PeptideSieveRetrainingTutorial

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Contents 1 PeptideSieve Retraining and Predictor Combining Tutorial 1.1 PeptideSieve Retraining 1.1.1 Reference 1.1.2 Getting the PeptideSieve Software 1.1.3 Getting LNKnet 1.1.4 INSTALLING LNKnet 1.1.5 Training Steps 1.1.5.1 Protein Selection 1.1.5.2 Peptides Selection 1.1.5.3 Selecting Features to Start Feature Selection 1.1.5.4 Converting the Peptide List to Property Vector (training file) 1.1.5.5 Feature Selection 1.1.5.6 Training 1.1.5.7 Code Generation Using an LNKnet Parameter File 1.1.5.8 Create New MUDPIT_ESI.txt file for the PeptideSieve 1.2 ESPPredictor 1.3 Detectability Predictor 1.4 APEX 1.5 Combining Predictors

PeptideSieve Retraining and Predictor Combining Tutorial

PeptideSieve Retraining

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 PeptideSieve Software

The source codes are available from PeptideSieve

The Binary executable can be downloaded from PeptideSieve

Files required to run the program: properties.txt and one or all of the experiment designing file, MUDPIT_ESI.txt, MUDPIT_ICAT.txt, PAGE_ESI.txt, and PAGE_MALDI.txt

Getting LNKnet

LNKnet software is public domain software made available by MIT Lincoln Laboratory. It can be downloaded from LNKnet

INSTALLING LNKnet

Unzip the package, and install the LNKnet on your computer according to INSTALL.src file. It is good idea to go through the LNKnet quick start guide using the the graphical user interface. When you perform an action on the graphical user interface, it gives an option to store a shell script. This script can then be edited and started from a shell window or called.

Training Steps

Protein Selection

Use PeptideAtlas build "Yeast non-ICAT PeptideAtlas 2009-12", and select proteins with minimum sequence length of 200AA and have protein coverage of 60-90%. 451 proteins are selected using this criteria. The protein list can be downloaded from [here].

Peptides Selection

• Proteotyptic peptides: 1439 peptides are selected using the following three criteria.

• observed tryptic peptide;

• observed in at least 4 samples

• Empirical Proteotypic Score (EPS) >= 0.5; EPS = Nsamples(peptide)/Nsamples(parent protein)

• NON-Proteotyptic peptides: All none observed peptides from the selected proteins are grouped into this category; There 1645 peptides in the group.

The peptide list can be downloaded from [here].

Selecting Features to Start Feature Selection

Parag started his feature selection using 1010 features (numeric physicochemical property scales for amino acids, peptide length, amino acid counts and peptide mass). It is too slow to do the features selection using all the features. I choose a subset of features based on PeptideSieve finally selected feature list across four different experiment designs, and the top 35 features from the ESP predictor. Through this way, I select 98 features to star with. The 98 features include peptide mass, peptide length, 20 AA counts, and 38 numeric physicochemical property scales for amino acids (mean and sum of the scores).

Converting the Peptide List to Property Vector (training file)

In order to do the training, each peptide needs to be represented by a fixed-length vector of real or discrete valued features. In this case, each peptide will be represented by 98 features. Each amino acid of a given peptide is replaced by a numerical value for each selected property and each property value was summed and average for each peptide result in a 76 dimensional property vector, plus the 20 amino acid compositions, and the length and mass of the peptide. Finally, a binary output class label was added to each feature vector. 1(positive) for proteotypic peptides and 0 otherwise.

Feature Selection

After creating the training file, a description file needs to be created. It should have the same base name as the training file. For example, if you name your training file as test.train, then the description file should be test.default. The description file contain the information about database type, number of input, number of output, and class labels, and input features label. The last two are optional.

It is stated in the LNKnet user guide that "For many classifiers, classification results are improved when the data has been normalized in some way." So I choose simple normalization to normalize the training input, test.train. You can do this either through graphical user interface or through command line. Here is an example command.

