VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm for improving and generating Hi-C data
Abstract Chromatin conformation plays an important role in a variety of genomic processes. Hi-C is one of the most popular assays for inspecting chromatin conformation. However, the utility of Hi-C contact maps is bottlenecked by resolution. Here we present VEHiCLE, a deep learning algorithm for res...
Guardado en:
Autores principales: | Max Highsmith, Jianlin Cheng |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/ee31201f4b854106bdfeb0f063f00380 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus
por: Yan Zhang, et al.
Publicado: (2018) -
HiC-DC+ enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP
por: Merve Sahin, et al.
Publicado: (2021) -
HiCancer: accurate and complete cancer genome phasing with Hi-C reads
por: Weihua Pan, et al.
Publicado: (2021) -
qc3C: Reference-free quality control for Hi-C sequencing data.
por: Matthew Z DeMaere, et al.
Publicado: (2021) -
Integrating Hi-C and FISH data for modeling of the 3D organization of chromosomes
por: Ahmed Abbas, et al.
Publicado: (2019)