<tool id="Dimont" name="Dimont" version="0.1" force_history_refresh="true">
<description>- a universal tool for de-novo motif discovery.</description>
<command>java -Xms256M -Xmx2G -jar \$JAR_PATH/DimontWeb.jar --run $script_file $summary $summary.id $__new_file_path__ $summary.extra_files_path</command>
<inputs>
<param type="text" size="40" name="Dimont_jobname" label="Job name" value="" optional="true" help="Please enter a name for your job that should be used in the history (optional)">
</param>
<param type="data" format="fasta" name="Dimont_ps_Input_sequences" label="&lt;hr /&gt;Input sequences" help="The input sequences for de-novo motif discovery (can be uploaded using &quot;GetData&quot; -&gt; &quot;Upload File&quot;), annotated FastA format. The required format is described in the help section." value="" optional="false">
</param>

<param type="text" size="40" name="Dimont_ps_Position_tag" label="Position tag" help="The tag for the position information in the FastA-annotation of the input file" value="" optional="false">
</param>

<param type="text" size="40" name="Dimont_ps_Value_tag" label="Value tag" help="The tag for the value information in the FastA-annotation of the input file" value="" optional="false">
</param>

<param type="float" name="Dimont_ps_Standard_deviation" label="Standard deviation" help="The standard deviation of the position distribution centered at the position specified by the position tag" value="75.0" optional="false">
<validator type="in_range" min="1.0" max="10000.0" message="Value is not in the specified range [1.0, 10000.0]."/></param>

<param type="text" size="40" name="Dimont_ps_Weighting_factor" label="Weighting factor" help="The value for weighting the data; either a value between 0 and 1, or a description relative to the standard deviation (e.g. +4sd)" value="0.2" optional="false">
</param>

<param type="integer" name="Dimont_ps_Starts" label="&lt;hr /&gt;Starts" help="The number of pre-optimization runs." value="20" optional="false">
<validator type="in_range" min="1" max="100" message="Value is not in the specified range [1, 100]."/></param>

<param type="integer" name="Dimont_ps_Initial_motif_width" label="&lt;hr /&gt;Initial motif width" help="The motif width that is used initially, may be adjusted during optimization." value="15" optional="false">
<validator type="in_range" min="1" max="50" message="Value is not in the specified range [1, 50]."/></param>

<param type="integer" name="Dimont_ps_Markov_order_of_motif_model" label="Markov order of motif model" help="The Markov order of the model for the motif." value="0" optional="false">
<validator type="in_range" min="0" max="3" message="Value is not in the specified range [0, 3]."/></param>

<param type="integer" name="Dimont_ps_Markov_order_of_background_model" label="Markov order of background model" help="The Markov order of the model for the background sequence and the background sequence, -1 defines uniform distribution." value="-1" optional="false">
<validator type="in_range" min="-1" max="5" message="Value is not in the specified range [-1, 5]."/></param>

<param type="float" name="Dimont_ps_Equivalent_sample_size" label="&lt;hr /&gt;Equivalent sample size" help="Reflects the strength of the prior on the model parameters." value="4.0" optional="false">
<validator type="in_range" min="0.0" max="Infinity" message="Value is not in the specified range [0.0, Infinity]."/></param>

<param type="boolean" name="Dimont_ps_Delete_BSs_from_profile" label="Delete BSs from profile" help="A switch for deleting binding site positions of discovered motifs from the profile before searching for futher motifs." checked="True" optional="false">
</param>

</inputs>
<requirements>
	<requirement type="set_environment">JAR_PATH</requirement>
	<requirement type="binary" version=">=1.6">java</requirement>
</requirements>
<configfiles>
<configfile name="script_file">
&lt;Dimont_ps_Input_sequences&gt;
&lt;value&gt;
${Dimont_ps_Input_sequences}&lt;/value&gt;
&lt;extension&gt;
${Dimont_ps_Input_sequences.ext}&lt;/extension&gt;
&lt;/Dimont_ps_Input_sequences&gt;

&lt;Dimont_ps_Position_tag&gt;
${Dimont_ps_Position_tag}&lt;/Dimont_ps_Position_tag&gt;

&lt;Dimont_ps_Value_tag&gt;
${Dimont_ps_Value_tag}&lt;/Dimont_ps_Value_tag&gt;

&lt;Dimont_ps_Standard_deviation&gt;
${Dimont_ps_Standard_deviation}&lt;/Dimont_ps_Standard_deviation&gt;

&lt;Dimont_ps_Weighting_factor&gt;
${Dimont_ps_Weighting_factor}&lt;/Dimont_ps_Weighting_factor&gt;

&lt;Dimont_ps_Starts&gt;
${Dimont_ps_Starts}&lt;/Dimont_ps_Starts&gt;

&lt;Dimont_ps_Initial_motif_width&gt;
${Dimont_ps_Initial_motif_width}&lt;/Dimont_ps_Initial_motif_width&gt;

&lt;Dimont_ps_Markov_order_of_motif_model&gt;
${Dimont_ps_Markov_order_of_motif_model}&lt;/Dimont_ps_Markov_order_of_motif_model&gt;

&lt;Dimont_ps_Markov_order_of_background_model&gt;
${Dimont_ps_Markov_order_of_background_model}&lt;/Dimont_ps_Markov_order_of_background_model&gt;

