pepXML file from PeptideProphet, applied RTCalc and outputs a filtered list of peptides based on the retention time Z-scores.

Created: 2014-02-06 | Last updated: 2014-02-06

Credits: Magnus Palmblad Sonja Holl

EBI NCBI BLAST filter e-value and length (1)

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The workflow queries the NCBI BLAST web service and extracts the e-values and length of the results. One of the sub-workflows filters the length first and afterwards the e-value and the other sub-workflow filters first the e-value and then the length. As the result may be different, this workflow was used for optimization purposes to find the sub-workflow performing best.

Created: 2013-08-23 | Last updated: 2014-02-06

Credits: Sonja Holl

Attributions: EBI_NCBI_BLAST

Biomarker Identification via RFE on the Grid (2)

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The first two components split the original data set into several sub-sampling sets. The RFE component performs the machine learning approach by executing several instances of a SVM, each of which consuming one sub-sample data set. The execution of the SVM takes place in a distributed computing environment, using the UNICORE-Taverna Plugin. The calc_objFunc component calculates the F-measure of the ranked gene list compared to a 'gold standard'.

Created: 2013-08-13 | Last updated: 2013-09-24

Credits: Sonja Holl

Attributions: Biomarker Identification via EFS on the Grid

Biomarker Identification via EFS on the Grid (2)

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The first two components split the original data set into several sub-sampling sets. The EFS component performs the machine learning approach by executing several instances of a SVM, each of which consuming one sub-sample data set. Another level of SVM execution is added by taking bootstapping into account. The execution of all SVMs takes place in a distributed computing environment using the UNICORE-Taverna plugin. The calc_objFunc component calculates the F-measure of the ranked gene list ...

Created: 2013-08-13 | Last updated: 2013-09-24

Credits: Sonja Holl

Attributions: Biomarker Identification via RFE on the Grid

X!Tandem and PeptideProphet on the Grid (1)

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The workflow performs the execution of X!Tandem and PeptideProphet from the TPP toolbox on the Grid. The execution is performed by the UNICORE Plugin for Taverna. mzXMLDecomposer/Composer is used to run the execution of X!Tandem in parallel. extract_values extract relevant information from thetandem.interact.pep.xml File. The file can then remain on the remote storage.

Created: 2013-08-13 | Last updated: 2013-09-04

Credits: Sonja Holl Yassene Magnus Palmblad

Attributions: de Bruin et al. Workflow 1 Cloud Parallel Processing of Tandem Mass Spectrometry Based Proteomics Data: X!Tandem

Optimization of retention time prediction (1)

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The workflow uses RTCalc from the TPP toolbox to perform two different retention time predictions. The third branch uses a linear retention time predictor (Palmblad et al., 2002). The workflow has a flag that switches on a specific branch.

Created: 2013-08-13 | Last updated: 2013-09-04

Credits: Sonja Holl Yassene Magnus Palmblad

Attributions: Retention Time Prediction with X!Tandem

ENM SVM workflow used for optimization (1)

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This workflow was used for the optimization of the SVM algorithm from the openModeller toolbox (http://openmodeller.sourceforge.net). The workflow uses 10-fold cross-validation and then calculates the average AUC, which can be used as fitness value during parameter optimization.

Created: 2013-08-09 | Last updated: 2013-09-04

Credits: Sonja Holl Renato De Giovanni

ENM Maxent workflow used for optimization (1)

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This workflow was used for the optimization of the Maxent algorithm from the openModeller toolbox (http://openmodeller.sourceforge.net/). The workflow uses 10-fold cross-validation and then calculates the average AUC, which can be used as fitness value during parameter optimization.

Created: 2013-08-09 | Last updated: 2013-09-04

Credits: Sonja Holl Renato De Giovanni