Zhang Zhenhao, Bao Xinyu, Jiang Linghua, Luo Xin, Wang Yichun, Comai Annelise, Waldhaus Joerg, Hansen Anders S, Li Wenbo, Liu Jie
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, USA.
bioRxiv. 2025 May 14:2025.05.08.652986. doi: 10.1101/2025.05.08.652986.
Advances in next-generation sequencing technologies have vastly expanded the availability of diverse genomic, epigenomic and transcriptomic data, presenting the opportunity to develop a general AI model that integrates comprehensive genomic knowledge into a unified model. Unlike previous predictive models, which are typically specialized to certain tasks, our general AI model unifies a wide range of genomic modalities, such as nascent RNA and ultra-high-resolution chromatin organization, within a multi-task architecture. Using ATAC-seq and DNA sequences as inputs, we incorporated diverse genomic modalities as output, and the model exhibits strong generalizability across different cell types and tissues in all tasks we trained. It accurately predicts gene-level transcription measured by various nascent RNA assays, and effectively captures enhancer-associated transcription. Additionally, it also accurately captures the potential functions of non-coding genetic variants and regulatory elements. Additionally, we extended the model trained on human data to a mouse general model, achieving accurate predictions of genomic modalities, such as high resolution chromatin contact maps with limited data availability, which are further validated using an established mouse inner-ear study. This comprehensive approach offers a powerful tool for understanding genome regulation in both human and mouse species.
新一代测序技术的进步极大地扩展了各种基因组、表观基因组和转录组数据的可得性，为开发一种将全面的基因组知识整合到统一模型中的通用人工智能模型提供了契机。与以往通常专门用于特定任务的预测模型不同，我们的通用人工智能模型在多任务架构中统一了广泛的基因组模式，如新生RNA和超高分辨率染色质组织。以ATAC序列和DNA序列作为输入，我们将各种基因组模式作为输出纳入其中，并且该模型在我们训练的所有任务中，在不同细胞类型和组织中均表现出很强的通用性。它能准确预测通过各种新生RNA检测方法测得的基因水平转录，并有效捕捉增强子相关转录。此外，它还能准确捕捉非编码基因变异和调控元件的潜在功能。此外，我们将基于人类数据训练的模型扩展为小鼠通用模型，在数据可用性有限的情况下，实现了对基因组模式的准确预测，如高分辨率染色质接触图谱，这在一项既定的小鼠内耳研究中得到了进一步验证。这种综合方法为理解人类和小鼠物种的基因组调控提供了一个强大的工具。
