--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/qiime2-2020.8/qiime_feature-classifier_classify-hybrid-vsearch-sklearn.xml	Thu Sep 03 09:33:04 2020 +0000
@@ -0,0 +1,214 @@
+<?xml version="1.0" ?>
+<tool id="qiime_feature-classifier_classify-hybrid-vsearch-sklearn" name="qiime feature-classifier classify-hybrid-vsearch-sklearn"
+      version="2020.8">
+  <description> ALPHA Hybrid classifier: VSEARCH exact match + sklearn classifier</description>
+  <requirements>
+    <requirement type="package" version="2020.8">qiime2</requirement>
+  </requirements>
+  <command><![CDATA[
+qiime feature-classifier classify-hybrid-vsearch-sklearn
+
+--i-query=$iquery
+
+--i-reference-reads=$ireferencereads
+
+--i-reference-taxonomy=$ireferencetaxonomy
+
+--i-classifier=$iclassifier
+
+#if str($pmaxaccepts) != 'None':
+--p-maxaccepts=$pmaxaccepts
+#end if
+
+--p-perc-identity=$ppercidentity
+
+--p-query-cov=$pquerycov
+
+#if str($pstrand) != 'None':
+--p-strand=$pstrand
+#end if
+
+--p-min-consensus=$pminconsensus
+
+#if str($pmaxhits) != 'None':
+--p-maxhits=$pmaxhits
+#end if
+
+#if str($pmaxrejects) != 'None':
+--p-maxrejects=$pmaxrejects
+#end if
+
+#if str($pconfidence) != 'None':
+--p-confidence=$pconfidence
+#end if
+
+#if str($preadorientation) != 'None':
+--p-read-orientation=$preadorientation
+#end if
+
+--p-threads=$pthreads
+
+#if $pnoprefilter:
+ --p-no-prefilter
+#end if
+
+--p-sample-size=$psamplesize
+
+--p-randseed=$prandseed
+
+--o-classification=oclassification
+
+#if str($examples) != 'None':
+--examples=$examples
+#end if
+
+;
+cp oclassification.qza $oclassification
+
+  ]]></command>
+  <inputs>
+    <param format="qza,no_unzip.zip" label="--i-query: ARTIFACT FeatureData[Sequence] Sequences to classify taxonomically.        [required]" name="iquery" optional="False" type="data" />
+    <param format="qza,no_unzip.zip" label="--i-reference-reads: ARTIFACT FeatureData[Sequence] reference sequences.                        [required]" name="ireferencereads" optional="False" type="data" />
+    <param format="qza,no_unzip.zip" label="--i-reference-taxonomy: ARTIFACT FeatureData[Taxonomy] reference taxonomy labels.                  [required]" name="ireferencetaxonomy" optional="False" type="data" />
+    <param format="qza,no_unzip.zip" label="--i-classifier: ARTIFACT TaxonomicClassifier Pre-trained sklearn taxonomic classifier for classifying the reads.                      [required]" name="iclassifier" optional="False" type="data" />
+    <param label="--p-maxaccepts: " name="pmaxaccepts" optional="True" type="select">
+      <option selected="True" value="None">Selection is Optional</option>
+      <option value="Int % Range(1">Int % Range(1</option>
+      <option value="None">None</option>
+    </param>
+    <param exclude_max="False" label="--p-perc-identity: PROPORTION Range(0.0, 1.0, inclusive_end=True) Percent sequence similarity to use for PREFILTER. Reject match if percent identity to query is lower. Set to a lower value to perform a rough pre-filter. This parameter is ignored if `prefilter` is disabled. [default: 0.5]" max="1.0" min="0.0" name="ppercidentity" optional="True" type="float" value="0.5" />
+    <param exclude_max="False" label="--p-query-cov: PROPORTION Range(0.0, 1.0, inclusive_end=True) Query coverage threshold to use for PREFILTER. Reject match if query alignment coverage per high-scoring pair is lower. Set to a lower value to perform a rough pre-filter. This parameter is ignored if `prefilter` is disabled.                            [default: 0.8]" max="1.0" min="0.0" name="pquerycov" optional="True" type="float" value="0.8" />
+    <param label="--p-strand: " name="pstrand" optional="True" type="select">
+      <option selected="True" value="None">Selection is Optional</option>
