MOTIVATION The motif discovery problem consists of finding over-represented patterns in a collection of biosequences. It is one of the classical sequence analysis problems, but still has not been satisfactorily solved in an exact and efficient manner. This is partly due to the large number of possibilities of defining the motif search space and the notion of over-representation. Even for well-d...

BayesMD [1] is a probabilistic, Bayesian model for predicting novel transcription factor binding sites. Biological information about binding sites properties, background sequence models, occurrence and positional preferences are built into the model in modular fashion. Mixture prior parameters for the motif and background are trained using information on TFBSs and organismspecific promoter sequ...

The goal of motif discovery algorithms is to efficiently find unknown recurring patterns in time series. Most available algorithms cannot utilize domain knowledge in any way which results in quadratic or at least sub-quadratic time and space complexity. For large time series datasets for which domain knowledge can be available this is a severe limitation. In this paper we define the Constrained...

Ensembl’s human non-coding and protein coding genes are used to automatically find DNA pattern motifs. The Backus-Naur form (BNF) grammar for regular expressions is used by genetic programming to ensure the generated strings are legal. The evolved motif suggests the presence of Thymine followed by one or more Adenines etc. early in transcripts indicate a non-protein coding gene.