基于UUATAC-seq和深度学习的脊椎动物调控序列景观建模，这一成果由浙江大学医学院郭国骥小组经过不懈努力而取得。相关论文于2025年7月8日发表在《细胞》杂志上。
为了解决这个问题，课题组开发了一种超通量，超灵敏的单核检测转座酶可达染色质主题测序（UUATAC-seq）协议，可以在1天的实验中构建一个物种的染色质可达性景观。使用UUATAC-seq，研究团队绘制了五个代表性脊椎动物物种的候选顺式调控元件（cCREs）。他们的分析显示，不同物种之间的基因组大小差异会影响cCREs的数量，但不会影响cCREs的大小。该团队引入了Nvwa顺式调控元件（NvwaCE），这是一个大型任务深度学习模型，旨在解释顺式调控语法，并直接从基因组序列中高精度地预测cCRE景观。NvwaCE证明了调节语法比核苷酸序列更保守，并且该语法将cCREs组织成不同的功能模块。
此外，NvwaCE准确预测了合成突变对谱系特异性cCRE功能的影响，与因果数量性状位点（QTL）和基因组编辑结果一致。总之，他们的研究为解码脊椎动物的调节语言提供了宝贵的依据。
据悉，脊椎动物基因组的调控序列仍然不完全清楚。
附：英文原文
Title: Modeling the vertebrate regulatory sequence landscape by UUATAC-seq and deep learning
Author: Xiaoping Han, Hanyu Wu, Xueyi Wang, Daiyuan Liu, Yuting Fu, Lei Yang, Renying Wang, Peijing Zhang, Jingjing Wang, Lifeng Ma, Jizhong Mao, Lina Zhou, Siqi Wang, Xinlian Zhang, Mengmeng Jiang, Xinru Wang, Guoxia Wen, Danmei Jia, Guoji Guo
Issue&Volume: 2025-07-08
Abstract: The regulatory sequences of vertebrate genomes remain incompletely understood. To address this, we developed an ultra-throughput, ultra-sensitive single-nucleus assay for transposase-accessible chromatin using sequencing (UUATAC-seq) protocol that enables the construction of chromatin accessibility landscapes for one species in a 1-day experiment. Using UUATAC-seq, we mapped candidate cis-regulatory elements (cCREs) across five representative vertebrate species. Our analysis revealed that genome size differences across species influence the number but not the size of cCREs. We introduced Nvwa cis-regulatory element (NvwaCE), a mega-task deep-learning model designed to interpret cis-regulatory grammar and predict cCRE landscapes directly from genomic sequences with high precision. NvwaCE demonstrated that regulatory grammar is more conserved than nucleotide sequences and that this grammar organizes cCREs into distinct functional modules. Moreover, NvwaCE accurately predicted the effects of synthetic mutations on lineage-specific cCRE function, aligning with causal quantitative trait loci (QTLs) and genome editing results. Together, our study provides a valuable resource for decoding the vertebrate regulatory language.
DOI: 10.1016/j.cell.2025.06.020
Source: https://www.cell.com/cell/abstract/S0092-8674(25)00686-5