An Estimation of Distribution Algorithm for Motif Discovery

Abstract

The problem of Transcription Factor Binding Sites identification or motif discovery is to identify the motif binding sites in the cis-regulatory regions of DNA sequences. The biological experiments are expensive and the problem is NP-hard computationally. We have proposed Estimation of Distribution Algorithm for Motif Discovery (EDAMD). We use Bayesian analysis to derive the fitness function to measure the posterior probability of a set of motif instances, which can be used to handle a variable number of motif instances in the sequences. EDAMD adopts a Gaussian distribution to model the distribution of the sets of motif instances, which is capable of capturing the bivariate correlation among the positions of motif instances. When a new Position Frequency Matrix (PFM) is generated from the Gaussian distribution, a new set of motif instances is identified based on the PFM via the Greedy Refinement operation. At the end of a generation, the Gaussian distribution is updated with the sets of motif instances. Since Greedy Refinement assumes a single motif instance on a sequence, a Post Processing operation based on the fitness function is used to find more motif instances after the evolution. The experiments have verified that EDAMD is comparable to or better than GAME and GALF on the real problems tested in this paper.