Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-37195-0_31
Title: Inference of spatial organizations of chromosomes using semi-definite embedding approach and Hi-C data
Authors: Zhang, Z.
Li, G. 
Toh, K.-C. 
Sung, W.-K. 
Keywords: 3D genome
Chromatin Interaction
Hi-C
Semi-definite Programming
Issue Date: 2013
Source: Zhang, Z.,Li, G.,Toh, K.-C.,Sung, W.-K. (2013). Inference of spatial organizations of chromosomes using semi-definite embedding approach and Hi-C data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7821 LNBI : 317-332. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-37195-0_31
Abstract: For a long period of time, scientists studied genomes 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 3D chromsomal structure within the nucleus. With the available Hi-C data, our next challenge is to model the 3D chromosomal structure from the 3C-dervied data computationally. This paper 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. To the best of our knowledge, ChromSDE is the only method which 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 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/43253
ISBN: 9783642371943
ISSN: 03029743
DOI: 10.1007/978-3-642-37195-0_31
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