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A Bayesian Search for Transcriptional Motifs 1 2 1,3 1,3 Andrew K. Miller *, Cristin G. Print , Poul M. F. Nielsen , Edmund J. Crampin * 1 Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand, 2 Department of Molecular Medicine and NZ Bioinformatics Institute, The University of Auckland, Auckland, New Zealand, 3 Department of Engineering Science, Faculty of Engineering, The University of Auckland, Auckland, New Zealand Abstract Identifying transcription factor (TF) binding sites (TFBSs) is an important step towards understanding transcriptional regulation. A common approach is to use gaplessly aligned, experimentally supported TFBSs for a particular TF, and algorithmically search for more occurrences of the same TFBSs. The largest publicly available databases of TF binding specificities contain models which are represented as position weight matrices (PWM). There are other methods using more sophisticated representations, but these have more limited databases, or aren’t publicly available. Therefore, this paper focuses on methods that search using one PWM per TF. An algorithm, MATCHTM, for identifying TFBSs corresponding to a particular PWM is available, but is not based on a rigorous statistical model of TF binding, making it difficult to interpret or adjust the parameters and output of the algorithm. Furthermore, there is no public description of the algorithm sufficient to exactly reproduce it. Another algorithm, MAST, computes a p-value for the presence of a TFBS using true probabilities of finding each base at each offset from that position. We developed a statistical model, BaSeTra