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

TL;DR

This paper introduces a graph-based computational approach that integrates chromatin accessibility and gene expression data across multiple cell types to predict long-range gene regulatory interactions in the human genome.

Contribution

It presents a novel method combining open chromatin and expression data with graph algorithms to identify distal regulatory elements supported by multiple datasets.

Findings

Validated interactions with Hi-C and CAGE data.

Identified high-confidence distal regulatory regions.

Revealed regulatory elements located several megabases from target genes.

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.