<?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




#if str( $id_to_taxonomy_fp.selector ) == 'history'
#set $tax = $id_to_taxonomy_fp.taxonomy_fp
--i-reference-taxonomy '$tax'
#else:
#set $tax = $id_to_taxonomy_fp.taxonomy_fp.fields.path
--i-reference-taxonomy '$tax'
#end if





#if str( $id_to_classifier_fp.selector ) == 'history'
#set $classifier = $id_to_classifier_fp.classifier_fp
--i-classifier '$classifier'
#else:
#set $classifier = $id_to_classifier_fp.classifier_fp.fields.path
--i-classifier '$classifier'
#end if





#if str($pmaxaccepts):
 --p-maxaccepts=$pmaxaccepts
#end if

#if str($pconfidence):
 --p-confidence=$pconfidence
#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($preadorientation) != 'None':
 --p-read-orientation=$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"/>


		<conditional name="id_to_taxonomy_fp" optional="True">
		   <param name="selector" type="select" label="Reference taxonomy to query">
			  <option value="cached">Public databases</option>
			  <option value="history">Databases from your history</option>
		   </param>
		   <when value="cached">
			  <param argument="--taxonomy_fp" label="Reference taxonomy" type="select" optional="True">
				 <options from_data_table="qiime_taxonomy" />
			  </param>
		   </when>
		   <when value="history">
			  <param argument="--taxonomy_fp" type="data" format="qza,no_unzip.zip" label="Reference databases" optional="True" />
		   </when>
		</conditional>


		<conditional name="id_to_classifier_fp" optional="True">
		   <param name="selector" type="select" label="Reference classifier to query">
			  <option value="cached">Public classifiers</option>
			  <option value="history">Classifiers from your history</option>
		   </param>
		   <when value="cached">
			  <param name="classifier_fp" label="Reference classifier" type="select" optional="True">
				 <options from_data_table="qiime_rep_set" />
			  </param>
		   </when>
		   <when value="history">
			  <param name="classifier_fp" type="data" format="qza,no_unzip.zip" label="Reference classifier" optional="True" />
		   </when>
		</conditional>


		<param label="--p-maxaccepts: VALUE Int % Range(1, None) | Str % Choices('all') Maximum number of hits to keep for each query. Set to 'all' to keep all hits > perc-identity similarity.  [default: 10]" name="pmaxaccepts" optional="True" type="text" value="10" />
		<param label="--p-confidence: VALUE Float % Range(0, 1, inclusive_end=True) | Str % Choices('disable')  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. [default: 0.7]" name="pconfidence" optional="True" type="text" value="0.7" />


		<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" min="0" max="1" exclusive_end="False" />
		<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" min="0" max="1" exclusive_end="False" />
		<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" min="0.5" max="1" exclusive_end="True" />
		<param label="--p-read-orientation: TEXT Choices('same', 'reverse-complement', 'auto') 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.   [default: 'auto'] " name="preadorientation" optional="True" type="select" >
			<option value="None">Selection is Optional</option>
			<option value="same">same</option>
			<option value="reverse-complement">reverse-complement</option>
			<option selected="True" 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" min="1"/>
		<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" min="0"/>
	</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.
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.
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.
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>