ComputationalPredictionofProteotypicPeptides

From SPCTools

(Difference between revisions)
Jump to: navigation, search
Revision as of 19:03, 13 April 2010
Zsun (Talk | contribs)

← Previous diff		 Revision as of 19:52, 13 April 2010
Zsun (Talk | contribs)

Next diff →
Line 39: Line 39:
This tool is still under development. If you want more information, please contact lars@imsb.biol.ethz.ch This tool is still under development. If you want more information, please contact lars@imsb.biol.ethz.ch
 +
 +===STEPP===
 +'''Reference''':[http://bioinformatics.oxfordjournals.org/cgi/content/full/24/13/1503 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 [http://omics.pnl.gov/software/STEPP.php STEPP]

Revision as of 19:52, 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.
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

Retrieved from "http://tools.proteomecenter.org/wiki/index.php?title=ComputationalPredictionofProteotypicPeptides"