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<div class="title">PTModel </div> </div>
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<div class="textblock"><p>Used to train a model for the prediction of proteotypic peptides.</p>
<p>The input consists of two files: One file contains the positive examples (the peptides which are proteotypic) and the other contains the negative examples (the nonproteotypic peptides).</p>
<p>Parts of this model has been described in the publication</p>
<p>Ole Schulz-Trieglaff, Nico Pfeifer, Clemens Gröpl, Oliver Kohlbacher and Knut Reinert LC-MSsim - a simulation software for Liquid Chromatography Mass Spectrometry data BMC Bioinformatics 2008, 9:423.</p>
<p>There are a number of parameters which can be changed for the svm (specified in the ini file): </p>
<ul>
<li>
kernel_type: the kernel function (e.g., POLY for the polynomial kernel, LINEAR for the linear kernel or RBF for the gaussian kernel); we recommend SVMWrapper::OLIGO for our paired oligo-border kernel (POBK) </li>
<li>
border_length: border length for the POBK </li>
<li>
k_mer_length: length of the signals considered in the POBK </li>
<li>
sigma: the amount of positional smoothing for the POBK </li>
<li>
degree: the degree parameter for the polynomial kernel </li>
<li>
c: the penalty parameter of the svm </li>
<li>
nu: the nu parameter for nu-SVC </li>
</ul>
<p>The last five parameters (sigma, degree, c, nu and p) are used in a cross validation (CV) to find the best parameters according to the training set. Thus, you have to specify the start value of a parameter, the step size in which the parameters should be increased and a final value for the particular parameter such that the tested parameter is never bigger than the given final value. If you want to perform a cross validation, for example, for the parameter c, you have to specify <b>c_start</b>, <b>c_step_size</b> and <b>c_stop</b> in the ini file. Let's say you want to perform a CV for c from 0.1 to 2 with step size 0.1. Open up your ini-file with INIFileEditor and modify the fields c_start, c_step_size, and c_stop accordingly.</p>
<p>If the CV should test additional parameters in a certain range you just include them analogously to the example above. Furthermore, you can specify the number of partitions for the CV with <b>number_of_partitions</b> in the ini file and the number of runs with <b>number_of_runs</b>.</p>
<p><br/>
Consequently you have two choices to use this application:</p>
<ol>
<li>
Set the parameters of the svm: The PTModel application will train the svm with the training data and store the svm model. </li>
<li>
Give a range of parameters for which a CV should be performed: The PTModel application will perform a CV to find the best parameter combination in the given range and afterwards train the svm with the best parameters and the whole training data. Then the model is stored. </li>
</ol>
<p><br/>
The model can be used in <a class="el" href="TOPP_PTPredict.html">PTPredict</a>, to predict the likelihood for peptides to be proteotypic.</p>
<p><b>The command line parameters of this tool are:</b> </p>
<pre class="fragment">
PTModel -- Trains a model for the prediction of proteotypic peptides from a training set.
Version: 1.11.1 Nov 14 2013, 11:18:15, Revision: 11976
Usage:
PTModel <options>
Options (mandatory options marked with '*'):
-in_positive <file>* Input file with positive examples (valid formats: 'idXML')
-in_negative <file>* Input file with negative examples (valid formats: 'idXML')
-out <file>* Output file: the model in libsvm format (valid formats: 'txt')
-c <float> The penalty parameter of the svm (default: '1')
-svm_type <type> The type of the svm (NU_SVC or C_SVC) (default: 'C_SVC' valid: 'NU_SVC',
'C_SVC')
-nu <float> The nu parameter [0..1] of the svm (for nu-SVR) (default: '0.5' min: '0'
max: '1')
-kernel_type <type> The kernel type of the svm (default: 'OLIGO' valid: 'LINEAR', 'RBF', 'POLY'
, 'OLIGO')
-degree <int> The degree parameter of the kernel function of the svm (POLY kernel) (defau
lt: '1' min: '1')
-border_length <int> Length of the POBK (default: '22' min: '1')
-k_mer_length <int> K_mer length of the POBK (default: '1' min: '1')
-sigma <float> Sigma of the POBK (default: '5')
-max_positive_count <int> Quantity of positive samples for training (randomly chosen if smaller than
available quantity) (default: '1000' min: '1')
-max_negative_count <int> Quantity of positive samples for training (randomly chosen if smaller than
available quantity) (default: '1000' min: '1')
-redundant If the input sets are redundant and the redundant peptides should occur
more than once in the training set, this flag has to be set
-additive_cv If the step sizes should be interpreted additively (otherwise the actual
value is multiplied with the step size to get the new value
Parameters for the grid search / cross validation::
-cv:skip_cv Has to be set if the cv should be skipped and the model should just be trai
ned with the specified parameters.
