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2008

BayesMD: Flexible Biological Modeling for Motif Discovery

8 years 3 months ago
BayesMD: Flexible Biological Modeling for Motif Discovery
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fashion. A mixture of Dirichlets is used as prior over nucleotide probabilities in binding sites. It is trained on transcription factor (TF) databases in order to extract the typical properties of TF binding sites. In a similar fashion we train organism-specific priors for the background sequences. Lastly, we use a prior over the position of binding sites. This prior represents information complementary to the motif and background priors coming from conservation, local sequence complexity, nucleosome occupancy, etc. and assumptions about the number of occurrences. The Bayesian inference is carried out using a combination of exact marginalization (multinomial parameters) and sampling (over the position of sites). Robust sampling results are achieved using the advanced sampling method parallel tempering. In a pos...
Man-Hung Eric Tang, Anders Krogh, Ole Winther
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where JCB
Authors Man-Hung Eric Tang, Anders Krogh, Ole Winther
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