A supervised learning framework for chromatin loop detection in genome-wide contact maps
Predicting chromatin loops from genome-wide interaction matrices such as Hi-C data provides insight into gene regulation events. Here, the authors present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps, and apply it to systematically pr...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/d358d96ed7504e06836b668c15d107b4 |
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| Sumario: | Predicting chromatin loops from genome-wide interaction matrices such as Hi-C data provides insight into gene regulation events. Here, the authors present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps, and apply it to systematically predict chromatin loops in 56 Hi-C datasets, with results available at the 3D Genome Browser. |
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