Open AccessEnhancer Predictions and Genome-Wide Regulatory Circuits
Author(s) -
M Beer,
Dustin Shigaki,
Danwei Huangfu
Publication year - 2020
Publication title -
annual review of genomics and human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.677
H-Index - 117
eISSN - 1545-293X
pISSN - 1527-8204
DOI - 10.1146/annurev-genom-121719-010946
Subject(s) - enhancer , biology , computational biology , transcription factor , regulatory sequence , encode , gene , regulation of gene expression , epigenomics , context (archaeology) , gene regulatory network , epigenetics , genetics , gene expression , dna methylation , paleontology
Spatiotemporal control of gene expression during development requires orchestrated activities of numerous enhancers, which are cis -regulatory DNA sequences that, when bound by transcription factors, support selective activation or repression of associated genes. Proper activation of enhancers is critical during embryonic development, adult tissue homeostasis, and regeneration, and inappropriate enhancer activity is often associated with pathological conditions such as cancer. Multiple consortia [e.g., the Encyclopedia of DNA Elements (ENCODE) Consortium and National Institutes of Health Roadmap Epigenomics Mapping Consortium] and independent investigators have mapped putative regulatory regions in a large number of cell types and tissues, but the sequence determinants of cell-specific enhancers are not yet fully understood. Machine learning approaches trained on large sets of these regulatory regions can identify core transcription factor binding sites and generate quantitative predictions of enhancer activity and the impact of sequence variants on activity. Here, we review these computational methods in the context of enhancer prediction and gene regulatory network models specifying cell fate.