MultiChIPmixHMM: An R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates

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Abstract

Background: Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates.Results: MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements.Conclusion: We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org. © 2013 Bérard et al.; licensee BioMed Central Ltd.

Figures

  • Figure 1 ROC curves. ROC curves for ChIPmix, MultiChIPmixHMM and TileHMM, for the two simulated scenarios. In ChIPmix_Union, the minimal value of the conditional probability over the replicates is considered. In ChIPmix_Inter, this is the maximal value.
  • Table 1 Comparison of ChIPmix, MultiChIPmixHMM and TileHMM after classification
  • Figure 2 Comparison of ChIPmix andMultiChIPmixHMM. Comparison of ChIPmix and MultiChIPmixHMM illustrated for a selected region on chromosome 4. Probes identified as enriched are shown in red. Non-enriched probes are displayed in black. Blue bars correspond to the location of genes.
  • Figure 3 Comparison of ChIPmix andMultiChIPmixHMM. Example of one known H3K27me3 target gene identified only with MultiChIPmixHMM. The second line corresponds to the log-ratio signal, and for the two last lines, the scale corresponds to the conditional probabilities. We note that the conditional probabilities are clearly higher with MultiChIPmixHMM.

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APA

Bérard, C., Seifert, M., Mary-Huard, T., & Martin-Magniette, M. L. (2013). MultiChIPmixHMM: An R package for ChIP-chip data analysis modeling spatial dependencies and multiple replicates. BMC Bioinformatics, 14. https://doi.org/10.1186/1471-2105-14-271

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