High-resolution genome-wide functional dissection of transcriptional regulatory regions and nucleotides in human.

Xinchen Wang, Liang He, Sarah M Goggin, Alham Saadat, Li Wang, Nasa Sinnott-Armstrong, Melina Claussnitzer, Manolis Kellis

Journal: Nature communications 2019;9(1):5380

PMID: 30568279

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Abstract

Genome-wide epigenomic maps have revealed millions of putative enhancers and promoters, but experimental validation of their function and high-resolution dissection of their driver nucleotides remain limited. Here, we present HiDRA (High-resolution Dissection of Regulatory Activity), a combined experimental and computational method for high-resolution genome-wide testing and dissection of putative regulatory regions. We test ~7 million accessible DNA fragments in a single experiment, by coupling accessible chromatin extraction with self-transcribing episomal reporters (ATAC-STARR-seq). By design, fragments are highly overlapping in densely-sampled accessible regions, enabling us to pinpoint driver regulatory nucleotides by exploiting differences in activity between partially-overlapping fragments using a machine learning model (SHARPR-RE). In GM12878 lymphoblastoid cells, we find ~65,000 regions showing enhancer function, and pinpoint ~13,000 high-resolution driver elements. These are enriched for regulatory motifs, evolutionarily-conserved nucleotides, and disease-associated genetic variants from genome-wide association studies. Overall, HiDRA provides a high-throughput, high-resolution approach for dissecting regulatory regions and driver nucleotides.

Address: Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.; Institute for Genomic Medicine, Columbia University, New York, NY, 10024, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. [email protected].; Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. [email protected].; Institute of Nutritional Science, University of Hohenheim, Garbenstrasse 30, 70599, Stuttgart, Germany. [email protected].; Harvard Medical School, Harvard University, Boston, MA, 02215, USA. [email protected].; Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. [email protected].; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. [email protected].

DOI: 10.1038/s41467-018-07746-1