| Summary: | <p>Abstract</p> <p>Background</p> <p>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 <it>ad hoc</it>. 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.</p> <p>Results</p> <p>Here we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using <it>t </it>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.</p> <p>Conclusion</p> <p>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 <it>ad hoc </it>estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.</p>
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