A Bayesian changepoint analysis of ChIP-Seq data of Lamin B

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.