A novel method for discovering local spatial clusters of genomic regions with functional relationships from DNA contact maps.
The three-dimensional structure of genomes makes it possible for genomic regions not adjacent in the primary sequence to be spatially proximal. These DNA contacts have been found to be related to various molecular activities. Previous methods for analyzing DNA contact maps obtained from Hi-C experiments have largely focused on studying individual interactions, forming spatial clusters composed of contiguous blocks of genomic locations, or classifying these clusters into general categories based on some global properties of the contact maps.



Here, we describe a novel computational method that can flexibly identify small clusters of spatially proximal genomic regions based on their local contact patterns. Using simulated data that highly resemble Hi-C data obtained from real genome structures, we demonstrate that our method identifies spatial clusters that are more compact than methods previously used for clustering genomic regions based on DNA contact maps. The clusters identified by our method enable us to confirm functionally related genomic regions previously reported to be spatially proximal in different species. We further show that each genomic region can be assigned a numeric affinity value that indicates its degree of participation in each local cluster, and these affinity values correlate quantitatively with DNase I hypersensitivity, gene expression, super enhancer activities and replication timing in a cell type specific manner. We also show that these cluster affinity values can precisely define boundaries of reported topologically associating domains, and further define local sub-domains within each domain.



The source code of BNMF and tutorials on how to use the software to extract local clusters from contact maps are available at http://yiplab.cse.cuhk.edu.hk/bnmf/



kevinyip@cse.cuhk.edu.hk



Supplementary data are available at Bioinformatics online.
Department of Computer Science and Engineering.
Xihao
X
Hu
Department of Computer Science and Engineering.
Christina Huan
CH
Shi
Department of Computer Science and Engineering Hong Kong Bioinformatics Centre CUHK-BGI Innovation Institute of Trans-omics Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
Kevin Y
KY
Yip
D004247:DNA; D016678:Genome; D023281:Genomics; D011381:Programming Languages; D012984:Software
D004247:DNA