+      <option value="both">both</option>
+      <option value="plus">plus</option>
+    </param>
+    <param exclude_max="False" exclude_min="True" label="--p-min-consensus: NUMBER Range(0.5, 1.0, inclusive_start=False, inclusive_end=True) Minimum fraction of assignments must match top hit to be accepted as consensus assignment.   [default: 0.51]" max="1.0" min="0.5" name="pminconsensus" optional="True" type="float" value="0.51" />
+    <param label="--p-maxhits: " name="pmaxhits" optional="True" type="select">
+      <option selected="True" value="None">Selection is Optional</option>
+      <option value="Int % Range(1">Int % Range(1</option>
+      <option value="None">None</option>
+    </param>
+    <param label="--p-maxrejects: " name="pmaxrejects" optional="True" type="select">
+      <option selected="True" value="None">Selection is Optional</option>
+      <option value="Int % Range(1">Int % Range(1</option>
+      <option value="None">None</option>
+    </param>
+    <param label="--p-confidence: " name="pconfidence" optional="True" type="select">
+      <option selected="True" value="None">Selection is Optional</option>
+      <option value="Float % Range(0">Float % Range(0</option>
+      <option value="1">1</option>
+      <option value="inclusive_end=True">inclusive_end=True</option>
+    </param>
+    <param label="--p-read-orientation: " name="preadorientation" optional="True" type="select">
+      <option selected="True" value="None">Selection is Optional</option>
+      <option value="same">same</option>
+      <option value="reverse-complement">reverse-complement</option>
+      <option value="auto">auto</option>
+    </param>
+    <param label="--p-no-prefilter: Do not toggle positive filter of query sequences on or off. [default: True]" name="pnoprefilter" selected="False" type="boolean" />
+    <param label="--p-sample-size: INTEGER Range(1, None)      Randomly extract the given number of sequences from the reference database to use for prefiltering. This parameter is ignored if `prefilter` is disabled. [default: 1000]" min="1" name="psamplesize" optional="True" type="integer" value="1000" />
+    <param label="--p-randseed: INTEGER  Use integer as a seed for the pseudo-random generator Range(0, None)      used during prefiltering. A given seed always produces the same output, which is useful for replicability. Set to 0 to use a pseudo-random seed. This parameter is ignored if `prefilter` is disabled.    [default: 0]" min="0" name="prandseed" optional="True" type="integer" value="0" />
+    <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
+    
+  </inputs>
+
+  <outputs>
+    <data format="qza" label="${tool.name} on ${on_string}: classification.qza" name="oclassification" />
+    
+  </outputs>
+
+  <help><![CDATA[
+ ALPHA Hybrid classifier: VSEARCH exact match + sklearn classifier
+###############################################################
+
+NOTE: THIS PIPELINE IS AN ALPHA RELEASE. Please report bugs to
+https://forum.qiime2.org! Assign taxonomy to query sequences using hybrid
+classifier. First performs rough positive filter to remove artifact and
+low-coverage sequences (use "prefilter" parameter to toggle this step on or
+off). Second, performs VSEARCH exact match between query and
+reference_reads to find exact matches, followed by least common ancestor
+consensus taxonomy assignment from among maxaccepts top hits, min_consensus
+of which share that taxonomic assignment. Query sequences without an exact
+match are then classified with a pre-trained sklearn taxonomy classifier to
+predict the most likely taxonomic lineage.
+
+Parameters
+----------
+query : FeatureData[Sequence]
+    Sequences to classify taxonomically.
+reference_reads : FeatureData[Sequence]
+    reference sequences.
