view scaffhunter/UPGMA.xml @ 2:ec31e4883c99 draft default tip

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<tool id="UPGMA" name="UPGMA" version="0.1"> 
    <description> : Order scaffold based on pseudo UPGMA method</description>  
    <stdio>
        <exit_code range="1:" />
    </stdio>
    <command>
		source $__tool_directory__/include_scaffhunter.sh ;
        python $__tool_directory__/UPGMA.py  
		--mat $mat
		--scaff $scaff
		--type $type
		--out1 $scaffold_order
		--out2 $marker_order
    </command>
    <inputs>  
    	<param name="mat" type="data" format="tabular" label="The data matrix with pair-wise statistics between markers" />
    	<param name="scaff" type="data" format="tabular" label="A tabulated file containing in col 1 : markers name, col2 : scaffold name (the file should be ordered by scaffold and marker order on scaffold)" />
		<param name="type" type="select" label="Data type" >
			<option value="IDENT" selected="true">linkage information (LOD score)</option>
			<option value="DIF">divergence information (recombination rate)</option>
		</param>
		<param name="prefix" type="text" label="Prefix of output files" value="upgma" />
    </inputs>
    <outputs>
        <data format="tabular" name="scaffold_order" label="${tool.name} : $prefix scaffold order" />
		<data format="tabular" name="marker_order" label="${tool.name} : $prefix marker order" />
    </outputs>
    <tests>
        <test>
            <param name="mat" value="lod_matrix.txt"/>
            <param name="scaff" value="alignment_scaffold.txt" /> 
            <output name="scaffold_order" file="scaffold_order.txt"/> 
            <output name="marker_order" file="marker_order.txt"/> 
        </test>
    </tests> 
    <help>

**Overview**

This program takes in input a matrix containing maker linkage or divergence and a tabulated file locating these markers on scaffolds (or sequence) and try to group and order them based on an UPGMA like approach.

This program work as followed:

* an mean linkage/divergence is calculated between scaffolds

* scaffolds are then grouped using an UPGMA like approach

* scaffolds are orientated and positioned (at the beginning or the end of a precedent group) trying to optimize a score calculated for each position and orientation.

In case of marker linkage information (LOD score) in the matrix, the score is calculated as followed:

.. image:: http://orygenesdb.cirad.fr/images/lod_score.png 

In case of marker divergence information (recombination rate) in the matrix the score is calculated as followed:

.. image:: http://orygenesdb.cirad.fr/images/recombinaison_rate.png 

with n the number of markers in the LG to order, xi and xj are the position of markers i and j in the tested order, and LODij the LOD score between markers i and j. To optimize computation time and as order is not tested within scaffolds, i and j are markers from different scaffolds.

-----

.. class:: infomark

**Galaxy integration** Martin Guillaume (CIRAD), Droc Gaetan (CIRAD).

.. class:: infomark

**Support** For any questions about Galaxy integration, please send an e-mail to [e-mail]

.. class:: infomark

**Program encapsulated in Galaxy by South Green**
    
	</help>
	<citations>
        <citation type="doi">10.1186/s12864-016-2579-4</citation> 
    </citations>
</tool>