An integrated model for detecting significant chromatin interactions from high-resolution Hi-C data
Genome-wide chromosome conformation capture has helped us identify features of genome topology influencing biology but requires careful statistical analysis. Here the authors present HiC-DC, a bioinformatics method that can detect statistically significant regulatory interactions.
Guardado en:
Autores principales: | Mark Carty, Lee Zamparo, Merve Sahin, Alvaro González, Raphael Pelossof, Olivier Elemento, Christina S. Leslie |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2017
|
Materias: | |
Acceso en línea: | https://doaj.org/article/5d47fe47c7bf4ef0ad411c538d7ab7a4 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution
por: Yannick G. Spill, et al.
Publicado: (2019) -
HiC-DC+ enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP
por: Merve Sahin, et al.
Publicado: (2021) -
Identification of significant chromatin contacts from HiChIP data by FitHiChIP
por: Sourya Bhattacharyya, et al.
Publicado: (2019) -
Revealing Hi-C subcompartments by imputing inter-chromosomal chromatin interactions
por: Kyle Xiong, et al.
Publicado: (2019) -
Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data
por: Haitham Ashoor, et al.
Publicado: (2020)