D'Elia Benedetta, Fuxman Bass Juan
Department of Molecular Biology, Cell Biology & Biochemistry, Boston University, Boston, MA, USA; Biological Design Center, Boston University, Boston, MA, USA.
Department of Molecular Biology, Cell Biology & Biochemistry, Boston University, Boston, MA, USA; Department of Biology, Boston University, Boston, MA, USA; Bioinformatics Program, Boston University, Boston, MA, USA; Biological Design Center, Boston University, Boston, MA, USA.
Curr Opin Struct Biol. 2025 Jun 27;94:103105. doi: 10.1016/j.sbi.2025.103105.
Determining the rules of transcriptional regulation, associated with a complex transcription factor grammar, is fundamental to understand the control of most biological processes, disease mechanisms, and evolution. High-throughput reporter assays, such as MPRAs and STARR-seq, have enabled systematic functional annotations of genomes and have provided large substrates for training machine learning models to determine these rules, predict the activity of native and synthetic elements, and design elements for different applications. This review provides an overview of high-throughput reporter assays and their applications for the study of enhancers, promoters, silencers and insulators. We discuss how these assays help identify causal disease-associated non-coding variants and design synthetic elements with desired features for functional studies or therapeutic purposes.
确定与复杂转录因子语法相关的转录调控规则，对于理解大多数生物过程、疾病机制和进化的控制至关重要。高通量报告基因检测，如MPRA和STARR-seq，已实现对基因组的系统功能注释，并为训练机器学习模型提供了大量底物，以确定这些规则、预测天然和合成元件的活性，以及设计用于不同应用的元件。本综述概述了高通量报告基因检测及其在增强子、启动子、沉默子和绝缘子研究中的应用。我们讨论了这些检测如何帮助识别与疾病相关的因果非编码变异，并设计具有所需特征的合成元件用于功能研究或治疗目的。
