High-resolution genome-wide functional dissection of transcriptional regulatory regions and nucleotides in human
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s41467-018-07746-1.pdf
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Author(s)
Goggin, Sarah M.
Saadat, Alham
Wang, Li
Sinnott-Armstrong, Nasa
Wang, Xinchen
He, Liang
Kellis, Manolis
Claussnitzer, Melina
Date Issued
December 2018
Journal
Nature Communications
Publisher
Nature Publishing Group
Citation
Wang, Xinchen, Liang He, Sarah M. Goggin, Alham Saadat, Li Wang, Nasa Sinnott-Armstrong, Melina Claussnitzer, and Manolis Kellis. “High-Resolution Genome-Wide Functional Dissection of Transcriptional Regulatory Regions and Nucleotides in Human.” Nature Communications 9, no. 1 (December 2018).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41467-018-07746-1