#!/bin/csh -ef

# ./test.norm.simple.run

norm_lnk -pathdata $path \

-finput test.train -fdescribe test.defaults -normalization 1\

-fparam test.norm.simple -debug 0 -verbose 3 \

|& nn_tee -h test.norm.simple.log

For feature selection, I use forward and backward searches. These searches select features one at a time based on the increase or decrease in the error rate measured using cross validation and any classifier. Once the feature selection search has complete, a subset of features can be selected for use in classification. This subset can be the first and presumably most important features. Here is the shell script to run forward and backward feature selection.

#!/bin/csh -ef

# ./test.for_bk.S.run

set loc=`pwd`

#feature selection

mlp_feat_sel -pathexp $loc -ferror X1mlp.err.cv -fparam X1mlp.param\

-pathdata $path\

-finput test.train -fdescribe test.defaults -npatterns 3084 -ninputs 98\

-normalize -fnorm test.norm.simple -cross_valid 4\

-fcross_valid vowel.test.cv -random_cv -random -seed 0\

-priors_npatterns 0 -debug 0 -verbose 3 -verror 0 \

-nodes 98,25,2 -alpha 0.6 -etta 0.1 -etta_change_type 0 -epsilon 0.1\

-kappa 0.01 -etta_nepochs 0 -decay 0 -tolerance 0.01 -hfunction 0\

-ofunction 0 -sigmoid_param 1 -cost_func 0 -cost_param 1 -epochs 30\

-batch 1,1,0 -init_mag 0.1 \

-search_type 2 -search_n 0 -search_verbosity 3\

-search_fparam test.for_bk.S.param -roc_target 0 -roc_lower 0 -roc_upper 1 \

|& nn_tee -h test.for_bk.S.log

Training

After the feature selection, you get a list of selected features, and then it is the time for the training.

#!/bin/csh -ef

# ./X1mlp.run

set loc=`pwd`

#train

(time mlp_lnk\

-create -pathexp $loc -ferror X1mlp.err.train -fparam X1mlp.param\

-pathdata $path\

-finput test.train -fdescribe test.defaults -npatterns 3084 -ninputs 13\

-features 70,3,71,60,8,26,45,97,61,22,46,14,81 -normalize -fnorm test.norm.simple\

-cross_valid 10 -fcross_valid vowel.test.cv -random -seed 0 -priors_npatterns 0 \

-debug 0 -verbose 3 -verror 0 \

-nodes 13,25,2 -alpha 0.6 -etta 0.01 -etta_change_type 0 -epsilon 0.1\

-kappa 0.01 -etta_nepochs 0 -decay 0 -tolerance 0.01 -hfunction 0\

-ofunction 0 -sig_param_list 1,1 -sigmoid_param 1 -cost_func 0 -cost_param 1 \

-epochs 30 -batch 1,1,0 -init_mag 0.1 \

)|& nn_tee -h X1mlp.log

echo -n "X1mlp.run" >> $path/LNKnet.note

grep "LAST TRAIN EPOCH" X1mlp.log | tail -1 >> $path/LNKnet.note

echo "current directory:" >> X1mlp.log

echo $loc >> X1mlp.log

You may already see from the command above, 13 features are selected from the 98 input features using feature selection program, and used for the final training. You cat get slightly different error rate when you vary the number of epoch step, etta value, and number of internal node.

Code Generation Using an LNKnet Parameter File

LNKnet has a filter program for each classification algorithm that generates C subroutines for pattern classification. Each filter program takes as an argument an algorithm parameter file, for this case it is X1mlp.param file. The program prints a subroutine, classify (), to the UNIX standard output stream. This subroutine can be called from a C program to classify patterns. The command to run the code generation:

#!/bin/csh -ef

# ./X1mlp.c.run

mlp2c -model_file X1mlp.param -suffix X1mlp >! X1mlp.c

Create New MUDPIT_ESI.txt file for the PeptideSieve

Finally, we have reached our goal of creating a new parameter file for PeptideSieve. In this retraining, we only retrain the parameter file for the experiment type, MUDPIT_ESI.

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.

• 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 standalone version

APEX

This tool is still under development. If you want more information, please contact Lars

Combining Predictors

We use the same set of peptides used to retrain PeptideSieve to do the training of combining the four different predictors. The steps of doing the training are simple. The predictor scores are the features for this training, so there are four features for each peptide, and the class label is inherited from the previous training file. The feature selection step is skipped since we want to include all the predictors. The training and code generation steps are same as above.