Integrative Prediction of Gene Expression with Chromatin Accessibility and Conformation Data
bioRxiv preprint doi: https://doi.org/10.1101/704478; this version posted July 16, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Integrative prediction of gene expression with chromatin accessibility and conformation data Florian Schmidt1;2;3;y, Fabian Kern1;3;4;y, and Marcel H. Schulz1;2;3;5;6;∗ 1High-throughput Genomics & Systems Biology, Cluster of Excellence on Multimodal Computing and Interaction, Saarland Informatics Campus, 66123 Saarbr¨ucken Germany 2Computational Biology & Applied Algorithmics, Max-Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbr¨ucken Germany 3Center for Bioinformatics, Saarland Informatics Campus, 66123 Saarbr¨ucken Germany 4Chair for Clinical Bioinformatics, Saarland Informatics Campus, 66123 Saarbr¨ucken Germany 5Institute of Cardiovascular Regeneration, Goethe-University, Theodor-Stern-Kai 7, 60590 Frankfurt am Main Germany 6German Center for Cardiovascular Research, Partner site Rhein-Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main Germany y These authors contributed equally ∗ Correspondence: marcel.schulz@em.uni-frankfurt.de Abstract Background: Enhancers play a fundamental role in orchestrating cell state and development. Al- though several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of en- hancers, which triggered the development of various methods to infer promoter enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organisa- tion of the chromatin, paved the way to pinpoint long-range PEIs.
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