annotate PTPredict.xml @ 4:6ead64a594bd draft default tip

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author bgruening
date Wed, 27 Jan 2016 10:06:49 -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 CTD2Galaxy.-->
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3 <!--Proposed Tool Section: [Peptide property prediction]-->
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4 <tool id="PTPredict" name="PTPredict" version="2.0.0">
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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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14
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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 -threads \${GALAXY_SLOTS:-24}
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28 #if $adv_opts.adv_opts_selector=='advanced':
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29 #if $adv_opts.param_force:
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30 -force
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31 #end if
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32 #end if
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33 </command>
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34 <inputs>
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35 <param format="xml" help="(-in) " label="input file" name="param_in" optional="False" type="data"/>
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36 <param format="txt" help="(-svm_model) " label="svm model in libsvm format (can be produced by PTModel)" name="param_svm_model" optional="False" type="data"/>
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37 <param help="(-max_number_of_peptides) &lt;br&gt;" label="the maximum number of peptides considered at once (bigger number will lead to faster results but needs more memory)" name="param_max_number_of_peptides" type="integer" value="100000"/>
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38 <expand macro="advanced_options">
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39 <param checked="false" falsevalue="" help="(-force) " label="Overwrite tool specific checks" name="param_force" optional="True" truevalue="-force" type="boolean"/>
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40 </expand>
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41 </inputs>
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42 <outputs>
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43 <data format="xml" name="param_out"/>
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44 </outputs>
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45 <help>predicts the likelihood of peptides to be proteotypic via svm_model which is trained by PTModel
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48 For more information, visit http://ftp.mi.fu-berlin.de/OpenMS/release-documentation/html/TOPP_PTPredict.html</help>
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49 </tool>