All Title Author
Keywords Abstract

Reconstruction of Single-Cell Chromosome

DOI: 10.12677/HJCB.2019.91001, PP. 1-7

Keywords: Hi-C数据,低秩矩阵,低秩矩阵的完备化,最短距离法
Hi-C Data
, Low-Rank Matrix, Completion of Low-Rank Matrices, Shortest Distance Method

Full-Text   Cite this paper   Add to My Lib


Genomics is one of the core areas of bioinformatics; there are two main research directions of genomics, structural genomics targeting whole genome sequencing and functional genomics targeting gene function interpretation. In the past few decades, genomics has experienced considerable development. The prediction of the three-dimensional structure of chromosomes is of great significance for the study of genomics. The reconstruction of the three-dimensional structure of chromosomes is to predict the conformation of the three-dimensional image from the one-dimensional and two-dimensional data of the genome, and then use the data analysis method to judge the reliability of the three-dimensional structure of the reconstructed chromosome. This paper is based on the single-cell chromosome Hi-C technology and Hi-3C derived data to capture the interaction data of individual cells, write the contact frequency matrix, and then convert the contact frequency matrix into a distance matrix to further obtain the three-dimensional structure of the chromosome. The contact matrix of single-cell Hi-C data is sparse and noise-containing, missing many non-contact sites. We refer to such a matrix as a low-rank matrix. The first problem we have to solve is the processing of low rank matrices, also called the completion of low rank distance matrices. This paper introduces several common low-rank matrix completion methods including optimization method and shortest distance method. It also introduces the different methods used in this paper. Finally, the final conclusion is obtained through MATLAB and compared of human research results.


[1]  彭城, 李国亮, 张红雨, 等. 染色质三维结构重建及其生物学意义[J]. 中国科学: 生命科学, 2014, 44(8): 794-802.
[2]  张卫. 基于Hi-C数据的预测染色体三维结构的方法研究[D]: [硕士学位论文]. 北京: 北京工业大学, 2016.
[3]  Paulsen, J., Gramstad, O. and Collas, P. (2015) Mainifold Based Optimization for Single-Cell 3D Ge-nome Reconstruction. PLoS Computational Biology, 11, e1004396.
[4]  李建更, 张卫, 李晓丹. 基于参数优化的染色体三维结构预测算法VMBO [J]. 北京工业大学学报, 2018, 44(2): 207-214.
[5]  Mishra, B., Meyer, G. and Sepulchre, R. (2011) Low-Rank Optimization for Distance Matrix Completion. 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, 12-15 December 2011, 4455-4460.
[6]  Mishra, B. (2014) A Riemannian Geometry for Low-Rank Matrix Completion.
[7]  项荣武, 刘艳杰, 胡忠盛. 图论中最短路径问题的解法[J]. 沈阳航空工业学院学报, 21(2): 86-88.
[8]  Hirata, Y., Oda, A., Ohta, K. and Aihara, K. (2016) Three-Dimensional Reconstruction of Single-Cell Chromosome Structure Using Recurrence Plots. Scientific Reports, 6, Article No. 34982.
[9]  Hirata, Y., Horai, S. and Aihara, K. (2008) Reproduction of Distance Ma-trices and Original Time Series from Recurrence Plot and Their Applications. The European Physical Journal Special Topics, 164, 13-22.
[10]  Tanio, M., Hirata, Y. and Suzuki, H. (2009) Reconstruction of Driving Forces through Recurrence Plots. Physics Letters A, 373, 2031-2040.
[11]  Varoquaux, N., Ay, F., Noble, W.S. and Vert, J.P. (2014) A Statistical Approach for Inferring the 3D Structure of the Genome. Bioinformatics, 30, i26-i33.
[12]  Ben-Elazar, S., Yakhini, Z. and Yanai, I. (2013) Spatial Lo-calization of Co-Regulated Genes Exceeds Genomic Gene Clustering in the Saccharomyces cerevisiae Genome. Nucleic Acids Research, 41, 2191-2201.


comments powered by Disqus

Contact Us


微信:OALib Journal