Position Specific Scoring Matrices capture the distribution of residues observed in each position in a conserved motif, but are not a good model for variable length motifs, recognition of new instances with insertions and deletions, and positional dependencies. Moreover, PSSMs can be used to search for instances of an ungapped motif in an unlabeled sequence, but do not lend themselves to precis...

The PROJECTION algorithm by Buhler and Tompa is one of the best existing methods for solving hard motif discovery problems for monad motifs of fixed length l. In this paper we introduce the AGGREGATION algorithm, which like PROJECTION projects all l-mers from the given input sequences into buckets, but uses a different scheme for selecting buckets for subsequent refinement search. This new neig...

Time series motif discovery is an important problem with applications in a variety of areas that range from telecommunications to medicine. Several algorithms have been proposed to solve the problem. However, these algorithms heavily use expensive random disk accesses or assume the data can fit into main memory. They only consider motifs at a single resolution and are not suited to interactivit...

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 ...