title: GeSICA: genome segmentation from intra-chromosomal associations.
abstract: BACKGROUND
  Various aspects of genome organization have been explored based on data from distinct technologies, including histone modification ChIP-Seq, 3C, and its derivatives. Recently developed Hi-C techniques enable the genome wide mapping of DNA interactomes, thereby providing the opportunity to study genome organization in detail, but these methods also pose challenges in methodology development.
  
  
  RESULTS
  We developed Genome Segmentation from Intra Chromosomal Associations, or GeSICA, to explore genome organization and applied the method to Hi-C data in human GM06990 and K562 cells. GeSICA calculates a simple logged ratio to efficiently segment the human genome into regions with two distinct states that correspond to rich and poor functional element states. Inside the rich regions, Markov Clustering was subsequently applied to segregate the regions into more detailed clusters. The binding sites of the insulator, cohesion, and transcription complexes are enriched in the boundaries between neighboring clusters, indicating that inferred clusters may have fine organizational features.
  
  
  CONCLUSIONS
  Our study presents a novel analysis method, known as GeSICA, which gives insight into genome organization based on Hi-C data. GeSICA is open source and freely available at: http://web.tongji.edu.cn/~zhanglab/GeSICA/
authors.affiliation: Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai 200092, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Liu
authors.forename: Yiqian
authors.initials: Y
authors.lastname: Zhang
authors.forename: Jianxing
authors.initials: J
authors.lastname: Feng
authors.forename: Ning
authors.initials: N
authors.lastname: Zheng
authors.forename: Junfeng
authors.initials: J
authors.lastname: Yin
authors.forename: Yong
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D001665:Binding Sites / Q000235:genetics; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D015894:Genome, Human / Q000235:genetics*; D006801:Humans