-cv:number_of_runs <int> Number of runs for the CV (default: '10' min: '1')
-cv:number_of_partitions <int> Number of CV partitions (default: '10' min: '2')
-cv:degree_start <int> Starting point of degree (default: '1' min: '1')
-cv:degree_step_size <int> Step size point of degree (default: '2')
-cv:degree_stop <int> Stopping point of degree (default: '4')
-cv:c_start <float> Starting point of c (default: '1')
-cv:c_step_size <float> Step size of c (default: '100')
-cv:c_stop <float> Stopping point of c (default: '1000')
-cv:nu_start <float> Starting point of nu (default: '0.1' min: '0' max: '1')
-cv:nu_step_size <float> Step size of nu (default: '1.3')
-cv:nu_stop <float> Stopping point of nu (default: '0.9' min: '0' max: '1')
-cv:sigma_start <float> Starting point of sigma (default: '1')
-cv:sigma_step_size <float> Step size of sigma (default: '1.3')
-cv:sigma_stop <float> Stopping point of sigma (default: '15')
Common TOPP options:
-ini <file> Use the given TOPP INI file
-threads <n> Sets the number of threads allowed to be used by the TOPP tool (default:
'1')
-write_ini <file> Writes the default configuration file
--help Shows options
--helphelp Shows all options (including advanced)
</pre><p> <b>INI file documentation of this tool:</b> <div class="ini_global">
<div class="legend">
<b>Legend:</b><br>
<div class="item item_required">required parameter</div>
<div class="item item_advanced">advanced parameter</div>
</div>
<div class="node"><span class="node_name">+PTModel</span><span class="node_description">Trains a model for the prediction of proteotypic peptides from a training set.</span></div>
<div class="item item_advanced"><span class="item_name" style="padding-left:16px;">version</span><span class="item_value">1.11.1</span>
<span class="item_description">Version of the tool that generated this parameters file.</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="node"><span class="node_name">++1</span><span class="node_description">Instance '1' section for 'PTModel'</span></div>
<div class="item"><span class="item_name item_required" style="padding-left:24px;">in_positive</span><span class="item_value"></span>
<span class="item_description">input file with positive examples</span><span class="item_tags">input file</span><span class="item_restrictions">*.idXML</span></div> <div class="item"><span class="item_name item_required" style="padding-left:24px;">in_negative</span><span class="item_value"></span>
<span class="item_description">input file with negative examples</span><span class="item_tags">input file</span><span class="item_restrictions">*.idXML</span></div> <div class="item"><span class="item_name item_required" style="padding-left:24px;">out</span><span class="item_value"></span>
<span class="item_description">output file: the model in libsvm format</span><span class="item_tags">output file</span><span class="item_restrictions">*.txt</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">c</span><span class="item_value">1</span>
<span class="item_description">the penalty parameter of the svm</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:24px;">svm_type</span><span class="item_value">C_SVC</span>
<span class="item_description">the type of the svm (NU_SVC or C_SVC)</span><span class="item_tags"></span><span class="item_restrictions">NU_SVC,C_SVC</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">nu</span><span class="item_value">0.5</span>
<span class="item_description">the nu parameter [0..1] of the svm (for nu-SVR)</span><span class="item_tags"></span><span class="item_restrictions">0:1</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">kernel_type</span><span class="item_value">OLIGO</span>
<span class="item_description">the kernel type of the svm</span><span class="item_tags"></span><span class="item_restrictions">LINEAR,RBF,POLY,OLIGO</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">degree</span><span class="item_value">1</span>
<span class="item_description">the degree parameter of the kernel function of the svm (POLY kernel)</span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">border_length</span><span class="item_value">22</span>
<span class="item_description">length of the POBK</span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">k_mer_length</span><span class="item_value">1</span>
<span class="item_description">k_mer length of the POBK</span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">sigma</span><span class="item_value">5</span>
<span class="item_description">sigma of the POBK</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:24px;">max_positive_count</span><span class="item_value">1000</span>
<span class="item_description">quantity of positive samples for training (randomly chosen if smaller than available quantity)</span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">max_negative_count</span><span class="item_value">1000</span>
<span class="item_description">quantity of positive samples for training (randomly chosen if smaller than available quantity)</span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">redundant</span><span class="item_value">false</span>