+reference_taxonomy : FeatureData[Taxonomy]
+    reference taxonomy labels.
+classifier : TaxonomicClassifier
+    Pre-trained sklearn taxonomic classifier for classifying the reads.
+maxaccepts : Int % Range(1, None) | Str % Choices('all'), optional
+    Maximum number of hits to keep for each query. Set to "all" to keep all
+    hits > perc_identity similarity. Note that if strand=both, maxaccepts
+    will keep N hits for each direction (if searches in the opposite
+    direction yield results that exceed the minimum perc_identity). In
+    those cases use maxhits to control the total number of hits returned.
+    This option works in pair with maxrejects. The search process sorts
+    target sequences by decreasing number of k-mers they have in common
+    with the query sequence, using that information as a proxy for sequence
+    similarity. After pairwise alignments, if the first target sequence
+    passes the acceptation criteria, it is accepted as best hit and the
+    search process stops for that query. If maxaccepts is set to a higher
+    value, more hits are accepted. If maxaccepts and maxrejects are both
+    set to "all", the complete database is searched.
+perc_identity : Float % Range(0.0, 1.0, inclusive_end=True), optional
+    Percent sequence similarity to use for PREFILTER. Reject match if
+    percent identity to query is lower. Set to a lower value to perform a
+    rough pre-filter. This parameter is ignored if `prefilter` is disabled.
+query_cov : Float % Range(0.0, 1.0, inclusive_end=True), optional
+    Query coverage threshold to use for PREFILTER. Reject match if query
+    alignment coverage per high-scoring pair is lower. Set to a lower value
+    to perform a rough pre-filter. This parameter is ignored if `prefilter`
+    is disabled.
+strand : Str % Choices('both', 'plus'), optional
+    Align against reference sequences in forward ("plus") or both
+    directions ("both").
+min_consensus : Float % Range(0.5, 1.0, inclusive_start=False, inclusive_end=True), optional
+    Minimum fraction of assignments must match top hit to be accepted as
+    consensus assignment.
+maxhits : Int % Range(1, None) | Str % Choices('all'), optional
+maxrejects : Int % Range(1, None) | Str % Choices('all'), optional
+reads_per_batch : Int % Range(0, None), optional
+    Number of reads to process in each batch for sklearn classification. If
+    "auto", this parameter is autoscaled to min(number of query sequences /
+    threads, 20000).
+confidence : Float % Range(0, 1, inclusive_end=True) | Str % Choices('disable'), optional
+    Confidence threshold for limiting taxonomic depth. Set to "disable" to
+    disable confidence calculation, or 0 to calculate confidence but not
+    apply it to limit the taxonomic depth of the assignments.
+read_orientation : Str % Choices('same', 'reverse-complement', 'auto'), optional
+    Direction of reads with respect to reference sequences in pre-trained
+    sklearn classifier. same will cause reads to be classified unchanged;
+    reverse-complement will cause reads to be reversed and complemented
+    prior to classification. "auto" will autodetect orientation based on
+    the confidence estimates for the first 100 reads.
+threads : Int % Range(1, None), optional
+    Number of threads to use for job parallelization.
+prefilter : Bool, optional
+    Toggle positive filter of query sequences on or off.
+sample_size : Int % Range(1, None), optional
+    Randomly extract the given number of sequences from the reference
+    database to use for prefiltering. This parameter is ignored if
+    `prefilter` is disabled.
+randseed : Int % Range(0, None), optional
+    Use integer as a seed for the pseudo-random generator used during
+    prefiltering. A given seed always produces the same output, which is
+    useful for replicability. Set to 0 to use a pseudo-random seed. This
+    parameter is ignored if `prefilter` is disabled.
+
+Returns
+-------
+classification : FeatureData[Taxonomy]
+    The resulting taxonomy classifications.
+  ]]></help>
+  <macros>
+    <import>qiime_citation.xml</import>
+  </macros>
+  <expand macro="qiime_citation"/>
+</tool>
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