TY - GEN
T1 - Network-based difierential analysis of Hi-C data
AU - Liu, Lu
AU - Ruan, Jianhua
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Chromatin conformation capture with high-throughput sequencing (Hi-C) is a powerful technique to detect genome-wide chromatin interactions. While many computational methods have been developed to analyze one HiC sample at a time for interpretable patterns, relatively few methods are available to compare HiC data across different experiments. In this paper we introduce a novel approach to detect differentially interacting genomic regions between different HiC samples using a network model. To make data from different experiments comparable, we propose a normalization strategy based on network topological properties. We devise two measurements, using local and global connectivity information from the HiC interaction network, respectively, to assess the interaction difference between two samples for each genomic locus. When multiple replicates are present for each sample, our approach provides the exibility for users to either pool all replicates together to therefore increase the network coverage, or to use the replicates in parallel to increase the signal to noise ratio. We show that while the local method works better in detecting changes from simulated networks, the global approach performs better on real HiC in detecting genomic regions. Furthermore, the global and local methods, regardless of pooling, are always superior to two existing methods.
AB - Chromatin conformation capture with high-throughput sequencing (Hi-C) is a powerful technique to detect genome-wide chromatin interactions. While many computational methods have been developed to analyze one HiC sample at a time for interpretable patterns, relatively few methods are available to compare HiC data across different experiments. In this paper we introduce a novel approach to detect differentially interacting genomic regions between different HiC samples using a network model. To make data from different experiments comparable, we propose a normalization strategy based on network topological properties. We devise two measurements, using local and global connectivity information from the HiC interaction network, respectively, to assess the interaction difference between two samples for each genomic locus. When multiple replicates are present for each sample, our approach provides the exibility for users to either pool all replicates together to therefore increase the network coverage, or to use the replicates in parallel to increase the signal to noise ratio. We show that while the local method works better in detecting changes from simulated networks, the global approach performs better on real HiC in detecting genomic regions. Furthermore, the global and local methods, regardless of pooling, are always superior to two existing methods.
KW - Differential analysis
KW - HiC data
KW - Networks
UR - http://www.scopus.com/inward/record.url?scp=85016554898&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016554898&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85016554898
T3 - Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017
SP - 165
EP - 172
BT - Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017
A2 - Al-Mubaid, Hisham
A2 - Eulenstein, Oliver
A2 - Ding, Qin
PB - The International Society for Computers and Their Applications (ISCA)
T2 - 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017
Y2 - 20 March 2017 through 22 March 2017
ER -