comparison picard_EstimateLibraryComplexity.xml @ 8:e417b1d6288d draft

planemo upload for repository https://github.com/galaxyproject/tools-devteam/tree/master/tools/picard commit bf94a1505c131fb3f67c867b6e1d886780efa42e

7:08f69add4d06

    <import>picard_macros.xml</import>
  </macros>
  <expand macro="requirements" />
  <command detect_errors="exit_code"><![CDATA[
    @java_options@
    
    picard
    EstimateLibraryComplexity
    
    INPUT="${inputFile}"
    OUTPUT="${outFile}"
    
    MIN_IDENTICAL_BASES="${min_identical_bases}"
    MAX_DIFF_RATE="${max_diff_rate}"
    MIN_MEAN_QUALITY="${min_mean_quality}"
    MAX_GROUP_RATIO="${max_group_ratio}"
    #import pipes
    READ_NAME_REGEX=${ pipes.quote( str( $read_name_regex ) ) or "''" }
    OPTICAL_DUPLICATE_PIXEL_DISTANCE="${optical_duplicate_pixel_distance}"
    
    VALIDATION_STRINGENCY="${validation_stringency}"
    QUIET=true
    VERBOSITY=ERROR
  
  ]]></command>
  <inputs>
    <param format="bam" name="inputFile" type="data" label="Select SAM/BAM dataset or dataset collection" help="If empty, upload or import a SAM/BAM dataset" />
    <param name="min_identical_bases" type="integer" value="5" label="The minimum number of bases at the starts of reads that must be identical for reads to be grouped together for duplicate detection" help="MIN_IDENTICAL_BASES; In effect total_reads / 4^max_id_bases reads will be compared at a time, so lower numbers will produce more accurate results but consume exponentially more memory and CPU; default=5"/>
    <param name="max_diff_rate" type="float" value="0.03" label="The maximum rate of differences between two reads to call them identical" help="MAX_DIFF_RATE; default=0.03"/>

      </sanitizer>
    </param>
    <param name="optical_duplicate_pixel_distance" type="integer" value="100" min="0" max="500" label="The maximum offset between two duplicte clusters in order to consider them optical duplicates" help="OPTICAL_DUPLICATE_PIXEL_DISTANCE; default=100"/>

   <expand macro="VS" />
    
  </inputs> 
  
  <outputs>
    <data format="tabular" name="outFile" label="${tool.name} on ${on_string}: Library complexity report"/>
  </outputs>
  
  <tests>
    <test>
      <param name="inputFile" value="picard_EstimateLibraryComplexity.bam" ftype="bam"/>
      <param name="min_identical_bases" value="5"/>
      <param name="max_diff_rate" value="0.03"/>

      <param name="max_group_ratio" value="500"/>
      <param name="validation_stringency" value="LENIENT"/>
      <output name="outFile" file="picard_EstimateLibraryComplexity_test1.tab" ftype="tabular" lines_diff="4"/>
    </test>
  </tests>
  
  
  <help>

**Purpose**

Attempts to estimate library complexity from sequence of read pairs alone. Does so by sorting all reads by the first N bases (5 by default)

@dataset_collections@

@description@

  MIN_IDENTICAL_BASES=Integer   The minimum number of bases at the starts of reads that must be identical for reads to be 
                                grouped together for duplicate detection.  In effect total_reads / 4^max_id_bases reads 
                                will be compared at a time, so lower numbers will produce more accurate results but 
                                consume exponentially more memory and CPU.  Default value: 5. 
  
  MAX_DIFF_RATE=Double          The maximum rate of differences between two reads to call them identical.  Default value: 
                                0.03.
  
  MIN_MEAN_QUALITY=Integer      The minimum mean quality of the bases in a read pair for the read to be analyzed. Reads 
                                with lower average quality are filtered out and not considered in any calculations.  
                                Default value: 20.
  
  MAX_GROUP_RATIO=Integer       Do not process self-similar groups that are this many times over the mean expected group 
                                size. I.e. if the input contains 10m read pairs and MIN_IDENTICAL_BASES is set to 5, then 
                                the mean expected group size would be approximately 10 reads.  Default value: 500.
  
  READ_NAME_REGEX=String        Regular expression that can be used to parse read names in the incoming SAM file. Read 
                                names are parsed to extract three variables: tile/region, x coordinate and y coordinate. 
                                These values are used to estimate the rate of optical duplication in order to give a more 
                                accurate estimated library size. Set this option to null to disable optical duplicate 
                                detection. The regular expression should contain three capture groups for the three 
                                variables, in order. It must match the entire read name. Note that if the default regex 
                                is specified, a regex match is not actually done, but instead the read name  is split on 
                                colon character. For 5 element names, the 3rd, 4th and 5th elements are assumed to be 
                                tile, x and y values. For 7 element names (CASAVA 1.8), the 5th, 6th, and 7th elements 
                                are assumed to be tile, x and y values.  Default value: 
                                [a-zA-Z0-9]+:[0-9]:([0-9]+):([0-9]+):([0-9]+).*.
  
  OPTICAL_DUPLICATE_PIXEL_DISTANCE=Integer  
                                The maximum offset between two duplicte clusters in order to consider them optical 
                                duplicates. This should usually be set to some fairly small number (e.g. 5-10 pixels) 
                                unless using later versions of the Illumina pipeline that multiply pixel values by 10, in 
                                which case 50-100 is more normal.  Default value: 100.
  

