annotate PTPredict.xml @ 5:8cbe72a79d8b draft

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date Mon, 12 Feb 2018 12:54:00 -0500
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1 <?xml version='1.0' encoding='UTF-8'?>
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2 <!--This is a configuration file for the integration of a tools into Galaxy (https://galaxyproject.org/). This file was automatically generated using CTDConverter.-->
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3 <!--Proposed Tool Section: [Peptide property prediction]-->
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4 <tool id="PTPredict" name="PTPredict" version="2.3.0">
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5 <description>predicts the likelihood of peptides to be proteotypic via svm_model which is trained by PTModel</description>
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6 <macros>
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7 <token name="@EXECUTABLE@">PTPredict</token>
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8 <import>macros.xml</import>
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9 </macros>
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10 <expand macro="references"/>
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11 <expand macro="stdio"/>
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12 <expand macro="requirements"/>
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13 <command>PTPredict
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15 #if $param_in:
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16 -in $param_in
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17 #end if
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18 #if $param_out:
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19 -out $param_out
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20 #end if
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21 #if $param_svm_model:
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22 -svm_model $param_svm_model
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23 #end if
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24 #if $param_max_number_of_peptides:
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25 -max_number_of_peptides $param_max_number_of_peptides
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26 #end if
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27 #if $adv_opts.adv_opts_selector=='advanced':
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28 #if $adv_opts.param_force:
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29 -force
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30 #end if
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31 #end if
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32 </command>
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33 <inputs>
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34 <param name="param_in" type="data" format="idxml" optional="False" label="input file" help="(-in) "/>
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35 <param name="param_svm_model" type="data" format="txt" optional="False" label="svm model in libsvm format (can be produced by PTModel)" help="(-svm_model) "/>
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36 <param name="param_max_number_of_peptides" type="integer" value="100000" label="the maximum number of peptides considered at once (bigger number will lead to faster results but needs more memory)" help="(-max_number_of_peptides) &lt;br&gt;"/>
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37 <expand macro="advanced_options">
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38 <param name="param_force" display="radio" type="boolean" truevalue="-force" falsevalue="" checked="false" optional="True" label="Overwrite tool specific checks" help="(-force) "/>
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39 </expand>
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40 </inputs>
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41 <outputs>
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42 <data name="param_out" format="idxml"/>
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43 </outputs>
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44 <help>predicts the likelihood of peptides to be proteotypic via svm_model which is trained by PTModel
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45
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46
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47 For more information, visit http://ftp.mi.fu-berlin.de/OpenMS/release-documentation/html/TOPP_PTPredict.html</help>
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48 </tool>