Finding DNA Regulatory Motifs with Position-dependent Models

We consider the problem of de novo DNA motif discovery. The position weight matrix (PWM) model has been extensively used, yet this model makes the assumption that nucleotides at different positions are independent of each other. Recent results have shown that nucleotides bound by transcription factors often exhibit adjacent or nonadjacent dependencies. We address this problem by devising positional dependency models capable of capturing adjacent dependencies and non-adjacent dependencies (SPWDM). Our algorithms are based on Gibbs sampling to update the model parameter and dependencies structure. We compare two scoring functions:   -score and a conditional probability based score. We also improve several Gibbs sampling stages. Experiments are carried out on simulated and real data, showing that the SPWDM model makes improvement over pure PWM. The modifications to the Gibbs sampling algorithm are also shown to be effective. 