**SimpleMoDe** 
is a no-frills tool for learning model structure and parameters of a single iPMM of given maximal order from a set of pre-aligned DNA sequences of same length.
Structure learning of the iPMM is done according to the BIC score and the parameters are estimated according to factorized sequential NML. For details regarding the learning procedure see:
R. Eggeling, T. Roos, P. Myllymaki, I. Grosse. Robust learning of inhomogeneous PMMs. *Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS)*. JMLR: Workshop and Conference Proceedings volume 33, 2014. 

If the content of the "Input data" file starts with '>', it is interpreted as FastA file. Otherwise it is interpreted as plain text, where every line contains a single sequence. 
The input expects upper- and lower case letters of the standard DNA alphabet {A,C,G,T}. If other symbols from the IUPAC code (such as N) are encountered, they are replaced by a random sample from the distribution of {A,C,G,T} in the data set. 
All input sequences are expected to have the same length.

The learning procedure attempts to avoid overfitting by selecting sparse model structures, so even a high maximal "Order" of the iPMM can fruitful in principle.
However, a large value does have a significant effect on the running time of the structure learning algorithm. 
Note that setting "Order" to 0 results in a traditional Position Weight Matrix (PWM) model.

A "Name" can be optionally given to make the output identifiable in the course of several experiments. 
If no "Name" is specified, it is set by default to "iPMM(*d*)", where *d* is the specified maximal model order.

The tool returns
(i) an *XML representation of the model* that can be used in subsequent analyses with tools **ScanApp** or **ClassificationApp**.
(ii) the precise *parameter values* in human-readable form.
(iii) a *sequence logo* of the position-specific mononucleotide marginals of the model.
(iv) a *sequence logo* of position-specific mononucleotide marginals of the model as reverse complement
(v) a *conditional sequence logo* that visualizes all dependencies learned by the model in terms of context-specific conditional probabilities.