Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus
Despite its popularity for measuring the spatial organization of mammalian genomes, the resolution of most Hi-C datasets is coarse due to sequencing cost. Here, Zhang et al. develop HiCPlus, a computational approach based on deep convolutional neural network, to infer high-resolution Hi-C interactio...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2018
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/5c053d03b3514bb6984fc7751445c963 |
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| Sumario: | Despite its popularity for measuring the spatial organization of mammalian genomes, the resolution of most Hi-C datasets is coarse due to sequencing cost. Here, Zhang et al. develop HiCPlus, a computational approach based on deep convolutional neural network, to infer high-resolution Hi-C interaction matrices from low-resolution Hi-C data. |
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