GitHub - JohnReid/STEME: An efficient accurate motif finder based on MEME and implemented using suffix arrays

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 STEME
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Accurate efficient motif finding in large data sets
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STEME started life as an approximation to the Expectation-Maximisation algorithm for 
the type of model used in motif finders such as MEME. STEME’s EM approximation 
runs an order of magnitude more quickly than the MEME implementation for typical 
parameter settings. STEME has now developed into a fully-fledged motif finder in 
its own right.

.. _MEME: http://meme.sdsc.edu/meme/intro.html


STEME's source code can be found at its `PyPI page`_. The latest version of STEME's documentation is 
at its `Python package page`_. An installation of STEME is available_ to run over the web.


.. _PyPI page: http://pypi.python.org/pypi/STEME/
.. _available : http://sysbio.mrc-bsu.cam.ac.uk/STEME/
.. _Python package page: http://packages.python.org/STEME/