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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"
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3 version="2020.8">
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4 <description>De novo clustering of features.</description>
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5 <requirements>
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6 <requirement type="package" version="2020.8">qiime2</requirement>
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7 </requirements>
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8 <command><![CDATA[
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9 qiime vsearch cluster-features-de-novo
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10
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11 --i-sequences=$isequences
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12
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13 --i-table=$itable
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14
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15 --p-perc-identity=$ppercidentity
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16
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17 --p-threads=$pthreads
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18
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19 --o-clustered-table=oclusteredtable
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20
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21 --o-clustered-sequences=oclusteredsequences
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22
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23 #if str($examples) != 'None':
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24 --examples=$examples
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25 #end if
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26
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27 ;
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28 cp oclusteredsequences.qza $oclusteredsequences
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29
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30 ]]></command>
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31 <inputs>
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32 <param format="qza,no_unzip.zip" label="--i-sequences: ARTIFACT FeatureData[Sequence] The sequences corresponding to the features in table. [required]" name="isequences" optional="False" type="data" />
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33 <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] The feature table to be clustered. [required]" name="itable" optional="False" type="data" />
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34 <param label="--p-perc-identity: PROPORTION Range(0, 1, inclusive_start=False, inclusive_end=True) The percent identity at which clustering should be performed. This parameter maps to vsearch\'s --id parameter. [required]" name="ppercidentity" optional="False" type="text" />
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35 <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
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36
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37 </inputs>
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38
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39 <outputs>
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40 <data format="qza" label="${tool.name} on ${on_string}: clusteredtable.qza" name="oclusteredtable" />
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41 <data format="qza" label="${tool.name} on ${on_string}: clusteredsequences.qza" name="oclusteredsequences" />
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42
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43 </outputs>
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44
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45 <help><![CDATA[
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46 De novo clustering of features.
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47 ###############################################################
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48
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49 Given a feature table and the associated feature sequences, cluster the
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50 features based on user-specified percent identity threshold of their
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51 sequences. This is not a general-purpose de novo clustering method, but
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52 rather is intended to be used for clustering the results of quality-
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53 filtering/dereplication methods, such as DADA2, or for re-clustering a
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54 FeatureTable at a lower percent identity than it was originally clustered
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55 at. When a group of features in the input table are clustered into a single
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56 feature, the frequency of that single feature in a given sample is the sum
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57 of the frequencies of the features that were clustered in that sample.
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58 Feature identifiers and sequences will be inherited from the centroid
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59 feature of each cluster. See the vsearch documentation for details on how
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60 sequence clustering is performed.
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61
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62 Parameters
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63 ----------
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64 sequences : FeatureData[Sequence]
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65 The sequences corresponding to the features in table.
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66 table : FeatureTable[Frequency]
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67 The feature table to be clustered.
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68 perc_identity : Float % Range(0, 1, inclusive_start=False, inclusive_end=True)
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69 The percent identity at which clustering should be performed. This
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70 parameter maps to vsearch's --id parameter.
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71 threads : Int % Range(0, 256, inclusive_end=True), optional
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72 The number of threads to use for computation. Passing 0 will launch one
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73 thread per CPU core.
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74
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75 Returns
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76 -------
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77 clustered_table : FeatureTable[Frequency]
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78 The table following clustering of features.
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79 clustered_sequences : FeatureData[Sequence]
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80 Sequences representing clustered features.
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81 ]]></help>
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82 <macros>
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83 <import>qiime_citation.xml</import>
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84 </macros>
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85 <expand macro="qiime_citation"/>
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86 </tool> |
