STEME: an accurate efficient motif finder for large data sets.
Project description
STEME
=======
Accurate efficient motif finding in large data sets
---------------------------------------------------
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/
=======
Accurate efficient motif finding in large data sets
---------------------------------------------------
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/
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