<span class="item_description">if the input sets are redundant and the redundant peptides should occur more than once in the training set, this flag has to be set</span><span class="item_tags"></span><span class="item_restrictions">true,false</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">additive_cv</span><span class="item_value">false</span>
<span class="item_description">if the step sizes should be interpreted additively (otherwise the actual value is multiplied with the step size to get the new value</span><span class="item_tags"></span><span class="item_restrictions">true,false</span></div> <div class="item item_advanced"><span class="item_name" style="padding-left:24px;">log</span><span class="item_value"></span>
<span class="item_description">Name of log file (created only when specified)</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item item_advanced"><span class="item_name" style="padding-left:24px;">debug</span><span class="item_value">0</span>
<span class="item_description">Sets the debug level</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:24px;">threads</span><span class="item_value">1</span>
<span class="item_description">Sets the number of threads allowed to be used by the TOPP tool</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item item_advanced"><span class="item_name" style="padding-left:24px;">no_progress</span><span class="item_value">false</span>
<span class="item_description">Disables progress logging to command line</span><span class="item_tags"></span><span class="item_restrictions">true,false</span></div> <div class="item item_advanced"><span class="item_name" style="padding-left:24px;">test</span><span class="item_value">false</span>
<span class="item_description">Enables the test mode (needed for internal use only)</span><span class="item_tags"></span><span class="item_restrictions">true,false</span></div> <div class="node"><span class="node_name">+++cv</span><span class="node_description">Parameters for the grid search / cross validation:</span></div>
<div class="item"><span class="item_name" style="padding-left:32px;">skip_cv</span><span class="item_value">false</span>
<span class="item_description">Has to be set if the cv should be skipped and the model should just be trained with the specified parameters.</span><span class="item_tags"></span><span class="item_restrictions">true,false</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">number_of_runs</span><span class="item_value">10</span>
<span class="item_description">number of runs for the CV</span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">number_of_partitions</span><span class="item_value">10</span>
<span class="item_description">number of CV partitions</span><span class="item_tags"></span><span class="item_restrictions">2:∞</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">degree_start</span><span class="item_value">1</span>
<span class="item_description">starting point of degree</span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">degree_step_size</span><span class="item_value">2</span>
<span class="item_description">step size point of degree</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">degree_stop</span><span class="item_value">4</span>
<span class="item_description">stopping point of degree</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">c_start</span><span class="item_value">1</span>
<span class="item_description">starting point of c</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">c_step_size</span><span class="item_value">100</span>
<span class="item_description">step size of c</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">c_stop</span><span class="item_value">1000</span>
<span class="item_description">stopping point of c</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">nu_start</span><span class="item_value">0.1</span>
<span class="item_description">starting point of nu</span><span class="item_tags"></span><span class="item_restrictions">0:1</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">nu_step_size</span><span class="item_value">1.3</span>
<span class="item_description">step size of nu</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">nu_stop</span><span class="item_value">0.9</span>
<span class="item_description">stopping point of nu</span><span class="item_tags"></span><span class="item_restrictions">0:1</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">sigma_start</span><span class="item_value">1</span>
<span class="item_description">starting point of sigma</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">sigma_step_size</span><span class="item_value">1.3</span>
<span class="item_description">step size of sigma</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">sigma_stop</span><span class="item_value">15</span>
<span class="item_description">stopping point of sigma</span><span class="item_tags"></span><span class="item_restrictions"> </span></div></div>
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