基于参数优化的染色体三维结构预测算法VMBO  

MBO-Based Method With Parameter Optimization to Predict 3D Chromatin Structure

作　　者：李建更[1,2] 张卫 李晓丹 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100124 [2]计算智能与智能系统北京市重点实验室,北京100124

出　　处：《北京工业大学学报》2018年第2期207-214,共8页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(61573029)

摘　　要：认识染色体的三维空间结构对于理解细胞核内基因组的表达、调控等具有重要作用.针对Hi-C数据稀疏和含有噪声的特点,提出了基于流形优化(manifold based optimization,MBO)与参数优化相结合的染色体三维结构预测方法——变参数的基于流形优化的算法(variable-parameter MBO,VMBO).通过黄金分割算法迭代优化转换参数,将染色体片段间的接触频率转换为空间距离值;然后用MBO算法重构染色体的三维平均结构(consensus structures).在实验部分用模拟数据集和真实的Hi-C数据集进行三维结构预测,预测结果的均方根误差(root mean squared deviation,RMSD)和距离的斯皮尔曼相关系数(distance Spearman correlation coefficient,d SCC)说明了VMBO算法的有效性和鲁棒性.Having known the 3D structures of chromosomos is of great importance to the understanding of gene expression and regulation in nuclei. Hi-C technology has been developed to capture genome-wide interactions and generate contact frequency data. Based on the characteristic of the sparse and noisy interaction sampling in Hi-C data, a MBO-based method with parameter optimization,named VMBO, was proposed to predict a 3D chromatin structure. First,for converting the interaction frequency to spatial distance between two chromosome fragments the conversion factor was optimized by golden section search. Second,manifold based optimization （MBO） was applied to reconstruct a consensus 3D structure. The VMBO accuracy and robustness were validated on both simulation data and real Hi-C data. The results of structure similarity measures, root mean squared deviation and distance Spearman correlation coefficient, indicate that the proposed method can well reconstruct 3D chromatin structures.

关 键 词：Hi-C接触频率 基于流形最优化(MBO) 染色体结构的预测 平均结构 变参数的基于流形优化的算法(VMBO) 

分 类 号：TP308[自动化与计算机技术—计算机系统结构]