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1 <?xml version="1.0" ?>
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2 <tool id="qiime_vsearch_cluster-features-de-novo" name="qiime vsearch cluster-features-de-novo" version="2019.4">
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3 <description> - De novo clustering of features.</description>
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4 <requirements>
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5 <requirement type="package" version="2019.4">qiime2</requirement>
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6 </requirements>
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7 <command><![CDATA[
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8 qiime vsearch cluster-features-de-novo
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9
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10 --i-sequences=$isequences
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11 --i-table=$itable
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12 --p-perc-identity="$ppercidentity"
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13
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14 #set $pthreads = '${GALAXY_SLOTS:-4}'
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15 #if str($pthreads):
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16 --p-threads="$pthreads"
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17 #end if
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18
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19 --o-clustered-table=oclusteredtable
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20 --o-clustered-sequences=oclusteredsequences
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21 ;
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22 cp oclusteredtable.qza $oclusteredtable;
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23 cp oclusteredsequences.qza $oclusteredsequences
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24 ]]></command>
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25 <inputs>
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26 <param format="qza,no_unzip.zip" label="--i-sequences: ARTIFACT FeatureData[Sequence] The sequences corresponding to the features in table. [required]" name="isequences" optional="False" type="data"/>
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27 <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] The feature table to be clustered. [required]" name="itable" optional="False" type="data"/>
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28
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29 <param label="--p-perc-identity: PROPORTION Range(0, 1, inclusive_start=False, inclusive_end=True) The percent identity at which clustering should be performed. This parameter maps to vsearch's --id parameter. [required]" name="ppercidentity" optional="False" min="0" max="1" value="" exclude_min="True" exclude_max="False" type="float"/>
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30 </inputs>
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31 <outputs>
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32 <data format="qza" label="${tool.name} on ${on_string}: clusteredtable.qza" name="oclusteredtable"/>
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33 <data format="qza" label="${tool.name} on ${on_string}: clusteredsequences.qza" name="oclusteredsequences"/>
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34 </outputs>
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35 <help><![CDATA[
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36 De novo clustering of features.
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37 ###############################
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38
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39 Given a feature table and the associated feature sequences, cluster the
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40 features based on user-specified percent identity threshold of their
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41 sequences. This is not a general-purpose de novo clustering method, but
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42 rather is intended to be used for clustering the results of quality-
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43 filtering/dereplication methods, such as DADA2, or for re-clustering a
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44 FeatureTable at a lower percent identity than it was originally clustered
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45 at. When a group of features in the input table are clustered into a single
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46 feature, the frequency of that single feature in a given sample is the sum
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47 of the frequencies of the features that were clustered in that sample.
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48 Feature identifiers and sequences will be inherited from the centroid
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49 feature of each cluster. See the vsearch documentation for details on how
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50 sequence clustering is performed.
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51
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52 Parameters
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53 ----------
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54 sequences : FeatureData[Sequence]
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55 The sequences corresponding to the features in table.
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56 table : FeatureTable[Frequency]
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57 The feature table to be clustered.
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58 perc_identity : Float % Range(0, 1, inclusive_start=False, inclusive_end=True)
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59 The percent identity at which clustering should be performed. This
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60 parameter maps to vsearch's --id parameter.
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61 Returns
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62 -------
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63 clustered_table : FeatureTable[Frequency]
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64 The table following clustering of features.
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65 clustered_sequences : FeatureData[Sequence]
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66 Sequences representing clustered features.
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67 ]]></help>
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68 <macros>
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69 <import>qiime_citation.xml</import>
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70 </macros>
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71 <expand macro="qiime_citation"/>
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72 </tool>
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