<tool id="artbio_bam_cleaning" name="ARTbio bam cleaning" version="1.6+galaxy2">
    <description>
        on flags and PCR Duplicates and MD recalibration
    </description>
    <macros>
        <import>macro.xml</import>
    </macros>
    <requirements>
        <requirement type="package" version="1.6">samtools</requirement>
        <requirement type="package" version="0.7.1">sambamba</requirement>
        <requirement type="package" version="1.3.2">freebayes</requirement>
    </requirements>
    <stdio>
        <exit_code range="1:" level="fatal" description="Error occured" />
    </stdio>
    <command detect_errors="exit_code"><![CDATA[
    @pipefail@
    @set_fasta_index@
    #set input_base = 'input'   
    ln -f -s $input_bam.metadata.bam_index input.bam.bai &&
    ln -s $input_bam input.bam &&
    sambamba view -h -t \${GALAXY_SLOTS:-2} --filter='mapping_quality >= 1 and not(unmapped) and not(mate_is_unmapped)' -f 'bam' $input_base".bam"
    | samtools rmdup - -
    |tee $input_base".filt1.dedup.bam"| bamleftalign --fasta-reference reference.fa -c --max-iterations "5" -
    | samtools calmd  -C 50 -b -@ \${GALAXY_SLOTS:-2} - reference.fa > $calmd
    #if $pipeline == 'fullfilter':
    && sambamba view -h -t \${GALAXY_SLOTS:-2} --filter='mapping_quality <= 254' -f 'bam' -o $fullfilter $calmd
    #end if
    ]]></command>
    <inputs>
        <expand macro="reference_source_conditional" />
        <param name="input_bam" type="data" format="bam" label="BAM or SAM file to process"/>
        <param name="pipeline" type="select" label="where to stop the pipeline">
            <option value="CalMD">At CalMD processing to keep split read alignments</option>
            <option value="fullfilter" selected="true">Full bam processing, will eliminate split read alignments in the final bam file</option>
        </param>
    </inputs>
    <outputs>
        <data name="calmd" format="bam" label="CalMD filter (for lumpy-smoove)">
            <filter>pipeline == 'CalMD'</filter>
        </data>
        <data name="fullfilter" format="bam" label="Full filtering (for somatic-varscan)">
             <filter>pipeline == 'fullfilter'</filter>
        </data>
       
    </outputs>
    <tests>
        <test>
            <param name="input_bam" value="match_chr21_DBA_974.bam" ftype="bam" />
            <param name="reference_source_selector" value="history" />
            <param name="ref_file" value="chr21.fa" />
            <output name="fullfilter" file="match_chr21_DBA_974.filt1.dedup.bamleft.calmd.filt2.bam" ftype="bam" />
        </test>
        <test>
            <param name="input_bam" value="match_chr21_DBA_974.bam" ftype="bam" />
            <param name="reference_source_selector" value="history" />
            <param name="ref_file" value="chr21.fa" />
            <param name="pipeline" value="CalMD"/>
            <output name="calmd" file="match_chr21_DBA_974.filt1.dedup.bamleft.calmd.bam" ftype="bam" />
        </test>
    </tests>
    <help>
ARTbio bam cleaning overview
============================

This tool is wrapping several cleaning steps to produce bam files suitable for subsequent
analyses with lumpy-smoove (or other large structural variation callers) or with
somatic-varscan (or small structural variation callers)


Workflow 
=============

The tool is using the following command line for filtering:

::

    sambamba view -h -t 8 --filter='mapping_quality >= 1 and not(unmapped) and not(mate_is_unmapped)' -f 'bam' $input_base".bam"
    &#124; samtools rmdup - -
    &#124;tee $input_base".filt1.dedup.bam" &#124; bamleftalign --fasta-reference reference.fa -c --max-iterations "5" -
    &#124; samtools calmd  -C 50 -b -@ 4 - reference.fa &gt; $input_base".filt1.dedup.bamleft.calmd.bam" ;
    sambamba view -h -t 8 --filter='mapping_quality &lt;&#61; 254' -f 'bam' -o $input_base".filt1.dedup.bamleft.calmd.filt2.bam" $input_base".filt1.dedup.bamleft.calmd.bam"
    
Purpose
--------

This "workflow" tool was generated in order to limit the number of ``python metadata/set.py`` jobs
which occur at each step of standard galaxy workflows. Indeed, these jobs are poorly optimized and may last considerable
amounts of time when datasets are large, at each step, lowering the overall performance of the workflow.

    </help>
    <citations>
        <citation type="doi">10.1371/journal.pone.0168397</citation>
    </citations>
</tool>