Interpretable deep learning reveals the sequence rules of Hippo signaling (ATAC-Seq)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1060689 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:How specific cells respond to signaling pathways is largely encoded in the DNA sequence. However, the sequence rules result from complex interactions between signaling and cell type-specific transcription factors and are considered intractable by traditional methods. Here, we leverage interpretable deep learning on high-resolution data and extensive validation experiments to identify the sequence rules for the Hippo pathway in mouse trophoblast stem cells. We show that Tead4 and Yap1 engage in two types of cooperativity. First, their binding is enhanced by cell type-specific transcription factors, including Tfap2c, in a distance-dependent manner. Second, a strictly-spaced Tead double motif is a canonical Hippo pathway element that mediates strong Tead4 cooperativity through transient protein-protein interactions on DNA. These mechanisms occur genome-wide and allow us to predict how small sequence changes alter the activity of enhancers in vivo. This illustrates the power of interpretable deep learning to decode canonical and cell type-specific sequence rules of signaling pathways. These super series include ChIP-nexus data for TF binding (Tead4, Yap1, Cdx2, Tfap2c, Gata3), H3K27ac Chip-seq, ATAC-seq, RNA-seq, and Nascent RNA captured through the TT-seq method in mouse trophoblast stem cells.
2025-01-31 | GSE252463 | GEO
Project description:How specific cells respond to signaling pathways is largely encoded in the DNA sequence. However, the sequence rules result from complex interactions between signaling and cell-type-specific transcription factors and are considered intractable by traditional methods. Here, we leverage interpretable deep learning on high-resolution data and extensive validation experiments to identify the sequence rules for the Hippo pathway in mouse trophoblast stem cells. We show that Tead4 and Yap1 engage in two types of cooperativity. First, their binding is enhanced by cell-type-specific transcription factors, including Tfap2c, in a distance-dependent manner. Second, a strictly-spaced Tead double motif is a canonical Hippo pathway element that mediates strong Tead4 cooperativity through transient protein-protein interactions on DNA. These mechanisms occur genome-wide and allow us to predict how small sequence changes alter the activity of enhancers in vivo. This illustrates the power of interpretable deep learning to decode canonical and cell type-specific sequence rules of signaling pathways.
2025-01-31 | GSE252457 | GEO
Project description:How specific cells respond to signaling pathways is largely encoded in the DNA sequence. However, the sequence rules result from complex interactions between signaling and cell-type-specific transcription factors and are considered intractable by traditional methods. Here, we leverage interpretable deep learning on high-resolution data and extensive validation experiments to identify the sequence rules for the Hippo pathway in mouse trophoblast stem cells. We show that Tead4 and Yap1 engage in two types of cooperativity. First, their binding is enhanced by cell-type-specific transcription factors, including Tfap2c, in a distance-dependent manner. Second, a strictly-spaced Tead double motif is a canonical Hippo pathway element that mediates strong Tead4 cooperativity through transient protein-protein interactions on DNA. These mechanisms occur genome-wide and allow us to predict how small sequence changes alter the activity of enhancers in vivo. This illustrates the power of interpretable deep learning to decode canonical and cell type-specific sequence rules of signaling pathways.
2025-01-31 | GSE252462 | GEO
Project description:How specific cells respond to signaling pathways is largely encoded in the DNA sequence. However, the sequence rules result from complex interactions between signaling and cell-type-specific transcription factors and are considered intractable by traditional methods. Here, we leverage interpretable deep learning on high-resolution data and extensive validation experiments to identify the sequence rules for the Hippo pathway in mouse trophoblast stem cells. We show that Tead4 and Yap1 engage in two types of cooperativity. First, their binding is enhanced by cell-type-specific transcription factors, including Tfap2c, in a distance-dependent manner. Second, a strictly-spaced Tead double motif is a canonical Hippo pathway element that mediates strong Tead4 cooperativity through transient protein-protein interactions on DNA. These mechanisms occur genome-wide and allow us to predict how small sequence changes alter the activity of enhancers in vivo. This illustrates the power of interpretable deep learning to decode canonical and cell type-specific sequence rules of signaling pathways.
2025-01-31 | GSE252456 | GEO
Project description:Interpretable deep learning reveals the sequence rules of Hippo signaling
| PRJNA1060687 | ENA
Project description:Interpretable deep learning reveals the sequence rules of Hippo signaling (RNA-Seq)
| PRJNA1060693 | ENA
Project description:Interpretable deep learning reveals the sequence rules of Hippo signaling (ChIP-Seq)
| PRJNA1060690 | ENA
Project description:High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed a multi-omics approach for uncovering MoAs through an interpretable machine learning model of the effects of compounds on transcriptomic, epigenomic, metabolomic, and proteomic data. We applied this approach to examine compounds with beneficial effects in models of Huntington’s disease, finding common MoAs for previously unrelated compounds that were not predicted based on similarities in the compounds’ structures, connectivity scores, or binding targets. We experimentally validated two such disease-relevant MoAs, autophagy activation and bioenergetics manipulation. This interpretable machine learning approach can be used to find and evaluate MoAs in future drug development efforts.
2020-01-22 | GSE129143 | GEO
Project description:High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed a multi-omics approach for uncovering MoAs through an interpretable machine learning model of the effects of compounds on transcriptomic, epigenomic, metabolomic, and proteomic data. We applied this approach to examine compounds with beneficial effects in models of Huntington’s disease, finding common MoAs for previously unrelated compounds that were not predicted based on similarities in the compounds’ structures, connectivity scores, or binding targets. We experimentally validated two such disease-relevant MoAs, autophagy activation and bioenergetics manipulation. This interpretable machine learning approach can be used to find and evaluate MoAs in future drug development efforts.
2020-01-22 | GSE129141 | GEO
Project description:Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning
| PRJNA956958 | ENA