Mercurial > repos > iuc > hyphy_fubar

annotate hyphy_fubar.xml @ 31:099fc3ec12d2 draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/hyphy/ commit 9ef881c2d3d5f240cbff33667b55f72cb82d9896"
author iuc
date Tue, 05 Oct 2021 05:08:01 +0000
parents cd41e7a4d35f
children 90e57f65ddd9
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1 <?xml version="1.0"?>
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2 <tool id="hyphy_fubar" name="HyPhy-FUBAR" version="@VERSION@+galaxy1" profile="19.09">
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3 <description>Fast Unconstrained Bayesian AppRoximation</description>
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4 <expand macro="bio_tools"/>
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5 <macros>
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6 <import>macros.xml</import>
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7 </macros>
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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 <param name="posterior" value="Variational-Bayes"/>
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38 <output name="fubar_output" file="fubar-out1.json" compare="sim_size"/>
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39 </test>
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40 </tests>
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41 <help><![CDATA[
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43 FUBAR : Faste Unbiased Bayesian AppRoximation
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44 =============================================
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46 What question does this method answer?
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47 --------------------------------------
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49 Which site(s) in a gene are subject to pervasive, i.e. consistently across the entire phylogeny, diversifying selection?
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51 Recommended Applications
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52 ------------------------
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54 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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55 (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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56 represent candidate sites subject to strong selective pressures across the entire phylogeny.
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58 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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60 Brief description
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61 -----------------
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62
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63 Perform a Fast Unbiased AppRoximate Bayesian (FUBAR) analysis of a
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64 coding sequence alignment to determine whether some sites have been
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65 subject to pervasive purifying or diversifying selection. There are three methods
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66 for estimating the posterior distribution of
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67 grid weights: collapsed Gibbs MCMC (faster), 0-th order Variation
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68 Bayes approximation (fastest), full Metropolis-Hastings (slowest).
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70 Input
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71 -----
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73 1. A *FASTA* sequence alignment.
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74 2. A phylogenetic tree in the *Newick* format
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76 Note: the names of sequences in the alignment must match the names of the sequences in the tree.
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79 Output
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80 ------
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82 A JSON file with analysis results (http://hyphy.org/resources/json-fields.pdf).
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83
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84 A custom visualization module for viewing these results is available (see http://vision.hyphy.org/FUBAR for an example)
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85
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86 Further reading
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87 ---------------
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88
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89 http://hyphy.org/methods/selection-methods/#FUBAR
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90
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91
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92 Tool options
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93 ------------
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94 ::
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95
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96
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97 --code Which genetic code to use
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98
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99 --grid The number of grid points
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100 Smaller : faster
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101 Larger : more precise posterior estimation but slower
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102 default value: 20
2
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103
6
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104 --method Inference method to use
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105 Variational-Bayes : 0-th order Variational Bayes approximation; fastest [default]
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106 Metropolis-Hastings : Full Metropolis-Hastings MCMC algorithm; orignal method [slowest]
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107 Collapsed-Gibbs : Collapsed Gibbs sampler [intermediate speed]
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108
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109
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110 --chains How many MCMC chains to run (does not apply to Variational-Bayes)
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111 default value: 5
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112
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113 --chain-length MCMC chain length (does not apply to Variational-Bayes)
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114 default value: 2,000,000
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115
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116 --burn-in MCMC chain burn in (does not apply to Variational-Bayes)
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117 default value: 1,000,000
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118
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119 --samples MCMC samples to draw (does not apply to Variational-Bayes)
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120 default value: 1,000
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121
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122 --concentration_parameter
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123 The concentration parameter of the Dirichlet prior
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124 default value: 0.5
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125
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126
1
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127 ]]></help>
0
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128 <expand macro="citations">
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129 <citation type="doi">10.1093/molbev/mst030</citation>
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130 </expand>
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131 </tool>