An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Single-cell long-read concatemer sequencing (scNanoHi-C) technology provides unique insights into the higher-order chromatin structure across the genome in individual cells, crucial for understanding 3D genome organization. However, the lack of specialized analytical tools for scNanoHi-C data impedes progress, as existing methods, which primarily focus on scHi-C technologies, do not fully address the specific challenges of scNanoHi-C, such as sparsity, cell-specific variability, and complex chromatin interaction networks. Here, we introduce DeepNanoHi-C, a novel deep learning framework specifically designed for scNanoHi-C data, which leverages a multi-step autoencoder and a Sparse Gated Mixture of Experts (MoE) to accurately predict chromatin interactions by imputing sparse contact maps, thereby capturing cell-specific structural features. DeepNanoHi-C effectively captures complex global chromatin contact patterns through the multi-step autoencoder and dynamically selects the most appropriate expert from a pool of experts based on distinct chromatin contact patterns. Furthermore, DeepNanoHi-C integrates multi-scale predictions through a dual-channel prediction net, refining complex interaction information and facilitating comprehensive downstream analyses of chromatin architecture. Experimental validation shows that DeepNanoHi-C outperforms existing methods in distinguishing cell types and demonstrates robust performance in data imputation tasks. Additionally, the framework identifies single-cell 3D genome features, such as cell-specific topologically associating domain (TAD) boundaries, further confirming its ability to accurately model chromatin interactions. Beyond single-cell analysis, DeepNanoHi-C also uncovers conserved genomic structures across species, providing insights into the evolutionary conservation of chromatin organization.

Single-cell long-read concatemer sequencing (scNanoHi-C) technology provides unique insights into the higher-order chromatin structure across the genome in individual cells, crucial for understanding 3D genome organization. However, the lack of specialized analytical tools for scNanoHi-C data impedes progress, as existing methods, which primarily focus on scHi-C technologies, do not fully address the specific challenges of scNanoHi-C, such as sparsity, cell-specific variability, and complex chromatin interaction networks. Here, we introduce DeepNanoHi-C, a novel deep learning framework specifically designed for scNanoHi-C data, which leverages a multi-step autoencoder and a Sparse Gated Mixture of Experts (MoE) to accurately predict chromatin interactions by imputing sparse contact maps, thereby capturing cell-specific structural features. DeepNanoHi-C effectively captures complex global chromatin contact patterns through the multi-step autoencoder and dynamically selects the most appropriate expert from a pool of experts based on distinct chromatin contact patterns. Furthermore, DeepNanoHi-C integrates multi-scale predictions through a dual-channel prediction net, refining complex interaction information and facilitating comprehensive downstream analyses of chromatin architecture. Experimental validation shows that DeepNanoHi-C outperforms existing methods in distinguishing cell types and demonstrates robust performance in data imputation tasks. Additionally, the framework identifies single-cell 3D genome features, such as cell-specific topologically associating domain (TAD) boundaries, further confirming its ability to accurately model chromatin interactions. Beyond single-cell analysis, DeepNanoHi-C also uncovers conserved genomic structures across species, providing insights into the evolutionary conservation of chromatin organization.

Single-cell long-read concatemer sequencing (scNanoHi-C) technology provides unique insights into the higher-order chromatin structure across the genome in individual cells, crucial for understanding 3D genome organization. However, the lack of specialized analytical tools for scNanoHi-C data impedes progress, as existing methods, which primarily focus on scHi-C technologies, do not fully address the specific challenges of scNanoHi-C, such as sparsity, cell-specific variability, and complex chromatin interaction networks. Here, we introduce DeepNanoHi-C, a novel deep learning framework specifically designed for scNanoHi-C data, which leverages a multi-step autoencoder and a Sparse Gated Mixture of Experts (MoE) to accurately predict chromatin interactions by imputing sparse contact maps, thereby capturing cell-specific structural features. DeepNanoHi-C effectively captures complex global chromatin contact patterns through the multi-step autoencoder and dynamically selects the most appropriate expert from a pool of experts based on distinct chromatin contact patterns. Furthermore, DeepNanoHi-C integrates multi-scale predictions through a dual-channel prediction net, refining complex interaction information and facilitating comprehensive downstream analyses of chromatin architecture. Experimental validation shows that DeepNanoHi-C outperforms existing methods in distinguishing cell types and demonstrates robust performance in data imputation tasks. Additionally, the framework identifies single-cell 3D genome features, such as cell-specific topologically associating domain (TAD) boundaries, further confirming its ability to accurately model chromatin interactions. Beyond single-cell analysis, DeepNanoHi-C also uncovers conserved genomic structures across species, providing insights into the evolutionary conservation of chromatin organization.