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1 <?xml version="1.0" ?>
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2 <tool id="qiime_vsearch_cluster-features-closed-reference" name="qiime vsearch cluster-features-closed-reference" version="2019.4">
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3 <description> - Closed-reference 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-closed-reference
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9 --i-sequences=$isequences
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10 --i-table=$itable
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11 --i-reference-sequences=$ireferencesequences
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12 --p-perc-identity="$ppercidentity"
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13
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14 #if str($pstrand) != 'None':
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15 --p-strand=$pstrand
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16 #end if
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17
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18 #set $pthreads = '${GALAXY_SLOTS:-4}'
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19
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20 #if str($pthreads):
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21 --p-threads="$pthreads"
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22 #end if
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23
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24 --o-clustered-table=oclusteredtable
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25 --o-clustered-sequences=oclusteredsequences
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26 --o-unmatched-sequences=ounmatchedsequences
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27 ;
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28 cp oclusteredtable.qza $oclusteredtable;
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29 cp oclusteredsequences.qza $oclusteredsequences;
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30 cp ounmatchedsequences.qza $ounmatchedsequences
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31 ]]></command>
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32 <inputs>
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33 <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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34 <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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35 <param format="qza,no_unzip.zip" label="--i-reference-sequences: ARTIFACT FeatureData[Sequence] The sequences to use as cluster centroids. [required]" name="ireferencesequences" optional="False" type="data"/>
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36 <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="float" value="" min="0" max="1" exclude_max="False" />
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37 <param label="--p-strand: " name="pstrand" optional="True" type="select">
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38 <option selected="True" value="None">Selection is Optional</option>
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39 <option value="plus">plus</option>
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40 <option value="both">both</option>
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41 </param>
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42 </inputs>
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43 <outputs>
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44 <data format="qza" label="${tool.name} on ${on_string}: clusteredtable.qza" name="oclusteredtable"/>
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45 <data format="qza" label="${tool.name} on ${on_string}: clusteredsequences.qza" name="oclusteredsequences"/>
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46 <data format="qza" label="${tool.name} on ${on_string}: unmatchedsequences.qza" name="ounmatchedsequences"/>
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47 </outputs>
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48 <help><![CDATA[
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49 Open-reference clustering of features.
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50 ######################################
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51
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52 Given a feature table and the associated feature sequences, cluster the
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53 features against a reference database based on user-specified percent
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54 identity threshold of their sequences. Any sequences that don't match are
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55 then clustered de novo. This is not a general-purpose clustering method,
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56 but rather is intended to be used for clustering the results of quality-
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57 filtering/dereplication methods, such as DADA2, or for re-clustering a
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58 FeatureTable at a lower percent identity than it was originally clustered
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59 at. When a group of features in the input table are clustered into a single
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60 feature, the frequency of that single feature in a given sample is the sum
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61 of the frequencies of the features that were clustered in that sample.
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62 Feature identifiers will be inherited from the centroid feature of each
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63 cluster. For features that match a reference sequence, the centroid feature
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64 is that reference sequence, so its identifier will become the feature
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65 identifier. The clustered_sequences result will contain feature
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66 representative sequences that are derived from the sequences input for all
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67 features in clustered_table. This will always be the most abundant sequence
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68 in the cluster. The new_reference_sequences result will contain the entire
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69 reference database, plus feature representative sequences for any de novo
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70 features. This is intended to be used as a reference database in subsequent
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71 iterations of cluster_features_open_reference, if applicable. See the
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72 vsearch documentation for details on how sequence clustering is performed.
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73
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74 Parameters
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75 ----------
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76 sequences : FeatureData[Sequence]
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77 The sequences corresponding to the features in table.
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78 table : FeatureTable[Frequency]
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79 The feature table to be clustered.
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80 reference_sequences : FeatureData[Sequence]
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81 The sequences to use as cluster centroids.
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82 perc_identity : Float % Range(0, 1, inclusive_start=False, inclusive_end=True)
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83 The percent identity at which clustering should be performed. This
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84 parameter maps to vsearch's --id parameter.
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85 strand : Str % Choices('plus', 'both'), optional
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86 Search plus (i.e., forward) or both (i.e., forward and reverse
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87 complement) strands.
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88
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89 Returns
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90 -------
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91 clustered_table : FeatureTable[Frequency]
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92 The table following clustering of features.
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93 clustered_sequences : FeatureData[Sequence]
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94 Sequences representing clustered features.
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95 new_reference_sequences : FeatureData[Sequence]
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96 The new reference sequences. This can be used for subsequent runs of
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97 open-reference clustering for consistent definitions of features across
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98 open-reference feature tables.
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99 ]]></help>
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100 <macros>
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101 <import>qiime_citation.xml</import>
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102 </macros>
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103 <expand macro="qiime_citation"/>
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104 </tool>
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