Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
Jacobs et al. report in Science that different co-repressors repress the transcriptional activity of different subsets of enhancers associated with genes of different function.
Leon Mutesa highlights a 2009 article by Yehuda and Bierer that considered the relevance of epigenetic mechanisms to post-traumatic stress disorder, which inspired his own research on the importance of DNA methylation changes in trauma survivors.
In this Review, the authors discuss recent advances in our understanding of Mediator and TFIID, coactivators associated with the RNA polymerase II (Pol II) pre-initiation complex (PIC), focusing on their structure, interactions with activators and impact on the function of the PIC.
A study in Nature reports a strong association between asymptomatic SARS-CoV-2 infections and the HLA-B*15:01 allele and reveals mechanistic insights into its protective effect.
In this Review, Zhou et al. discuss our current understanding of the genetic control of key steps involved in human brain development and diseases, and they describe current and emerging approaches for investigating the underlying genetic architecture.
Liang et al. report in Nature that complementary Alu sequences allow an enhancer to find its cognate promoter over long distances, potentially through the formation of RNA duplexes.
Fay-Wei Li recalls a 1966 paper by Klekowski and Baker, who built on their observation that homosporous pteridophytes have many more chromosomes than heterosporous lineages to generate hypotheses on the evolutionary impact of polyploidy.
This Review explores the use of non-mammalian model organisms in the genetic diagnosis of rare diseases, focusing on the use of worms, flies and zebrafish. The strategies, genetic technologies and approaches to using these models are discussed, as well as how they can provide insight into more common disease mechanisms.
Single-cell omics approaches are providing unprecedented insights into cellular function and dysfunction. This Editorial highlights the remarkable potential of these technologies and their profound impact on our understanding of biology and disease.
Control can be applied to alter the ecological or evolutionary trajectory of a target system towards a predefined objective. In this Review, the authors discuss the aims, applications, mechanisms and dynamics of eco-evolutionary control across different biological systems.
In this Review, the authors summarize the current evidence for the use of genomic sequencing in newborn screening for rare diseases. As several large-scale studies launch internationally, the authors discuss major challenges and opportunities that lie ahead and identify key research priorities.
In this Review, the authors describe how the application of new technologies to the microRNA (miRNA) field has yielded key insights into miRNA biology. The authors summarize our current understanding of miRNA biogenesis, function and processing, and highlight challenges to address in future research.
Incongruence occurs when phylogenetic trees show conflicting evolutionary histories such as patterns of branching or relationships among taxa. This Review discusses the biological and analytical factors that lead to incongruence, methodological advances to identify and resolve incongruence, and avenues for future research.
Regulatory circuits of gene expression can be represented as gene regulatory networks (GRNs) that are useful to understand cellular identity and disease. Here, the authors review the computational methods used to infer GRNs — in particular from single-cell multi-omics data — as well as the biological insights that they can provide, and methods for their downstream analysis and experimental assessment.
A study in Nature Biotechnology describes Scriabin, a highly scalable framework for inference of cell–cell communication from scRNA-seq data at the level of individual cells.