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comparison unifrac.weighted.xml @ 0:955f1163d44e draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/mothur commit a9d1e0debcd357d8080a1c6c5f1d206dd45a7a4d
author iuc
date Fri, 19 May 2017 05:52:48 -0400
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-1:000000000000 0:955f1163d44e
1 <tool profile="16.07" id="mothur_unifrac_weighted" name="unifrac.weighted" version="@WRAPPER_VERSION@.0">
2 <description>Describes whether two or more communities have the same structure</description>
3 <macros>
4 <import>macros.xml</import>
5 </macros>
6 <expand macro="requirements"/>
7 <expand macro="stdio"/>
8 <expand macro="version_command"/>
9 <command><![CDATA[
10 @SHELL_OPTIONS@
11
12 ## create symlinks to input datasets
13 ln -s "$tree" tree.dat &&
14 ln -s "$group" group.dat &&
15 ln -s "$name" name.dat &&
16 ln -s "$count" count.dat &&
17
18 echo 'unifrac.weighted(
19 tree=tree.dat,
20 #if $group:
21 group=group.dat,
22 #if $groups:
23 groups=${ str($groups).replace(",","-") },
24 #end if
25 #end if
26 #if $name:
27 name=name.dat,
28 #end if
29 iters=$iters,
30 #if $subsampling.use == "yes":
31 #if $subsampling.subsample:
32 subsample=$subsampling.subsample,
33 #else
34 subsample=T,
35 #end if
36 consensus=$subsampling.consensus,
37 #else
38 random=$random,
39 #end if
40 #if $distance:
41 distance=$distance,
42 #end if
43 #if $count:
44 count=count.dat,
45 #end if
46 root=$root,
47 processors='\${GALAXY_SLOTS:-1}'
48 )'
49 | sed 's/ //g' ## mothur trips over whitespace
50 | mothur
51 | tee mothur.out.log
52 ## rename some datasets
53 #if $subsampling.use == "yes":
54 && mv tree.*.ave.dist tree.ave
55 && mv tree.*.std.dist tree.std
56 #end if
57 ]]></command>
58 <inputs>
59 <param name="tree" type="data" format="mothur.tre" label="tree - Tree"/>
60 <param name="group" type="data" format="mothur.groups" label="group - Group file for the tree"/>
61 <param name="groups" type="select" optional="true" multiple="true" label="groups - Select groups for pairwise comparisons" help="You must select at least 2 groups">
62 <options>
63 <filter type="data_meta" ref="group" key="groups"/>
64 </options>
65 </param>
66 <param name="name" type="data" format="mothur.names" optional="true" label="name - Names file for the tree"/>
67 <param name="iters" type="integer" value="1000" min="0" label="iters - Number of iterations to try (default 1000)"/>
68 <param name="random" type="boolean" truevalue="true" falsevalue="false" checked="false" label="random - Compare your trees with randomly generated trees" help="unused if subsample=True"/>
69 <conditional name="subsampling">
70 <param name="use" type="select" label="use subsampling of groups?" help="(instead of randomly generated comparisons)">
71 <option value="no" selected="true">no</option>
72 <option value="yes">yes</option>
73 </param>
74 <when value="yes">
75 <param name="subsample" type="integer" value="" min="1" optional="true" label="subsample - (defaults to the size of the smallest group)" help="the size per group of the sample"/>
76 <param name="consensus" type="boolean" truevalue="true" falsevalue="false" checked="false" label="consensus - " help="The consensus parameter allows you to indicate you would like trees built from distance matrices created with the results of the subsampling, as well as a consensus tree built from these trees. Default=F"/>
77 </when>
78 <when value="no"/>
79 </conditional>
80 <param name="distance" type="select" label="distance - Create a distance matrix for your history">
81 <option value="lt" selected="true">Phylip Lower Triangle Matrix</option>
82 <option value="square">Phylip Square Matrix</option>
83 <option value="column">Pairwise Distance Matrix</option>
84 </param>
85 <param name="root" type="boolean" truevalue="true" falsevalue="false" checked="false" label="root - the entire root in your calculations"/>
86 <param name="count" type="data" format="mothur.count_table" optional="true" label="count - a count_table" help="generated by count.seqs"/>
87 </inputs>
88 <outputs>
89 <expand macro="logfile-output"/>
90 <data name="summary" format="tabular" from_work_dir="tree*wsummary" label="${tool.name} on ${on_string}: summary"/>
91 <data name="dist" format="mothur.lower.dist" from_work_dir="tree.dat*.dist " label="${tool.name} on ${on_string}: dist">
92 <change_format>
93 <when input="distance" value="square" format="mothur.square.dist"/>
94 <when input="distance" value="column" format="mothur.pair.dist"/>
95 </change_format>
96 </data>
97 <data name="weighted" format="tabular" from_work_dir="tree*.weighted" label="${tool.name} on ${on_string}: unweighted">
