annotate read_NVC.xml @ 49:6b33e31bda10 draft

Uploaded tar based on https://github.com/lparsons/galaxy_tools/tree/master/tools/rseqc 1a3c419bc0ded7c40cb2bc3e7c87bfb01ddfeba2

<tool id="rseqc_read_NVC" name="Read NVC" version="2.4galaxy1">
    <description>to check the nucleotide composition bias</description>

    <macros>
        <import>rseqc_macros.xml</import>
    </macros>

    <requirements>
        <expand macro="requirement_package_r" />
        <expand macro="requirement_package_numpy" />
        <expand macro="requirement_package_rseqc" />
    </requirements>

    <expand macro="stdio" />

    <version_command><![CDATA[read_NVC.py --version]]></version_command>

    <command>
        read_NVC.py
            --input-file $input
            --out-prefix output
            $nx
            --mapq $mapq
    </command>

    <inputs>
        <param name="input" type="data" format="bam,sam" label="input bam/sam file" help="(--input-file)"/>
        <param name="nx" type="boolean" value="false" truevalue="--nx" falsevalue="" label="Include N,X in NVC plot" help="(--nx)"/>
        <param name="mapq" type="integer" label="Minimum mapping quality (default=30)" help="Minimum phred scale mapping quality to consider a read 'uniquely mapped' (--mapq)" value="30" />
    </inputs>

    <outputs>
        <data format="xls" name="outputxls" from_work_dir="output.NVC.xls" label="${tool.name} on ${on_string} (XLS)" />
        <data format="txt" name="outputr" from_work_dir="output.NVC_plot.r" label="${tool.name} on ${on_string} (R Script)" />
        <data format="pdf" name="outputpdf" from_work_dir="output.NVC_plot.pdf" label="${tool.name} on ${on_string} (PDF)" />
    </outputs>

    <tests>
        <test>
            <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/>
            <output name="outputxls" file="output.NVC.xls"/>
            <output name="outputr" file="output.NVC_plot.r"/>
        </test>
    </tests>

    <help><![CDATA[
read_NVC.py
+++++++++++

This module is used to check the nucleotide composition bias. Due to random priming, certain
patterns are over represented at the beginning (5'end) of reads. This bias could be easily
examined by NVC (Nucleotide versus cycle) plot. NVC plot is generated by overlaying all
reads together, then calculating nucleotide composition for each position of read
(or each sequencing cycle). In ideal condition (genome is random and RNA-seq reads is
randomly sampled from genome), we expect A%=C%=G%=T%=25% at each position of reads.

NOTE: this program expect a fixed read length

Inputs
++++++++++++++

Input BAM/SAM file
    Alignment file in BAM/SAM format.

Include N,X in NVC plot
    Plots N and X alongside A, T, C, and G in plot.

Output
++++++++++++++

This module is used to check the nucleotide composition bias. Due to random priming, certain patterns are over represented at the beginning (5'end) of reads. This bias could be easily examined by NVC (Nucleotide versus cycle) plot. NVC plot is generated by overlaying all reads together, then calculating nucleotide composition for each position of read (or each sequencing cycle). In ideal condition (genome is random and RNA-seq reads is randomly sampled from genome), we expect A%=C%=G%=T%=25% at each position of reads.


1. output.NVC.xls: plain text file, each row is position of read (or sequencing cycle), each column is nucleotide (A,C,G,T,N,X)
2. output.NVC_plot.r: R script to generate NVC plot.
3. output.NVC_plot.pdf: NVC plot.


.. image:: http://rseqc.sourceforge.net/_images/NVC_plot.png
   :height: 600 px
   :width: 600 px
   :scale: 80 %

-----

About RSeQC
+++++++++++

The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation.

The RSeQC package is licensed under the GNU GPL v3 license.

.. image:: http://rseqc.sourceforge.net/_static/logo.png

.. _RSeQC: http://rseqc.sourceforge.net/
]]>
    </help>

    <expand macro="citations" />

</tool>