Mercurial > repos > galaxyp > openms_rtpredict
view RTPredict.xml @ 7:3166bc58c62c draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/openms commit f608f41d45664d04d3124c6ebc791bf8a566b3c5
author | galaxyp |
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date | Fri, 17 May 2019 05:03:43 -0400 |
parents | 1eb2400e3f61 |
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<?xml version='1.0' encoding='UTF-8'?> <!--This is a configuration file for the integration of a tools into Galaxy (https://galaxyproject.org/). This file was automatically generated using CTDConverter.--> <!--Proposed Tool Section: [Peptide property prediction]--> <tool id="RTPredict" name="RTPredict" version="2.3.0"> <description>Predicts retention times for peptides using a model trained by RTModel.</description> <macros> <token name="@EXECUTABLE@">RTPredict</token> <import>macros.xml</import> </macros> <expand macro="references"/> <expand macro="stdio"/> <expand macro="requirements"/> <command detect_errors="aggressive"><![CDATA[RTPredict #if $param_in_id: -in_id $param_in_id #end if #if $param_in_text: -in_text $param_in_text #end if #if $param_in_oligo_params: -in_oligo_params $param_in_oligo_params #end if #if $param_in_oligo_trainset: -in_oligo_trainset $param_in_oligo_trainset #end if #if $param_svm_model: -svm_model $param_svm_model #end if #if $param_total_gradient_time: -total_gradient_time $param_total_gradient_time #end if #if $param_out_id_file: -out_id:file $param_out_id_file #end if #if $param_out_id_positive: -out_id:positive $param_out_id_positive #end if #if $param_out_id_negative: -out_id:negative $param_out_id_negative #end if #if $param_out_text_file: -out_text:file $param_out_text_file #end if #if $adv_opts.adv_opts_selector=='advanced': #if $adv_opts.param_max_number_of_peptides: -max_number_of_peptides $adv_opts.param_max_number_of_peptides #end if #if $adv_opts.param_force: -force #end if #if $adv_opts.param_out_id_rewrite_peptideidentification_rtmz: -out_id:rewrite_peptideidentification_rtmz #end if #end if ]]></command> <inputs> <param name="param_in_id" type="data" format="idxml" optional="True" label="Peptides with precursor information" help="(-in_id) "/> <param name="param_in_text" type="data" format="txt" optional="True" label="Peptides as text-based file" help="(-in_text) "/> <param name="param_in_oligo_params" type="data" format="paramXML" optional="True" label="input file with additional model parameters when using the OLIGO kernel" help="(-in_oligo_params) "/> <param name="param_in_oligo_trainset" type="data" format="txt" optional="True" label="input file with the used training dataset when using the OLIGO kernel" help="(-in_oligo_trainset) "/> <param name="param_svm_model" type="data" format="txt" optional="False" label="svm model in libsvm format (can be produced by RTModel)" help="(-svm_model) "/> <param name="param_total_gradient_time" type="float" min="1e-05" optional="True" value="1.0" label="The time (in seconds) of the gradient (peptide RT prediction)" help="(-total_gradient_time) "/> <expand macro="advanced_options"> <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) "/> <param name="param_force" display="radio" type="boolean" truevalue="-force" falsevalue="" checked="false" optional="True" label="Overwrite tool specific checks" help="(-force) "/> <param name="param_out_id_rewrite_peptideidentification_rtmz" display="radio" type="boolean" truevalue="-out_id:rewrite_peptideidentification_rtmz" falsevalue="" checked="false" optional="True" label="Rewrites each peptideidentification's rt and mz from prediction and calculation (according to the best hit)" help="(-rewrite_peptideidentification_rtmz) "/> </expand> </inputs> <outputs> <data name="param_out_id_file" format="idxml"/> <data name="param_out_id_positive" format="idxml"/> <data name="param_out_id_negative" format="idxml"/> <data name="param_out_text_file" format="tabular"/> </outputs> <help>Predicts retention times for peptides using a model trained by RTModel. For more information, visit https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/2.3.0/html/TOPP_RTPredict.html</help> </tool>