@more_info@

  </help>
</tool>

8:e417b1d6288d

    <import>picard_macros.xml</import>
  </macros>
  <expand macro="requirements" />
  <command detect_errors="exit_code"><![CDATA[
    @java_options@
    @symlink_element_identifier@
    picard
    EstimateLibraryComplexity

    INPUT='$inputFile.element_identifier'
    OUTPUT="${outFile}"

    MIN_IDENTICAL_BASES="${min_identical_bases}"
    MAX_DIFF_RATE="${max_diff_rate}"
    MIN_MEAN_QUALITY="${min_mean_quality}"
    MAX_GROUP_RATIO="${max_group_ratio}"
    #import pipes
    READ_NAME_REGEX=${ pipes.quote( str( $read_name_regex ) ) or "''" }
    OPTICAL_DUPLICATE_PIXEL_DISTANCE="${optical_duplicate_pixel_distance}"

    VALIDATION_STRINGENCY="${validation_stringency}"
    QUIET=true
    VERBOSITY=ERROR

  ]]></command>
  <inputs>
    <param format="bam" name="inputFile" type="data" label="Select SAM/BAM dataset or dataset collection" help="If empty, upload or import a SAM/BAM dataset" />
    <param name="min_identical_bases" type="integer" value="5" label="The minimum number of bases at the starts of reads that must be identical for reads to be grouped together for duplicate detection" help="MIN_IDENTICAL_BASES; In effect total_reads / 4^max_id_bases reads will be compared at a time, so lower numbers will produce more accurate results but consume exponentially more memory and CPU; default=5"/>
    <param name="max_diff_rate" type="float" value="0.03" label="The maximum rate of differences between two reads to call them identical" help="MAX_DIFF_RATE; default=0.03"/>

      </sanitizer>
    </param>
    <param name="optical_duplicate_pixel_distance" type="integer" value="100" min="0" max="500" label="The maximum offset between two duplicte clusters in order to consider them optical duplicates" help="OPTICAL_DUPLICATE_PIXEL_DISTANCE; default=100"/>

   <expand macro="VS" />

  </inputs>

  <outputs>
    <data format="tabular" name="outFile" label="${tool.name} on ${on_string}: Library complexity report"/>
  </outputs>

  <tests>
    <test>
      <param name="inputFile" value="picard_EstimateLibraryComplexity.bam" ftype="bam"/>
      <param name="min_identical_bases" value="5"/>
      <param name="max_diff_rate" value="0.03"/>

      <param name="max_group_ratio" value="500"/>
      <param name="validation_stringency" value="LENIENT"/>
      <output name="outFile" file="picard_EstimateLibraryComplexity_test1.tab" ftype="tabular" lines_diff="4"/>
    </test>
  </tests>


  <help>

**Purpose**

Attempts to estimate library complexity from sequence of read pairs alone. Does so by sorting all reads by the first N bases (5 by default)

@dataset_collections@

@description@

  MIN_IDENTICAL_BASES=Integer   The minimum number of bases at the starts of reads that must be identical for reads to be
                                grouped together for duplicate detection.  In effect total_reads / 4^max_id_bases reads
                                will be compared at a time, so lower numbers will produce more accurate results but
                                consume exponentially more memory and CPU.  Default value: 5.

  MAX_DIFF_RATE=Double          The maximum rate of differences between two reads to call them identical.  Default value:
                                0.03.

  MIN_MEAN_QUALITY=Integer      The minimum mean quality of the bases in a read pair for the read to be analyzed. Reads
                                with lower average quality are filtered out and not considered in any calculations.
                                Default value: 20.

  MAX_GROUP_RATIO=Integer       Do not process self-similar groups that are this many times over the mean expected group
                                size. I.e. if the input contains 10m read pairs and MIN_IDENTICAL_BASES is set to 5, then
                                the mean expected group size would be approximately 10 reads.  Default value: 500.

  READ_NAME_REGEX=String        Regular expression that can be used to parse read names in the incoming SAM file. Read
                                names are parsed to extract three variables: tile/region, x coordinate and y coordinate.
                                These values are used to estimate the rate of optical duplication in order to give a more
                                accurate estimated library size. Set this option to null to disable optical duplicate
                                detection. The regular expression should contain three capture groups for the three
                                variables, in order. It must match the entire read name. Note that if the default regex
                                is specified, a regex match is not actually done, but instead the read name  is split on
                                colon character. For 5 element names, the 3rd, 4th and 5th elements are assumed to be
                                tile, x and y values. For 7 element names (CASAVA 1.8), the 5th, 6th, and 7th elements
                                are assumed to be tile, x and y values.  Default value:
                                [a-zA-Z0-9]+:[0-9]:([0-9]+):([0-9]+):([0-9]+).*.

  OPTICAL_DUPLICATE_PIXEL_DISTANCE=Integer
                                The maximum offset between two duplicte clusters in order to consider them optical
                                duplicates. This should usually be set to some fairly small number (e.g. 5-10 pixels)
                                unless using later versions of the Illumina pipeline that multiply pixel values by 10, in
                                which case 50-100 is more normal.  Default value: 100.


@more_info@

  </help>
</tool>