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Nat Genet. 2017 Oct;49(10):1428-1436. doi: 10.1038/ng.3950. Epub 2017 Sep 4.

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

Author information

1
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
2
Department of Computer Science, Vrije Universiteit, Amsterdam, the Netherlands.
3
School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
4
Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
5
Department of Biomedical Data Sciences, Dartmouth College, Hanover, New Hampshire, USA.
6
Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
7
Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA.
8
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA.
9
Department of Computer Science, Yale University, New Haven, Connecticut, USA.
10
Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
11
CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
12
Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.

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.

PMID:
28869592
DOI:
10.1038/ng.3950
[Indexed for MEDLINE]

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