Varying levels of complexity in transcription factor binding motifs.

ABSTRACT: Binding of transcription factors to DNA is one of the keystones of gene regulation. The existence of statistical dependencies between binding site positions is widely accepted, while their relevance for computational predictions has been debated. Building probabilistic models of binding sites that may capture dependencies is still challenging, since the most successful motif discovery approaches require numerical optimization techniques, which are not suited for selecting dependency structures. To overcome this issue, we propose sparse local inhomogeneous mixture (Slim) models that combine putative dependency structures in a weighted manner allowing for numerical optimization of dependency structure and model parameters simultaneously. We find that Slim models yield a substantially better

SUBMITTER: Keilwagen J 

PROVIDER: S-EPMC4605289 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature