annotate upgma_cluster.xml @ 0:e7324235f49e draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/qiime/ commit c9bf747b23b4a9d6adc20c7740b9247c22654862

<tool id="qiime_upgma_cluster" name="Build UPGMA tree" version="@WRAPPER_VERSION@.0">
    <description>comparing samples</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements"/>
    <version_command>upgma_cluster.py -v</version_command>
    <command detect_errors="aggressive"><![CDATA[
        mkdir input
        &&
        #for $i, $matrix in enumerate($input_path)
            cp '$matrix' 'input/dataset_$i' &&
        #end for
        upgma_cluster.py
            --input_path input
            --output_path output
    ]]></command>
    <inputs>
        <param argument="--input_path" type="data" format="txt" label="Distance matrix" multiple="true"/>
    </inputs>
    <outputs>
        <collection type="list" name="output_trees" label="${tool.name} on ${on_string}: UPGMA trees">
            <discover_datasets pattern="(?P&lt;designation&gt;.*)\.tre" directory="output"/>
        </collection>
    </outputs>
    <tests>
        <test>
            <param name="input_path" value="upgma_cluster/beta_div_1.txt,upgma_cluster/beta_div_2.txt,upgma_cluster/beta_div_3.txt,upgma_cluster/beta_div_4.txt"/>
            <output_collection name="output_trees" type="list" count="4">
                <element name="upgma_dataset_0">
                    <assert_contents>
                        <has_text text="PC.636" />
                    </assert_contents>
                </element>
                <element name="upgma_dataset_3">
                    <assert_contents>
                        <has_text text="PC.355" />
                    </assert_contents>
                </element>
            </output_collection>
        </test>
    </tests>
    <help><![CDATA[
**What it does**

In addition to using PCoA, it can be useful to cluster samples using UPGMA (Unweighted Pair Group Method with Arithmetic mean, also known as average linkage). As with PCoA, the input to this step is a distance matrix (i.e. resulting file from beta_diversity.py).
The output is a newick formatted tree compatible with most standard tree viewing programs. Batch processing is also available, allowing the analysis of an entire directory of distance matrices.
    ]]></help>
    <citations>
        <expand macro="citations"/>
    </citations>
</tool>