STEME: an accurate efficient motif finder for large data sets.
Project description
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.
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.
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