Caudai Claudia, Salerno Emanuele, Zoppe Monica, Merelli Ivan, Tonazzini Anna
IEEE/ACM Trans Comput Biol Bioinform. 2019 Nov-Dec;16(6):1867-1878. doi: 10.1109/TCBB.2018.2838669.
A method and a stand-alone Python code to estimate the 3D chromatin structure from chromosome conformation capture data are presented. The method is based on a multiresolution, modified-bead-chain chromatin model, evolved through quaternion operators in a Monte Carlo sampling. The solution space to be sampled is generated by a score function with a data-fit part and a constraint part where the available prior knowledge is implicitly coded. The final solution is a set of 3D configurations that are compatible with both the data and the prior knowledge. The iterative code, provided here as additional material, is equipped with a graphical user interface and stores its results in standard-format files for 3D visualization. We describe the mathematical-computational aspects of the method and explain the details of the code. Some experimental results are reported, with a demonstration of their fit to the data.
本文提出了一种从染色体构象捕获数据估计三维染色质结构的方法及独立的Python代码。该方法基于多分辨率、改进的珠链染色质模型,通过四元数算子在蒙特卡罗采样中演化。待采样的解空间由一个具有数据拟合部分和约束部分的评分函数生成,其中可用的先验知识被隐式编码。最终的解是一组与数据和先验知识都兼容的三维构型。作为补充材料提供的迭代代码配备了图形用户界面,并将其结果存储在标准格式文件中以进行三维可视化。我们描述了该方法的数学计算方面,并解释了代码的细节。报告了一些实验结果,并展示了它们与数据的拟合情况。