NatureGenomicsPeer-reviewed journal article
Index and biological spectrum of accessible DNA elements in the human genome
Wouter R. A. MeulemaniD, Alexander Muratov, Eric RynesiD, Jessica M. Halow, Kristen LeeiD, Daniel BatesiD, Morgan Diegel, Douglas Dunn, Fidencio Jun Neri, Athanasios TeodosiadisiD, Alex P. ReynoldsiD, Eric HaugeniD, Jemma S. NelsoniD, Audra Johnson, Mark FrerkeriD, Michael J BuckleyiD, Richard S. SandstromiD, Jeff D. VierstraiD, Rajinder KauliD, John A. StamatoyannopoulosiD
Abstract
DNase I hypersensitive sites (DHSs) are generic markers of regulatory DNA and harbor disease- and phenotypic trait-associated genetic variation. We established high-precision maps of DNase I hypersensitive sites from 733 human biosamples encompassing 439 cell and tissue types and states, and integrated these to precisely delineate and numerically index ~3.6 million DHSs encoded within the human genome, providing a common coordinate system for regulatory DNA. Here we show that the expansive scale of cell and tissue states sampled exposes an unprecedented degree of stereotyped actuation of large sets of elements, signaling the operation of distinct genome-scale regulatory programs. We show further that the complex actuation patterns of individual elements can be captured comprehensively by a simple regulatory vocabulary reflecting their dominant cellular manifestation. This vocabulary, in turn, enables comprehensive and quantitative regulatory annotation of both protein-coding genes and the vast array of well-defined but poorly-characterized non-coding RNA genes. Finally, we show that the combination of high-precision DHSs and regulatory vocabularies markedly concentrate disease- and trait-associated non-coding genetic signals both along the genome and across cellular compartments. Taken together, our results provide a common and extensible coordinate system and vocabulary for human regulatory DNA, and a new global perspective on the architecture of human gene regulation.
- organism: human
- assay: ChIP-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 331
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