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Retrieving chromatin patterns from deep sequencing data using correlation functions

Jana Molitor, Jan-Philipp Mallm, View ORCID ProfileKarsten Rippe, Fabian Erdel
doi: https://doi.org/10.1101/054049
Jana Molitor
1German Cancer Research Center (DKFZ) and Bioquant, Research Group Genome Organization & Function, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
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Jan-Philipp Mallm
1German Cancer Research Center (DKFZ) and Bioquant, Research Group Genome Organization & Function, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
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Karsten Rippe
1German Cancer Research Center (DKFZ) and Bioquant, Research Group Genome Organization & Function, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
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  • For correspondence: Karsten.Rippe@dkfz.de F.Erdel@dkfz.de
Fabian Erdel
1German Cancer Research Center (DKFZ) and Bioquant, Research Group Genome Organization & Function, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
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  • For correspondence: Karsten.Rippe@dkfz.de F.Erdel@dkfz.de
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Abstract

Epigenetic modifications and other chromatin features partition the genome on multiple length scales. They define chromatin domains with distinct biological functions that come in sizes ranging from single modified DNA bases to several megabases in case of heterochromatic histone modifications. Due to chromatin folding, domains that are well separated along the linear nucleosome chain can form long-range interactions in three-dimensional space. It has now become a routine task to map epigenetic marks and chromatin structure by deep sequencing methods. However, assessing and comparing the properties of chromatin domains and their positional relationships across data sets without a priori assumptions remains challenging. Here, we introduce multi-scale correlation evaluation (MCORE), which uses the fluctuation spectrum of mapped sequencing reads to quantify and compare chromatin patterns over a broad range of length scales in a model-independent manner. We applied MCORE to map the chromatin landscape in mouse embryonic stem cells and differentiated neural cells. We integrated sequencing data from chromatin immunoprecipitation, RNA expression, DNA methylation and chromosome conformation capture experiments into network models that reflect the positional relationships among these features on different genomic scales. Furthermore, we used MCORE to compare our experimental data to models for heterochromatin reorganization during differentiation. The application of correlation functions to deep sequencing data complements current evaluation schemes and will support the development of quantitative descriptions of chromatin networks.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 06, 2017.
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Retrieving chromatin patterns from deep sequencing data using correlation functions
Jana Molitor, Jan-Philipp Mallm, Karsten Rippe, Fabian Erdel
bioRxiv 054049; doi: https://doi.org/10.1101/054049
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Retrieving chromatin patterns from deep sequencing data using correlation functions
Jana Molitor, Jan-Philipp Mallm, Karsten Rippe, Fabian Erdel
bioRxiv 054049; doi: https://doi.org/10.1101/054049

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