| Autor: |
Yongge Li, Fusong Ju, Zhiyuan Chen, Yiming Qu, Huanhuan Xia, Liang He, Lijun Wu, Jianwei Zhu, Bin Shao, Pan Deng |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Genome Biology, Vol 24, Iss 1, Pp 1-22 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1474-760X |
| DOI: |
10.1186/s13059-023-03103-8 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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