<?xml version="1.0" ?>
<tool id="qiime_feature-classifier_classify-hybrid-vsearch-sklearn" name="qiime feature-classifier classify-hybrid-vsearch-sklearn" version="2019.7">
	<description> -  ALPHA Hybrid classifier: VSEARCH exact match + sklearn classifier</description>
	<requirements>
		<requirement type="package" version="2019.7">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

#if str($ppercidentity):
 --p-perc-identity=$ppercidentity
#end if

#if str($pquerycov):
 --p-query-cov=$pquerycov
#end if

#if str($pstrand) != 'None':
 --p-strand=$pstrand
#end if

#if str($pminconsensus):
 --p-min-consensus=$pminconsensus
#end if

#if str($pconfidence) != 'None':
 --p-confidence=$pconfidence
#end if

#if str($preadorientation) != 'None':
 --p-read-orientation=$preadorientation
#end if

#set $pthreads = '${GALAXY_SLOTS:-4}'

#if str($pthreads):

#if str($pthreads):
 --p-threads="$pthreads"
#end if

#end if


#if $pprefilter:
 --p-prefilter
#end if

#if str($psamplesize):
 --p-sample-size=$psamplesize
#end if

#if str($prandseed):
 --p-randseed=$prandseed
#end if


--o-classification=oclassification

;
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 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]" name="ppercidentity" optional="True" type="float" value="0.5"/>
		<param 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]" 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 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]" name="pminconsensus" optional="True" type="float" value="0.51"/>
		<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-prefilter: --p-no-prefilter Toggle positive filter of query sequences on or off. [default: True]" name="pprefilter" 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]" 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]" name="prandseed" optional="True" type="integer" value="0"/>
	</inputs>
	<outputs>
		<data format="qza" label="${tool.name} on ${on_string}: classification.qza" name="oclassification"/>
	</outputs>
	<help><![CDATA[

	]]></help>
<macros>
    <import>qiime_citation.xml</import>
</macros>
<expand macro="qiime_citation"/>
</tool>