Please use this identifier to cite or link to this item: https://doi.org/10.1089/cmb.2013.0076
Title: 3D chromosome modeling with semi-definite programming and Hi-C data
Authors: Zhang, Z.
Li, G.
Toh, K.-C. 
Sung, W.-K. 
Keywords: chromatin interaction
Hi-C
semi-definite programming
three-dimensional genome
Issue Date: 1-Nov-2013
Citation: Zhang, Z., Li, G., Toh, K.-C., Sung, W.-K. (2013-11-01). 3D chromosome modeling with semi-definite programming and Hi-C data. Journal of Computational Biology 20 (11) : 831-846. ScholarBank@NUS Repository. https://doi.org/10.1089/cmb.2013.0076
Abstract: For a long period of time, scientists studied genomes while assuming they are linear. Recently, chromosome conformation capture (3C)-based technologies, such as Hi-C, have been developed that provide the loci contact frequencies among loci pairs in a genome-wide scale. The technology unveiled that two far-apart loci can interact in the tested genome. It indicated that the tested genome forms a three-dimensional (3D) chromosomal structure within the nucleus. With the available Hi-C data, our next challenge is to model the 3D chromosomal structure from the 3C-derived data computationally. This article presents a deterministic method called ChromSDE, which applies semi-definite programming techniques to find the best structure fitting the observed data and uses golden section search to find the correct parameter for converting the contact frequency to spatial distance. Further, we develop a measure called consensus index to indicate if the Hi-C data corresponds to a single structure or a mixture of structures. To the best of our knowledge, ChromSDE is the only method that can guarantee recovering the correct structure in the noise-free case. In addition, we prove that the parameter of conversion from contact frequency to spatial distance will change under different resolutions theoretically and empirically. Using simulation data and real Hi-C data, we showed that ChromSDE is much more accurate and robust than existing methods. Finally, we demonstrated that interesting biological findings can be uncovered from our predicted 3D structure. © 2013 Mary Ann Liebert, Inc.
Source Title: Journal of Computational Biology
URI: http://scholarbank.nus.edu.sg/handle/10635/113916
ISSN: 10665277
DOI: 10.1089/cmb.2013.0076
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