title: Bayesian centroid estimation for motif discovery.
abstract: Biological sequences may contain patterns that signal important biomolecular functions; a classical example is regulation of gene expression by transcription factors that bind to specific patterns in genomic promoter regions. In motif discovery we are given a set of sequences that share a common motif and aim to identify not only the motif composition, but also the binding sites in each sequence of the set. We 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, using simulated and real datasets, that the centroid estimator can differ from the traditional maximum a posteriori or maximum likelihood estimators.
authors: Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America.
authors: Luis
authors: L
authors: Carvalho
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001665:Binding Sites; D019295:Computational Biology; D016013:Likelihood Functions; D059372:Nucleotide Motifs / Q000235:genetics*; D009711:Nucleotides / Q000737:chemistry* / Q000378:metabolism; D011485:Protein Binding; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
chemical_list: D009711:Nucleotides; D014157:Transcription Factors