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OpenMS
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Used to train a model for peptide retention time prediction or peptide separation prediction.
For retention time prediction, a support vector machine is trained with peptide sequences and their measured retention times. For peptide separation prediction, two files have to be given: One file contains the positive examples (the peptides which are collected) and the other contains the negative examples (the flowthrough peptides).
These methods and applications of this model are described in the following publications:
Nico Pfeifer, Andreas Leinenbach, Christian G. Huber and Oliver Kohlbacher Statistical learning of peptide retention behavior in chromatographic separations: A new kernel-based approach for computational proteomics. BMC Bioinformatics 2007, 8:468
Nico Pfeifer, Andreas Leinenbach, Christian G. Huber and Oliver Kohlbacher Improving Peptide Identification in Proteome Analysis by a Two-Dimensional Retention Time Filtering Approach J. Proteome Res. 2009, 8(8):4109-15
There are a number of parameters which can be changed for the svm (specified in the ini file and command line):
The last five parameters (sigma, degree, c, nu and p) can be used in a cross validation (CV) to find the best parameters according to the training set. Therefore you have to specify the start value of a parameter, the step size in which the parameters should be increased and a final value for the particular parameter such that the tested parameter is never bigger than the given final value. If you want to perform a cross validation for example for the parameter c, enable CV (across all 5 parameters) and set skip_cv to false in the INI file. This can be easily done with using the INIFileEditor.
Furthermore, you can specify the number of partitions for the CV with number_of_partitions in the ini file and the number of runs with number_of_runs.
Consequently you have two choices to use this application:
The model can be used in RTPredict, to predict retention times for peptides or peptide separation depending on how you trained the model.
The command line parameters of this tool are:
INI file documentation of this tool: