view mothur/tools/mothur/unifrac.unweighted.xml @ 1:fcc0778f6987

Migrated tool version 1.16.0 from old tool shed archive to new tool shed repository

<tool id="mothur_unifrac_unweighted" name="unifrac.unweighted" version="1.16.0">
 <description>Describes whether two or more communities have the same structure</description>
 <command interpreter="python">
  mothur_wrapper.py 
  --cmd='unifrac.unweighted'
  --result='^mothur.\S+\.logfile$:'$logfile,'^\S+\.uwsummary$:'$summary,'^\S+\.unweighted\.(column\.|philip\.)?dist$:'$dist
  --outputdir='$logfile.extra_files_path'
  --READ_cmd='read.tree'
  --READ_tree=$tree
  #if $group.__str__ != "None" and len($group.__str__) > 0:
   --READ_group='$group'
  #end if
  #if $groups.__str__ != "None" and len($groups.__str__) > 0:
   --groups='$groups'
  #end if
  #if $name.__str__ != "None" and len($name.__str__) > 0:
   --READ_name='$name'
  #end if
  #if int($iters.__str__) > 0:
   --iters=$iters
  #end if
  $random
  #if $distance.__str__ != "false":
   --distance=$distance
  #end if
  --processors=2
 </command>
 <inputs>
  <!-- list,group  or shared -->
  <param name="tree" type="data" format="tre" label="read.tree(tree=) - Tree"/>
  <param name="group" type="data" format="groups" label="read.tree(group=) - Group file for the tree"/>
  <param name="groups" type="select" label="groups - Groups to display" multiple="true">
   <options from_dataset="group">
    <column name="name" index="1"/>
    <column name="value" index="1"/>
    <filter type="unique_value" name="unq_grp" column="1" />
   </options>
  </param>
  <param name="name" type="data" format="names" optional="true" label="name - Names file for the tree"/>
  <param name="iters" type="integer" value="1000" label="iters - Number of iterations to try (default 1000)"/>
  <param name="random" type="boolean" truevalue="--random=true" falsevalue="" checked="false" label="random - Compare your trees with randomly generated trees" />
  <param name="distance" type="select" label="distance - Select a distance matrix for your history">
   <option value="false">None</option>
   <option value="lt">Philip Lower Triangle Matrix</option>
   <option value="square">Philip Square Matrix</option>
   <option value="column">Pairwise Distance Matrix</option>
  </param>
  <!-- random uses input prompts, not sure how to model that
  <param name="random" type="text" value="" label="random - The root name for random tests"/>
  -->
 </inputs>
 <outputs>
  <data format="html" name="logfile" label="${tool.name} on ${on_string}: logfile" />
  <data format="tabular" name="summary" label="${tool.name} on ${on_string}: summary">
  </data>
  <data format="lower.dist" name="dist" label="${tool.name} on ${on_string}: dist">
   <filter>distance != 'false'</filter>
   <change_format>
    <when input="distance" value="square" format="square.dist" />
    <when input="distance" value="column" format="pair.dist" />
   </change_format>
  </data>
  <!-- random uses input prompts, not sure how to model that
  <data format="tabular" name="random" label="${tool.name} on ${on_string}: random">
  </data>
  -->
 </outputs>
 <requirements>
  <requirement type="binary">mothur</requirement>
 </requirements>
 <tests>
 </tests>
 <help>
**Mothur Overview**

Mothur_, initiated by Dr. Patrick Schloss and his software development team
in the Department of Microbiology and Immunology at The University of Michigan,
provides bioinformatics for the microbial ecology community.

.. _Mothur: http://www.mothur.org/wiki/Main_Page

**Command Documenation**

The unifrac.unweighted_ command the unweighted UniFrac algorithm. The unifrac.weighted command implements the weighted version of the command. Both of these methods are available through the UniFrac website. The UniFrac methods are generic tests that describes whether two or more communities have the same structure. The significance of the test statistic can only indicate the probability that the communities have the same structure by chance. The value does not indicate a level of similarity.

.. _unifrac.unweighted: http://www.mothur.org/wiki/Unifrac.unweighted


 </help>
</tool>