ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data

ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data

Abstract Background With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-C technique can generate genome-wide chrom...

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Bibliographic Details
Main Authors: Oluwatosin Oluwadare, Jianlin Cheng
Format: Article
Language:English
Published: BMC 2017-11-01
Series:BMC Bioinformatics
Subjects:
Clustering
Hi-C
Topologically associated domain (TAD)
CTCF
Chromosome conformation capturing
Genome structure
Online Access:http://link.springer.com/article/10.1186/s12859-017-1931-2

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http://link.springer.com/article/10.1186/s12859-017-1931-2

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