Graph-based data integration predicts long-range regulatory interactions across the human genome

Sofie Demeyer; Tom Michoel

doi:10.1101/004622

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bioRxiv · 10.1101/004622

Graph-based data integration predicts long-range regulatory interactions across the human genome

Sofie Demeyer·Tom Michoel

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Abstract

Transcriptional regulation of gene expression is one of the main processes that affect cell diversification from a single set of genes. Regulatory proteins often interact with DNA regions located distally from the transcription start sites (TSS) of the genes. We developed a computational method that combines open chromatin and gene expression information for a large number of cell types to identify these distal regulatory elements. Our method builds correlation graphs for publicly available DNase-seq and exon array datasets with matching samples and uses graph-based methods to filter findings supported by multiple datasets and remove indirect interactions. The resulting set of interactions was validated with both anecdotal information of known long-range interactions and unbiased experimental data deduced from Hi-C and CAGE experiments. Our results provide a novel set of high-confidence candidate open chromatin regions involved in gene regulation, often located several Mb away from the TSS of their target gene.

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Sofie Demeyer, Tom Michoel. 2014-04-29. Graph-based data integration predicts long-range regulatory interactions across the human genome. https://doi.org/10.1101/004622

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