Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets.

ABSTRACT: In some dimeric cases of transcription factor (TF) binding, the specificity of dimeric motifs has been observed to differ notably from what would be expected were the two factors to bind to DNA independently of each other. Current motif discovery methods are unable to learn monomeric and dimeric motifs in modular fashion such that deviations from the expected motif would become explicit and the noise from dimeric occurrences would not corrupt monomeric models. We propose a novel modeling technique and an expectation maximization algorithm, implemented as software tool MODER, for discovering monomeric TF binding motifs and their dimeric combinations. Given training data and seeds for monomeric motifs, the algorithm learns in the same probabilistic framework a mixture model which represents

SUBMITTER: Toivonen J 

PROVIDER: S-EPMC5934673 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature