ComputationalPredictionofProteotypicPeptides

From SPCTools

Contents 1 ESPPredictor 2 Detectability Predictor 3 APEX 4 STEPP

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.

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

Reference:A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics

Classifier: SVM (Support Vector Machine)

How to run: The software is available from STEPP