<tool id="rseqc_read_distribution" name="Read Distribution" version="1.1">
    <description>calculates how mapped reads were distributed over genome feature</description>
    <requirements>
        <requirement type="package" version="1.7.1">numpy</requirement>
        <requirement type="package" version="2.3.7">rseqc</requirement>
    </requirements>
    <command>
        read_distribution.py -i $input -r $refgene > $output
    </command>
    <stdio>
        <exit_code range="1:" level="fatal" description="An error occured during execution, see stderr and stdout for more information" />
        <regex match="[Ee]rror" source="both" description="An error occured during execution, see stderr and stdout for more information" />
    </stdio>
    <inputs>
        <param name="input" type="data" format="bam,sam" label="input bam/sam file" />
        <param name="refgene" type="data" format="bed" label="reference gene model" />
    </inputs>
    <outputs>
        <data format="txt" name="output" />
    </outputs>
    <help>
read_distribution.py
++++++++++++++++++++

Provided a BAM/SAM file and reference gene model, this module will calculate how mapped
reads were distributed over genome feature (like CDS exon, 5'UTR exon, 3' UTR exon, Intron,
Intergenic regions). When genome features are overlapped (e.g. a region could be annotated
as both exon and intron by two different transcripts) , they are prioritize as:
CDS exons > UTR exons > Introns > Intergenic regions, for example, if a read was mapped to
both CDS exon and intron, it will be assigned to CDS exons.

* "Total Reads": This does NOT include those QC fail,duplicate and non-primary hit reads
* "Total Tags": reads spliced once will be counted as 2 tags, reads spliced twice will be counted as 3 tags, etc. And because of this, "Total Tags" >= "Total Reads"
* "Total Assigned Tags": number of tags that can be unambiguously assigned the 10 groups (see below table).
* Tags assigned to "TSS_up_1kb" were also assigned to "TSS_up_5kb" and "TSS_up_10kb", tags assigned to "TSS_up_5kb" were also assigned to "TSS_up_10kb". Therefore, "Total Assigned Tags" = CDS_Exons + 5'UTR_Exons + 3'UTR_Exons + Introns + TSS_up_10kb + TES_down_10kb.
* When assign tags to genome features, each tag is represented by its middle point.

RSeQC cannot assign those reads that:

* hit to intergenic regions that beyond region starting from TSS upstream 10Kb to TES downstream 10Kb.
* hit to regions covered by both 5'UTR and 3' UTR. This is possible when two head-to-tail transcripts are overlapped in UTR regions.
* hit to regions covered by both TSS upstream 10Kb and TES downstream 10Kb. 


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

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

Reference gene model
    Gene model in BED format.

Sample Output
++++++++++++++

Output:

===============     ============        ===========         ===========
Group               Total_bases         Tag_count           Tags/Kb    
===============     ============        ===========         ===========
CDS_Exons           33302033            20002271            600.63     
5'UTR_Exons         21717577            4408991             203.01     
3'UTR_Exons         15347845            3643326             237.38     
Introns             1132597354          6325392             5.58       
TSS_up_1kb          17957047            215331              11.99      
TSS_up_5kb          81621382            392296              4.81       
TSS_up_10kb         149730983           769231              5.14       
TES_down_1kb        18298543            266161              14.55      
TES_down_5kb        78900674            729997              9.25       
TES_down_10kb       140361190           896882              6.39       
===============     ============        ===========         ===========

-----

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>
</tool>