Parameter estimation for robust HMM analysis of ChIP-chip data

Background: Tiling arrays are an important tool for the study of transcriptional activity, proteinDNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis. Results: Here we develop a hidden Markov model for the analysis of chromatin structure ChIPchip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure. Concl...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Peter Humburg, David Bulger, Glenn Stone
• Keywords: BMCBI 2008, Chromatin Structure, Hidden Markov Models, Parameter