Mercurial > repos > iuc > hyphy_fubar

annotate hyphy_fubar.xml @ 24:c5ef306c2041 draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/hyphy/ commit 2742ee3b4e90f65352845265d2f85c4263e0eabb"
author iuc
date Tue, 20 Apr 2021 10:24:45 +0000
parents 3db56014322f
children cd41e7a4d35f
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1 <?xml version="1.0"?>
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2 <tool id="hyphy_fubar" name="HyPhy-FUBAR" version="@VERSION@+galaxy0" 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 <token name="@OPERATION@">FUBAR</token>
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7 <token name="@operation@">fubar</token>
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8 </macros>
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9 <expand macro="requirements"/>
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10 <command detect_errors="exit_code"><![CDATA[
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11 @SYMLINK_FILES@
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12 hyphy fubar
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13 --alignment ./$input_file
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14 #if $input_nhx:
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15 --tree ./input.nhx
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16 #end if
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17 --code '$gencodeid'
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18 --method '$posteriorEstimationMethod.method'
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19 --grid '$grid_points'
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20 @posteriorEstimationMethod_cmd@
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21 --concentration_parameter '$concentration'
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22 > '$fubar_log' ;
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23 @CATCH_ERROR@
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24 ]]></command>
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25 <inputs>
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26 <expand macro="inputs"/>
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27 <expand macro="gencode"/>
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28 <param argument="--grid" name="grid_points" type="integer" value="20" min="5" max="50" label="Grid points" />
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29 <expand macro="conditional_posteriorEstimationMethod" />
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30 <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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31
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32 </inputs>
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33 <outputs>
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34 <data name="fubar_log" format="txt"/>
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35 <data name="fubar_output" format="hyphy_results.json" />
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36 </outputs>
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37 <tests>
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38 <test>
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39 <param name="input_file" ftype="fasta.gz" value="fubar-in1.fa.gz"/>
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40 <param name="input_nhx" ftype="nhx" value="fubar-in1.nhx"/>
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41 <param name="posterior" value="Variational-Bayes"/>
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42 <output name="fubar_output" file="fubar-out1.json" compare="sim_size"/>
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43 </test>
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44 </tests>
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45 <help><![CDATA[
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47 FUBAR : Faste Unbiased Bayesian AppRoximation
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48 =============================================
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50 What question does this method answer?
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51 --------------------------------------
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53 Which site(s) in a gene are subject to pervasive, i.e. consistently across the entire phylogeny, diversifying selection?
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55 Recommended Applications
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56 ------------------------
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58 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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59 (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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60 represent candidate sites subject to strong selective pressures across the entire phylogeny.
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61
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62 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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64 Brief description
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65 -----------------
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66
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67 Perform a Fast Unbiased AppRoximate Bayesian (FUBAR) analysis of a
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68 coding sequence alignment to determine whether some sites have been
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69 subject to pervasive purifying or diversifying selection. There are three methods
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70 for estimating the posterior distribution of
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71 grid weights: collapsed Gibbs MCMC (faster), 0-th order Variation
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72 Bayes approximation (fastest), full Metropolis-Hastings (slowest).
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74 Input
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75 -----
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77 1. A *FASTA* sequence alignment.
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78 2. A phylogenetic tree in the *Newick* format
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80 Note: the names of sequences in the alignment must match the names of the sequences in the tree.
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83 Output
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84 ------
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85
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86 A JSON file with analysis results (http://hyphy.org/resources/json-fields.pdf).
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87
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88 A custom visualization module for viewing these results is available (see http://vision.hyphy.org/FUBAR for an example)
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89
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90 Further reading
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91 ---------------
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92
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93 http://hyphy.org/methods/selection-methods/#FUBAR
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94
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95
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96 Tool options
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97 ------------
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98 ::
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99
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100
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101 --code Which genetic code to use
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102
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103 --grid The number of grid points
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104 Smaller : faster
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105 Larger : more precise posterior estimation but slower
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106 default value: 20
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107
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108 --method Inference method to use
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109 Variational-Bayes : 0-th order Variational Bayes approximation; fastest [default]
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110 Metropolis-Hastings : Full Metropolis-Hastings MCMC algorithm; orignal method [slowest]
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111 Collapsed-Gibbs : Collapsed Gibbs sampler [intermediate speed]
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112
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113
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114 --chains How many MCMC chains to run (does not apply to Variational-Bayes)
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115 default value: 5
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116
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117 --chain-length MCMC chain length (does not apply to Variational-Bayes)
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118 default value: 2,000,000
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119
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120 --burn-in MCMC chain burn in (does not apply to Variational-Bayes)
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121 default value: 1,000,000
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122
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123 --samples MCMC samples to draw (does not apply to Variational-Bayes)
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124 default value: 1,000
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125
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126 --concentration_parameter
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127 The concentration parameter of the Dirichlet prior
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128 default value: 0.5
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129
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130
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131 ]]></help>
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132 <expand macro="citations">
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133 <citation type="doi">10.1093/molbev/mst030</citation>
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134 </expand>
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135 </tool>