Multi-histone ChIP-Seq Analysis with DecoDen
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.18.512665v1?rss=1

Authors: Narendra, T., Visona, G., Cardona, C. d. J., Schweikert, G.

Abstract: Epigenetic mechanisms coordinate packaging, accessibility and read-out of the DNA sequence within the chromatin context. They significantly contribute to the regulation of gene expression. Thus, they play fundamental roles during differentiation on the one hand and maintenance and propagation of cell identity on the other. Epigenetic malfunctioning is associated with a large range of diseases, from neurodevelopmental disorders to cancer progression. In humans, hundreds of known epigenetic factors and complexes are involved in establishing covalent modifications on the DNA sequence itself and on associated histone proteins. Within the cellular context, the resulting combinatorial epigenomic patterns are neither established nor interpreted independently of each other and therefore exhibit high correlations in a region-specific manner. Post-translational modifications of histone proteins can be analysed using Chromatin Immunoprecipitation followed by sequencing (ChIP-Seq). Often, several assays for a number of different histone modifications are performed as part of the same experimental design. These measurements are, however, confounded by shared biases including chromatin accessibility, PCR amplification and mappability. Existing computational methods analyse each histone modification separately, while often also merging biological or technical replicates. We introduce DecoDen, a new approach that leverages replicates and multi-histone ChIP-Seq experiments for a fixed cell type to learn and remove shared biases. DecoDen (Deconvolve and Denoise) consists of two major steps: We use non-negative matrix factorisation (NMF) to learn a joint cell-type specific signal. Half-sibling regression (HSR) is then used to correct for the cell-type specific biases in the histone modification signals. We demonstrate that DecoDen is a robust and interpretable method that enables the unbiased discovery of subtle peaks, which are particularly important in an individual-specific context.

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