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.

Michael A. Beer

Dustin Shigaki

Danwei Huangfu

Annual Review of Genomics and Human Genetics

自 2000 年出版的《基因组学和人类遗传学年度回顾》涵盖了基因组学领域的重大发展，因为它们适用于人类遗传学和人类基因组。我们对基因组技术、基因组结构和功能、基因改造、人类变异和群体遗传学、人类进化以及更重要的是人类遗传疾病的各个方面以及包括个体化医学等领域都特别感兴趣。

Since its inception in 2000, the Annual Review of Genomics and Human Genetics has covered significant advancements in the field of genomics, particularly as they relate to human genetics and the human genome. We are particularly interested in various aspects of genomic technologies, genomic structure and function, genetic modification, human variation and population genetics, human evolution, and, importantly, human genetic diseases, including fields such as personalized medicine.

《Annual Review of Genomics and Human Genetics》自2000年出版以来，涵盖了基因组学领域的重要进展，特别是与人类遗传学和人类基因组相关的研究。我们关注基因组技术、基因组结构与功能、基因改造、人类变异与群体遗传学、人类进化，以及人类遗传疾病等多个方面，尤其是个体化医学等领域。

Enhancer Predictions and Genome-Wide Regulatory Circuits

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, and are reviewed in the context of enhancers prediction and gene regulatory network models specifying cell fate.

Category	Quartile
生物学	2
生物学, 遗传学	2

Enhancer Predictions and Genome-Wide Regulatory Circuits

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
GENETICS & HEREDITY	1