Zeitlinger Julia, Roy Sushmita, Ay Ferhat, Mathelier Anthony, Medina-Rivera Alejandra, Mahony Shaun, Sinha Saurabh, Ernst Jason
Stowers Institute for Medical Research, Kansas City, MO 64112, United States.
Department of Pathology & Laboratory Medicine, The University of Kansas Medical Center, Kansas City, KS 66160, United States.
Bioinform Adv. 2025 May 9;5(1):vbaf106. doi: 10.1093/bioadv/vbaf106. eCollection 2025.
: Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges toward solving this fundamental problem. We describe how cis-regulatory elements are mapped with various genomics assays and how studies of the 3D chromatin organization could help identifying long-range regulatory effects. We discuss how the cis-regulatory sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also describe current methods for mapping gene regulatory networks to describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals.
预测基因变异如何在生物体、细胞和分子水平上影响表型结果,需要解读顺式调控密码,即非编码区域调控基因的序列规则。从这个角度出发,我们讨论了在解决这一基本问题方面的最新计算进展和挑战。我们描述了如何通过各种基因组学分析来定位顺式调控元件,以及三维染色质组织的研究如何有助于识别长程调控效应。我们讨论了如何利用序列到功能的神经网络来学习和解释顺式调控序列规则,目标是识别人类疾病中的遗传变异。我们还描述了目前用于绘制基因调控网络以描述生物过程的方法。我们指出了当前知识上的空白以及计算方法的技术局限性和基准测试挑战。最后,我们讨论了新出现的技术,如空间转录组学,并概述了创建一个更通用的顺式调控密码模型的策略,该模型在更广泛的细胞类型和个体中具有更广泛的适用性。