浙江大学最新Cell论文：AI基因组模型——女娲CE，破译脊椎动物基因组调控语言

Core Viewpoint - The research highlights the development of a high-throughput and ultra-sensitive single-nucleus ATAC sequencing technology (UUATAC-seq) and a deep learning model (NvwaCE) for predicting regulatory sequences in vertebrates, providing valuable resources for understanding the regulatory language of vertebrate genomes [5][15]. Group 1: Technology Development - The UUATAC-seq technology allows for the efficient construction of chromatin accessibility maps within a single day for a given species [8]. - The NvwaCE model is designed to interpret the "grammar" of cis-regulatory elements (cCRE) and can directly predict cCRE landscapes from genomic sequences with high accuracy [11]. Group 2: Research Findings - The study found that the conservation of regulatory grammar is significantly stronger than that of nucleotide sequences, revealing the sequence basis for cell-type-specific gene expression [6]. - The analysis indicated that differences in genome size among species affect the number of cCREs but not their size [10]. Group 3: Practical Applications - The NvwaCE model accurately predicts the impact of synthetic mutations on lineage-specific cCRE functionality, aligning with quantitative trait loci (QTL) and genome editing results [13]. - A specific gene mutation site (HBG1-68:A>G) was predicted to have curative potential for sickle cell disease, marking the first instance of an AI-designed functional site being validated in human cells [14].