title: A Bayesian changepoint analysis of ChIP-Seq data of Lamin B.
abstract: The spatial organisation of the chromosomes in the nucleus is influenced by chromatin regions binding to the nucleic lamina, i.e., the inner part of the nucleic envelope. To investigate the architecture of chromosomes in the interphase nucleus, it is thus of high interest to detect such chromatin segments. This goal can be achieved by considering the fibrous protein Lamin B as a surrogate, since regions of high abundance of Lamin B can indicate chromatin segments attached to the nucleic lamina. We analyse ChIP-Seq (Chromatin-Immunoprecipitation Sequencing) data from an experiment that is designed to record Lamin B abundance. We introduce a Bayesian segmentation procedure in which a Markov Chain Monte Carlo (MCMC) algorithm is used for inference about the desired segmentation. The procedure is based on a Bayesian hierarchical model. Inference allows the distinction between regions of high versus low levels of Lamin B, and therefore, gives an insight into the binding of the chromatin to the nucleic envelope. An implementation of this approach is available in the statistical software environment R. This article is part of a special issue entitled: Computational proteomics in the post-identification era. Guest Editors: Martin Eisenacher and Christian Stephan.
authors.affiliation: Faculty of Statistics, TU Dortmund University, Dortmund, Germany.
authors.forename: S
authors.initials: S
authors.lastname: Herrmann
authors.forename: H
authors.initials: H
authors.lastname: Schwender
authors.forename: K
authors.initials: K
authors.lastname: Ickstadt
authors.forename: P
authors.initials: P
authors.lastname: Müller
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D047369:Chromatin Immunoprecipitation; D034921:Lamin Type B / Q000737:chemistry*
chemical_list: D034921:Lamin Type B
keywords: Bayesian hierarchical model; Changepoint detection; Markov Chain Monte Carlo algorithm