**SlimDimontPredictor** allows for predicting binding sites in new data using a previously trained parse local inhomogeneous mixture (Slim) model. For training a Slim model see tool "SlimDimont".

This tool may be useful if you, for instance, want to predict binding sites of a previously discovered motifs in other data sets, or if you want to try different p-values for filtering predictions.

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.
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.

For instance, an annotated FastA file for ChIP-exo data could look like::
	
	> signal: 515
	ggccatgtgtatttttttaaatttccac...
	> signal: 199
	GGTCCCCTGGGAGGATGGGGACGTGCTG...
	...

where the confidence for the first two sequences 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.

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

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 parameter "p-value" defines a threshold on the p-values of predicted binding sites, and only binding sites with a lower p-value are reported by SlimDimontPredictor.
The SlimDimont tool uses a p-value threshold of ``1E-3``, which is also the default value of SlimDimontPredictor.

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

If you use Slim models for motif discovery, please cite

 \J. Keilwagen and J. Grau. Varying levels of complexity in transcription factor binding motifs. doi:10.1093/nar/gkv577 Nucleic Acids Research, 2015.

and

 \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 SlimDimontPredictor, please contact_ us.

.. _example: http://www.jstacs.de/downloads/dimont-example.fa
.. _Slim: http://jstacs.de/index.php/Slim
.. _contact: mailto:grau@informatik.uni-halle.de