ComputationalPredictionofProteotypicPeptides
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| '''Classifier''': 30 two-layer feed-forward neural networks trained using the resilient back propagation algorithm | '''Classifier''': 30 two-layer feed-forward neural networks trained using the resilient back propagation algorithm | ||
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| '''How to run''': | '''How to run''': | ||
| There are also two ways of running it. | There are also two ways of running it. | ||
Revision as of 20:17, 13 April 2010
Contents |
ESPPredictor
Reference:Prediction of high-responding peptides for targeted protein assays by mass spectrometry Vincent A. Fusaro, D. R. Mani, Jill P. Mesirov & Steven A. Carr Nature Biotechnology (2009) 27:190-198.
Classfier: Random forest
How to run the module
There are two ways of running it:
- Using genepattern web service tool hosted by Broad Institute. There is a detailed instruction on how to run it. The tool can accept the peptide list only. The invalid amino acid is not allowed, such as B, J, U, O, Z and X.
- Through command line
- SYSTEM requirement: R, matlab, Java
- Follow the first two steps of "How to run the module" in the instruction page.
- Click export on the right hand side of reset button and between "properties" and "help" text to export a zip file, which contains the program source files.
- Follow the first two steps of "How to run the module" in the instruction page.
- You will need to do a little bit of modification on ESPPredictor.java file to let it parse the command line parameters correctly, since the class, CmdSplitter, does not exist. After a simple modification, my local ESPPredictor can run using the following command line. The "zzz" phrase is the separator for the input parameters of the matlab and R program.
- java -classpath <libdir>/../ ESPPredictor.ESPPredictor <libdir> peptideFeatureSet <input.file> zzz \
- <R2.5_HOME> <libdir>/ESP_Predictor.R Predict <libdir>PeptideFeatureSet.csv <libdir>ESP_Predictor_Model_020708.RData
Detectability Predictor
Reference: H. Tang, R. J. Arnold, P. Alves, Z. Xun, D. E. Clemmer, M. V. Novotny, J. P. Reilly, P. Radivojac. A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics, (2006) 22 (14): e481-e488.
Classifier: 30 two-layer feed-forward neural networks trained using the resilient back propagation algorithm
How to run: There are also two ways of running it.
- Through Delectability Predictor web service tool hosted by Indiana University.
- Through command line: you need to make request to hatang@indiana.edu in order to get the standalone version
APEX
This tool is still under development. If you want more information, please contact lars@imsb.biol.ethz.ch
STEPP
Classifier: SVM (Support Vector Machine)
How to run: The software is available from STEPP
