annotate rgFastQC.xml @ 0:e28c965eeed4 draft

Imported from capsule None

<tool name="FastQC:Read QC" id="fastqc" version="0.52">
  <description>reports using FastQC</description>
  <command interpreter="python">
    rgFastQC.py -i "$input_file" -d "$html_file.files_path" -o "$html_file" -n "$out_prefix" -f "$input_file.ext" -j "$input_file.name" -e "\$JAVA_JAR_PATH/fastqc"
#if $contaminants.dataset and str($contaminants) > ''
-c "$contaminants"
#end if
  </command>
  <requirements>
    <requirement type="package" version="0.10.1">FastQC</requirement>
  </requirements>
  <inputs>
    <param format="fastqsanger,fastq,bam,sam" name="input_file" type="data" label="Short read data from your current history" />
    <param name="out_prefix" value="FastQC" type="text" label="Title for the output file - to remind you what the job was for" size="80"
      help="Letters and numbers only please - other characters will be removed">
    <sanitizer invalid_char="">
        <valid initial="string.letters,string.digits"/>
    </sanitizer>
    </param>
    <param name="contaminants" type="data" format="tabular" optional="true" label="Contaminant list" 
           help="tab delimited file with 2 columns: name and sequence.  For example: Illumina Small RNA RT Primer	CAAGCAGAAGACGGCATACGA"/>
  </inputs>
  <outputs>
    <data format="html" name="html_file"  label="${out_prefix}_${input_file.name}.html" />
  </outputs>
  <tests>
    <test>
      <param name="input_file" value="1000gsample.fastq" />
      <param name="out_prefix" value="fastqc_out" />
      <param name="contaminants" value="fastqc_contaminants.txt" ftype="tabular" />
      <output name="html_file" file="fastqc_report.html" ftype="html" lines_diff="100"/>
    </test>
  </tests>
  <help>

.. class:: infomark

**Purpose**

FastQC aims to provide a simple way to do some quality control checks on raw
sequence data coming from high throughput sequencing pipelines. 
It provides a modular set of analyses which you can use to give a quick
impression of whether your data has any problems of 
which you should be aware before doing any further analysis.

The main functions of FastQC are:

- Import of data from BAM, SAM or FastQ files (any variant)
- Providing a quick overview to tell you in which areas there may be problems
- Summary graphs and tables to quickly assess your data
- Export of results to an HTML based permanent report
- Offline operation to allow automated generation of reports without running the interactive application


-----


.. class:: infomark

**FastQC**

This is a Galaxy wrapper. It merely exposes the external package FastQC_ which is documented at FastQC_
Kindly acknowledge it as well as this tool if you use it.
FastQC incorporates the Picard-tools_ libraries for sam/bam processing.

The contaminants file parameter was borrowed from the independently developed
fastqcwrapper contributed to the Galaxy Community Tool Shed by J. Johnson.

-----

.. class:: infomark

**Inputs and outputs**

FastQC_ is the best place to look for documentation - it's very good. 
A summary follows below for those in a tearing hurry.

This wrapper will accept a Galaxy fastq, sam or bam as the input read file to check.
It will also take an optional file containing a list of contaminants information, in the form of
a tab-delimited file with 2 columns, name and sequence.

The tool produces a single HTML output file that contains all of the results, including the following:

- Basic Statistics
- Per base sequence quality
- Per sequence quality scores
- Per base sequence content
- Per base GC content
- Per sequence GC content
- Per base N content
- Sequence Length Distribution
- Sequence Duplication Levels
- Overrepresented sequences
- Kmer Content

All except Basic Statistics and Overrepresented sequences are plots.
 .. _FastQC: http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/
 .. _Picard-tools: http://picard.sourceforge.net/index.shtml

</help>
</tool>