Parameter estimation for robust HMM analysis of ChIP-chip data.

BMC Bioinformatics
Peter HumburgGlenn Stone

Abstract

Tiling arrays are an important tool for the study of transcriptional activity, protein-DNA 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. Here we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip 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. We illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like Ti...Continue Reading

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Citations

Dec 29, 2009·Nature Genetics·Guillaume J Filion, Bas van Steensel
Jan 30, 2010·Bioinformatics·Qianxing Mo, Faming Liang
Sep 24, 2009·BMC Bioinformatics·Christiana SpyrouSimon Tavaré
Oct 28, 2009·BMC Bioinformatics·Mingqi WuYanan Tian
Jul 6, 2010·BMC Bioinformatics·Zhaohui S QinArul M Chinnaiyan
Sep 11, 2013·BMC Bioinformatics·Caroline BérardMarie-Laure Martin-Magniette
Mar 23, 2011·PLoS Genetics·Catherine RegnardPeter B Becker
Mar 19, 2013·Statistical Applications in Genetics and Molecular Biology·Xiao GuanghuaYang Xie

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Methods Mentioned

BETA
immunoprecipitation
ChIP-chip
ChIP

Software Mentioned

TileMap
Welch
Baum
R package tileHMM

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