Microsoft Research AI4Science, Beijing, China.
School of Medicine, Tsinghua University, Beijing, China.
Genome Biol. 2023 Nov 23;24(1):266. doi: 10.1186/s13059-023-03103-8.
Linking cis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to model cis-regulatory patterns for genomic elements up to 2 Mb from target genes. Coupled with a training strategy that predicts gene expression from flanking candidate cis-regulatory elements (cCREs), CREaTor can model cell type-specific cis-regulatory patterns in new cell types without prior knowledge of cCRE-gene interactions or additional training. The zero-shot modeling capability, combined with the use of only RNA-seq and ChIP-seq data, allows for the ready generalization of CREaTor to a broad range of cell types.
将顺式调控序列与靶基因联系起来一直是一个长期存在的挑战。在这项研究中，我们引入了 CREaTor，这是一种基于注意力的深度神经网络，旨在为距离靶基因长达 2Mb 的基因组元件建模顺式调控模式。结合一种从侧翼候选顺式调控元件 (cCRE) 预测基因表达的训练策略，CREaTor 可以在没有关于 cCRE-基因相互作用或额外训练的先验知识的情况下，对新的细胞类型进行细胞类型特异性顺式调控模式建模。零镜头建模能力，再加上仅使用 RNA-seq 和 ChIP-seq 数据，使得 CREaTor 可以轻松推广到广泛的细胞类型。
