CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms.

Journal: Genome biology
PMID: 37996959

Abstract

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.

Authors

  • Yongge Li
    Microsoft Research AI4Science, Beijing, China.
  • Fusong Ju
    Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Zhiyuan Chen
    Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
  • Yiming Qu
    Microsoft Research AI4Science, Beijing, China.
  • Huanhuan Xia
    Microsoft Research AI4Science, Beijing, China.
  • Liang He
    Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China.
  • Lijun Wu
    Department of Rheumatism and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China.
  • Jianwei Zhu
    College of Food Science and Engineering, Jilin Agricultural University, Changchun 130118, China.
  • Bin Shao
    Microsoft Research Asia, Beijing, China.
  • Pan Deng

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