Bayesian Centroid Inference and Characterization of Posterior Spaces with Applications in Motif-Finding

• Activity Number: 630
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Luis E. Carvalho*+
• Companies: Boston University
• Keywords: Gibbs sampling ; stochastic backtracking

Abstract

Biological sequences may contain patterns that signal important biomolecularfunctions; a classical example is regulation of gene expression by transcription factors that bind to specific patterns in genomic promoter regions. In de-novo motif discovery we are given a set of sequences that sharea common motif and aim to identify not only this motif composition, but also the binding sites in each sequence of the set. We present a Bayesian model that is an extended version of the model adopted by the Gibbs motif sampler, and propose a new centroid estimator that arises from a refined and meaningful loss function for binding site inference. We discuss the main advantages of centroid estimation for motif discovery, including computational convenience, and how its principled derivation offers further insights about the posterior distribution of binding site configurations. We also illustrate the proposed approach on both simulated and real datasets, and conclude with directions for future work.