annotate hyphy_fubar.xml @ 34:6e72195b364a draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/hyphy/ commit 821e0f3e4546bc55e093ab63bd9f9b57f2bf772d
author iuc
date Thu, 18 Aug 2022 14:00:13 +0000
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1 <?xml version="1.0"?>
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2 <tool id="hyphy_fubar" name="HyPhy-FUBAR" version="@TOOL_VERSION@+galaxy1" profile="19.09">
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3 <description>Fast Unconstrained Bayesian AppRoximation</description>
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4 <macros>
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5 <import>macros.xml</import>
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6 </macros>
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7 <expand macro="bio_tools"/>
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8 <expand macro="requirements"/>
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9 <command detect_errors="exit_code"><![CDATA[
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10 @SYMLINK_FILES@
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11 ln -s '$fubar_output' ${input_file}.FUBAR.json &&
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12 hyphy fubar
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13 --alignment ./$input_file
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14 @INPUT_TREE@
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15 --code '$gencodeid'
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16 --method '$posteriorEstimationMethod.method'
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17 --grid '$grid_points'
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18 @posteriorEstimationMethod_cmd@
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19 --concentration_parameter '$concentration'
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20 @ERRORS@
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21 ]]></command>
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22 <inputs>
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23 <expand macro="inputs"/>
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24 <expand macro="gencode"/>
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25 <param argument="--grid" name="grid_points" type="integer" value="20" min="5" max="50" label="Grid points" />
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26 <expand macro="conditional_posteriorEstimationMethod" />
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27 <param argument="--concentration_parameter" name="concentration" type="float" value="0.5" min="0.001" max="1" label="Concentration parameter of the Dirichlet prior" />
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28
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29 </inputs>
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30 <outputs>
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31 <data name="fubar_output" format="hyphy_results.json" />
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32 </outputs>
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33 <tests>
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34 <test>
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35 <param name="input_file" ftype="fasta.gz" value="fubar-in1.fa.gz"/>
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36 <param name="input_nhx" ftype="nhx" value="fubar-in1.nhx"/>
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37 <conditional name="posteriorEstimationMethod">
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38 <param name="method" value="Variational-Bayes"/>
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39 </conditional>
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40 <output name="fubar_output" file="fubar-out1.json" compare="sim_size"/>
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41 </test>
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42 </tests>
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43 <help><![CDATA[
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45 FUBAR : Faste Unbiased Bayesian AppRoximation
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46 =============================================
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48 What question does this method answer?
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49 --------------------------------------
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51 Which site(s) in a gene are subject to pervasive, i.e. consistently across the entire phylogeny, diversifying selection?
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53 Recommended Applications
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54 ------------------------
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56 The phenomenon of pervasive selection is generally most prevalent in pathogen evolution and any biological system influenced by evolutionary arms race dynamics
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57 (or balancing selection), including adaptive immune escape by viruses. As such, FUBAR is ideally suited to identify sites under positive selection which
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58 represent candidate sites subject to strong selective pressures across the entire phylogeny.
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60 FUBAR is our recommended method for detecting pervasive selection at individual sites on large (> 500 sequences) datasets for which other methods have prohibitive runtimes, unless you have access to a computer cluster.
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62 Brief description
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63 -----------------
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64
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65 Perform a Fast Unbiased AppRoximate Bayesian (FUBAR) analysis of a
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66 coding sequence alignment to determine whether some sites have been
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67 subject to pervasive purifying or diversifying selection. There are three methods
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68 for estimating the posterior distribution of
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69 grid weights: collapsed Gibbs MCMC (faster), 0-th order Variation
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70 Bayes approximation (fastest), full Metropolis-Hastings (slowest).
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72 Input
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73 -----
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75 1. A *FASTA* sequence alignment.
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76 2. A phylogenetic tree in the *Newick* format
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78 Note: the names of sequences in the alignment must match the names of the sequences in the tree.
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81 Output
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82 ------
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83
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84 A JSON file with analysis results (http://hyphy.org/resources/json-fields.pdf).
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85
6
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86 A custom visualization module for viewing these results is available (see http://vision.hyphy.org/FUBAR for an example)
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87
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88 Further reading
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89 ---------------
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90
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91 http://hyphy.org/methods/selection-methods/#FUBAR
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92
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93
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94 Tool options
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95 ------------
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96 ::
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97
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98
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99 --code Which genetic code to use
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100
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101 --grid The number of grid points
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102 Smaller : faster
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103 Larger : more precise posterior estimation but slower
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104 default value: 20
2
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105
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106 --method Inference method to use
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107 Variational-Bayes : 0-th order Variational Bayes approximation; fastest [default]
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108 Metropolis-Hastings : Full Metropolis-Hastings MCMC algorithm; orignal method [slowest]
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109 Collapsed-Gibbs : Collapsed Gibbs sampler [intermediate speed]
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110
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111
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112 --chains How many MCMC chains to run (does not apply to Variational-Bayes)
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113 default value: 5
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114
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115 --chain-length MCMC chain length (does not apply to Variational-Bayes)
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116 default value: 2,000,000
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117
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118 --burn-in MCMC chain burn in (does not apply to Variational-Bayes)
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119 default value: 1,000,000
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120
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121 --samples MCMC samples to draw (does not apply to Variational-Bayes)
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122 default value: 1,000
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123
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124 --concentration_parameter
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125 The concentration parameter of the Dirichlet prior
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126 default value: 0.5
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127
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128
1
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129 ]]></help>
0
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130 <expand macro="citations">
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131 <citation type="doi">10.1093/molbev/mst030</citation>
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132 </expand>
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133 </tool>