98 <filter>(random and subsampling['use'] == 'no')</filter>
99 </data>
100 <data name="ave_dist" format="mothur.lower.dist" from_work_dir="tree.ave" label="${tool.name} on ${on_string}: ave.dist">
101 <filter>subsampling['use'] == 'yes'</filter>
102 <change_format>
103 <when input="distance" value="square" format="mothur.square.dist"/>
104 <when input="distance" value="column" format="mothur.pair.dist"/>
105 </change_format>
106 </data>
107 <data name="std_dist" format="mothur.lower.dist" from_work_dir="tree.std" label="${tool.name} on ${on_string}: std.dist">
108 <filter>subsampling['use'] == 'yes'</filter>
109 <change_format>
110 <when input="distance" value="square" format="mothur.square.dist"/>
111 <when input="distance" value="column" format="mothur.pair.dist"/>
112 </change_format>
113 </data>
114 <data name="all_tree" format="mothur.tre" from_work_dir="tree*.all.tre" label="${tool.name} on ${on_string}: all.tre">
115 <filter>(subsampling['use'] == 'yes' and subsampling['consensus'])</filter>
116 </data>
117 <data name="cons_tree" format="mothur.tre" from_work_dir="tree*.cons.tre" label="${tool.name} on ${on_string}: cons.tre">
118 <filter>(subsampling['use'] == 'yes' and subsampling['consensus'])</filter>
119 </data>
120 </outputs>
121 <tests>
122 <test><!-- test with defaults -->
123 <param name="tree" value="abrecovery.tre" ftype="mothur.tre"/>
124 <param name="group" value="abrecovery.groups" ftype="mothur.groups"/>
125 <param name="groups" value="A,B,C"/>
126 <output name="summary" md5="f723493c1b909f5dbe5688d36be9fe73" ftype="tabular"/>
127 <output name="dist" md5="3660f333ac1326c8df213d1d3af1f865" ftype="mothur.lower.dist"/>
128 <expand macro="logfile-test"/>
129 </test>
130 <test><!-- test with random and no subsampling -->
131 <param name="tree" value="abrecovery.tre" ftype="mothur.tre"/>
132 <param name="group" value="abrecovery.groups" ftype="mothur.groups"/>
133 <param name="groups" value="A,B,C"/>
134 <param name="random" value="true"/>
135 <output name="summary" md5="a29236de2d96703e504e667b73cba790" ftype="tabular"/>
136 <output name="weighted" ftype="tabular">
137 <assert_contents>
138 <has_text text="A-BScore"/>
139 <has_text text="A-CRandFreq"/>
140 <has_text text="B-CRandCumul"/>
141 </assert_contents>
142 </output>
143 <output name="dist" md5="3660f333ac1326c8df213d1d3af1f865" ftype="mothur.lower.dist"/>
144 <expand macro="logfile-test"/>
145 </test>
146 <test><!-- test with group file and group select -->
147 <param name="tree" value="abrecovery.tre" ftype="mothur.tre"/>
148 <param name="group" value="abrecovery.groups" ftype="mothur.groups"/>
149 <param name="groups" value="A,B"/>
150 <param name="distance" value="square"/>
151 <output name="summary" md5="c253d4a3225804ad24b6c2e3a1095040" ftype="tabular"/>
152 <output name="dist" md5="7df2d4a2e69b9c1259d39fe59165734f" ftype="mothur.square.dist"/>
153 <expand macro="logfile-test"/>
154 </test>
155 <test><!-- test with subsample and consensus -->
156 <param name="tree" value="abrecovery.tre" ftype="mothur.tre"/>
157 <param name="group" value="abrecovery.groups" ftype="mothur.groups"/>
158 <param name="groups" value="A,B,C"/>
159 <param name="use" value="yes"/>
160 <param name="subsample" value="10"/>
161 <param name="distance" value="column"/>
162 <param name="consensus" value="true"/>
163 <output name="summary" md5="f723493c1b909f5dbe5688d36be9fe73" ftype="tabular"/>
164 <output name="dist" md5="f017b1d7c7de38f4b90a1d8e07c8605c" ftype="mothur.pair.dist"/>
165 <output name="ave_dist" ftype="mothur.pair.dist">
166 <assert_contents>
167 <has_text text="A"/>
168 <has_text text="B"/>
169 <has_text text="C"/>
170 </assert_contents>
171 </output>
172 <output name="std_dist" ftype="mothur.pair.dist">
173 <assert_contents>
174 <has_text text="A"/>
175 <has_text text="B"/>
176 <has_text text="C"/>
177 </assert_contents>
178 </output>
179 <output name="all_tree" ftype="mothur.tre">
180 <assert_contents>
181 <has_text text="A"/>
182 <has_text text="B"/>
183 <has_text text="C"/>
184 </assert_contents>
185 </output>
186 <output name="cons_tree" ftype="mothur.tre">
187 <assert_contents>
188 <has_text text="A"/>
189 <has_text text="B"/>
190 <has_text text="C"/>
191 </assert_contents>
192 </output>
193 <expand macro="logfile-test"/>
194 </test>
195 </tests>
196 <help>
197 <![CDATA[
198
199 @MOTHUR_OVERVIEW@
200
201 **Command Documentation**
202
203 The unifrac.weighted_ command implements the weighted UniFrac algorithm. The unifrac.unweighted command implements the unweighted 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.
204
205 .. _unifrac.weighted: https://www.mothur.org/wiki/Unifrac.weighted
206
207 ]]>
208 </help>
209 <expand macro="citations"/>
210 </tool>