A novel method to identify topological domains using Hi-C data

• Published in: Quantitative biology 2015-08, Vol.3 (2), p.81-89
• Main Authors: Wang, Yang, Li, Yanjian, Gao, Juntao, Zhang, Michael Q.
• Format: Article
• Language: English
• Subjects: Bioinformatics; Biomedical and Life Sciences; chromatin domain; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; dynamic programming; Hi-C; Life Sciences; Mathematical and Computational Biology; Research Article
• ISSN: 2095-4689; 2095-4697
• DOI: 10.1007/s40484-015-0047-9

Over the last decade the 3C-based (Chromosome Conformation Capture, 3C) approaches have been developed to describe the frequency of chromatin interaction. The invention of Hi-C allows us to obtain genome-wide chromatin interaction map. However, it is challenging to develop efficient and robust analytical tools to interpret the Hi-C data. Here we present a new method called Clustering based Hi-C Domain Finder (CHDF), which is based on the difference of interaction intensity inside/outside domains, to identify Hi-C domains. We also compared CHDF with existing methods including Direction Index (DI) and HiCseg. CHDF can define more chromatin domains validated by higher resolution local chromatin structure data (Chromosome Conformation Capture Carbon Copy (5C) data). Using Hi-C data of lower sequencing depth, chromatin structure identified by CHDF is closer to that discovered by data of higher sequencing depth. Furthermore, the implement of CHDF is faster than the other two. Using CHDF, we are potentially able to discover more hints and clues about chromatin structural elements at domain level.