In silico prediction of high-resolution Hi-C interaction matrices

Existing computational approaches to predict long-range regulatory interactions do not fully exploit high-resolution Hi-C datasets. Here the authors present a Random Forests regression-based approach to predict high-resolution Hi-C counts using one-dimensional regulatory genomic signals.

Guardado en:
Detalles Bibliográficos
Autores principales: Shilu Zhang, Deborah Chasman, Sara Knaack, Sushmita Roy
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
Materias:
Q
Acceso en línea:https://doaj.org/article/371269a5f21c4aab82d477791ec36389
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:Existing computational approaches to predict long-range regulatory interactions do not fully exploit high-resolution Hi-C datasets. Here the authors present a Random Forests regression-based approach to predict high-resolution Hi-C counts using one-dimensional regulatory genomic signals.