&lt;Dimont_ps_Equivalent_sample_size&gt;
${Dimont_ps_Equivalent_sample_size}&lt;/Dimont_ps_Equivalent_sample_size&gt;

&lt;Dimont_ps_Delete_BSs_from_profile&gt;
${Dimont_ps_Delete_BSs_from_profile}&lt;/Dimont_ps_Delete_BSs_from_profile&gt;

</configfile>
</configfiles>
<outputs>
<data format="html" name="summary" label="#if str($Dimont_jobname) == '' then $tool.name + ' on ' + $on_string else $Dimont_jobname#">
</data>
</outputs>
<tests>
	<test>
		<param name="Dimont_jobname" value="Test" />
		<param name="Dimont_ps_Input_sequences" value="dimont_test.fasta" />
		<param name="Dimont_ps_Position_tag" value="peakSummit" />
		<param name="Dimont_ps_Value_tag" value="maxT" />
		<param name="Dimont_ps_Standard_deviation" value="75.0" />
		<param name="Dimont_ps_Weighting_factor" value="0.2" />
		<param name="Dimont_ps_Starts" value="20" />
		<param name="Dimont_ps_Initial_motif_width" value="15" />
		<param name="Dimont_ps_Markov_order_of_motif_model" value="0" />
		<param name="Dimont_ps_Markov_order_of_background_model" value="-1" />
		<param name="Dimont_ps_Equivalent_sample_size" value="4.0" />
		<param name="Dimont_ps_Delete_BSs_from_profile" value="True" />
		<output name="summary" file="Test/Test_html.html" />
	</test>
</tests>
<help>
**Dimont** is a universal tool for de-novo motif discovery. Dimont has successfully been applied to ChIP-seq, ChIP-exo and protein-binding microarray (PBM) data.

Input sequences must be supplied in an annotated FastA format as a file uploaded by the "Upload File" task in section "Get Data" of Galaxy or generated using the "Dimont Data Extractor" tool.
In the annotation of each sequence, you need to provide a value that reflects the confidence that this sequence is bound by the factor of interest.
Such confidences may be peak statistics (e.g., number of fragments under a peak) for ChIP data or signal intensities for PBM data. In addition, you need to provide an anchor position within the sequence. 
In case of ChIP data, this anchor position could for instance be the peak summit.
For instance, an annotated FastA file for ChIP-exo data comprising sequences of length 100 centered around the peak summit could look like::
	
	> peak: 50; signal: 515
	ggccatgtgtatttttttaaatttccac...
	> peak: 50; signal: 199
	GGTCCCCTGGGAGGATGGGGACGTGCTG...
	...

where the anchor point is given as 50 for the first two sequences, and the confidence amounts to 515 and 199, respectively.
The FastA comment may contain additional annotations of the format ``key1 : value1; key2: value2;...``.
We also provide an example_ input file and a Perl script_ for preparing data in the format required by Dimont.

Accordingly, you would need to set the parameter "Position tag" to ``peak`` and the parameter "Value tag" to ``signal`` for the input file.

For the standard deviation of the position prior, the initial motif length and the number of pre-optimization runs, we provide default values that worked well in our studies on ChIP and PBM data. 
However, you may want adjust these parameters to meet your prior information.

The parameter "Markov order of the motif model" sets the order of the inhomogeneous Markov model used for modeling the motif. If this parameter is set to ``0``, you obtain a position weight matrix (PWM) model. 
If it is set to ``1``, you obtain a weight array matrix (WAM) model. You can set the order of the motif model to at most ``3``.

The parameter "Markov order of the background model" sets the order of the homogeneous Markov model used for modeling positions not covered by a motif. 
If this parameter is set to ``-1``, you obtain a uniform distribution, which worked well for ChIP data. For PBM data, orders of up to ``4`` resulted in an increased prediction performance in our case studies. The maximum allowed value is ``5``.

The parameter "Weighting factor" defines the proportion of sequences that you expect to be bound by the targeted factor with high confidence. For ChIP data, the default value of ``0.2`` typically works well. 
For PBM data, containing a large number of unspecific probes, this parameter should be set to a lower value, e.g. ``0.01``.

The "Equivalent sample size" reflects the strength of the influence of the prior on the model parameters, where higher values smooth out the parameters to a greater extent.

The parameter "Delete BSs from profile" defines if BSs of already discovered motifs should be deleted, i.e., "blanked out", from the sequence before searching for futher motifs.

You can also install this web-application within your local Galaxy server. Instructions can be found at the Dimont_ page of Jstacs. 
There you can also download a command line version of Dimont.

If you use Dimont, please cite

 \J. Grau, S. Posch, I. Grosse, and J. Keilwagen. A general approach for discriminative de-novo motif discovery from high-throughput data. *Nucleic Acids Research*, 41(21):e197, 2013.

If you experience problems using Dimont, please contact_ us.

.. _example: http://www.jstacs.de/downloads/dimont-example.fa
.. _script: http://www.jstacs.de/index.php/Dimont#Data_preparation
.. _Dimont: http://jstacs.de/index.php/Dimont
.. _contact: mailto:grau@informatik.uni-halle.de
</help>
</tool>