ComputationalPredictionofProteotypicPeptides
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PeptideSieve
Reference:Nat Biotechnol. 2007 Jan;25(1):125-31. Computational prediction of proteotypic peptides for quantitative proteomics. Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, Ranish J, Raught B, Schmitt R, Werner T, Kuster B, Aebersold R.
Getting the software: New native C++ version (.51) released 5/2008: download the peptideSieve files from the Sashimi project at SourceForge. Linux, os x and windows binaries (PeptideSieve.exe PeptideSieve.linux.i386 PeptideSieve.osx.i386) are available.
A GUI windows version is available from our collaborator Chee-Hong! It is updated to PeptideSieve version .51
Running the software:
PeptideSieve is a commandline utility. Running it sans arguments gives the usage instructions: PeptideSieve: Identify Proteotypic Peptides from a FASTA or TXT file. Version - 0.6 Options: -O [ --outputDirectory ] arg : set output directory -e [ --outputExtension ] arg : set extension for output files -o [ --outputFile ] arg : output file name if not input.extension -P [ --propertyFile ] arg (=properties.txt) : set property file -f [ --inputFormat ] arg (=FASTA) : FASTA or TXT, specifying input format -l [ --minSeqLength ] arg (=6) : minimum sequence length to consider -L [ --maxSeqLength ] arg (=40) : maximum sequence length to consider -m [ --minMass ] arg (=400) : minimum mass to consider -M [ --maxMass ] arg (=3000) : maximum mass to consider -c [ --numAllowedMisCleavages ] arg (=0): maximum number of miscleavages to consider -s [ --saveConvertedFile ] : save the converted propertyFile -h [ --help ] : display usage information -d [ --experimentalDesign ] arg (=PAGE_MALDI.txt): which design to return, any of the following, in quotes, comma separated "PAGE_MALDI.txt,PAGE_ESI.tx t,MUDPIT_ESI.txt,MUDPIT_ICA T.txt" -p [ --pValue ] arg (=0.80000000000000004): only return peptides with p values greater than X example usages: Simple Run with Fasta : PeptideSieve shortExample.tfa Simple Run with txt: PeptideSieve -f TXT example.txt Specify Classifiers: PeptideSieve -d "MUDPIT_ESI,PAGE_MALDI" -f TXT example.txt Make Properties File and Quit: PeptideSieve -d -s -f TXT example.txt
It is CRITICAL to either place the properties.txt file in the directory where PeptideSieve is being executed or to specify the location of properties.txt using the -P flag or PeptideSieve will work very strangely.
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 this:
- 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 acids are 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 this.
- 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:Webb-Robertson BJ, Cannon WR, Oehmen CS, Shah AR, Gurumoorthi V, Lipton MS, Waters KM. A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics. Bioinformatics, 2008 Jul 1;24(13):1503-9. Epub 2008 May 3.
Classifier: SVM (Support Vector Machine)
Getting the software: The software is available from STEPP