DIpartite: A tool for detecting bipartite motifs by considering base interdependencies.

ABSTRACT: It is extremely important to identify transcription factor binding sites (TFBSs). Some TFBSs are proposed to be bipartite motifs known as two-block motifs separated by gap sequences with variable lengths. While position weight matrix (PWM) is commonly used for the representation and prediction of TFBSs, dinucleotide weight matrix (DWM) enables expression of the interdependencies of neighboring bases. By incorporating DWM into the detection of bipartite motifs, we have developed a novel tool for ab initio motif detection, DIpartite (bipartite motif detection tool based on dinucleotide weight matrix) using a Gibbs sampling strategy and the minimization of Shannon's entropy. DIpartite predicts the bipartite motifs by considering the interdependencies of neighboring positions, that is, DWM. We

SUBMITTER: Vahed M 

PROVIDER: S-EPMC6716629 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature