Reconstruction of enhancer–target networks in 935 samples of human primary cells, tissues and cell lines

Abstract

We propose a new method for determining the target genes of transcriptional enhancers in specific cells and tissues. It combines global trends across many samples and sample-specific information, and considers the joint effect of multiple enhancers. Our method outperforms existing methods when predicting the target genes of enhancers in unseen samples, as evaluated by independent experimental data. Requiring few types of input data, we are able to apply our method to reconstruct the enhancer-target networks in 935 samples of human primary cells, tissues and cell lines, which constitute by far the largest set of enhancer-target networks. The similarity of these networks from different samples closely follows their cell and tissue lineages. We discover three major co-regulation modes of enhancers and find defense-related genes often simultaneously regulated by multiple enhancers bound by different transcription factors. We also identify differentially methylated enhancers in hepatocellular carcinoma (HCC) and experimentally confirm their altered regulation of HCC-related genes.

Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs

“…However, these approaches lose the tissue specificity of the interactions. Other approaches integrate many diverse chromatin signals such as posttranslational histone modifications, chromatin accessibility, or transcriptional activity [57][58][59][60][61][62], and combine them with sequence features [63], or evolutionary constrains [64]. While these methods predict enhancer-gene association with good performance, they require for each specific condition of interest a multiplicity of input datasets, which are often not available.…”

Enhancer-gene maps in the human and zebrafish genomes using evolutionary linkage conservation

“…The first uses 35 chromosomal conformation capture techniques to identify physical interaction between two loci in the genome [25][26][27][28][29][30] , but it is not clear which of these interactions are linked to a regulatory role. The second measures the correlation of transcription activity between noncoding sequences and nearby genes 31,32 , assuming the two are signatures of a coordinated regulatory function. Finally, single nucleotide variants (SNVs) can be associated to significant 40 differences in gene expression, thus qualifying as expression QTL (eQTL) that presumably reside within or close to enhancers 33 .…”

Integrative analysis of epigenetics data identifies gene-specific regulatory elements

“…Using machine learning, Cao et al propose to integrate predicted REMs into cell-type specific interaction networks [6], similar to Hait et al, who also provide regulatory-maps derived from statistical associations between the activity of REMs and target gene-expression [21]. Shooshtari et al combined chromatin accessibility data with Genome-Wide Association study Studies (GWAS) to better pinpoint regulatory events in autoimmune and inflammatory diseases [47].…”