近年来，高通量测序产生了海量的组学数据，用于研究癌症的发生和发展机理。癌症基因组的突变位点研究是其中一个热点和难点，传统的研究方法主要是基于DNA一维序列，而忽略了DNA三维结构的贡献。本项目研发计算方法整合DNA三维结构和多组学数据到癌症基因组的突变位点研究。首先，基于隐变量分解模型，研究多种DNA三维结构性质数据的去噪方法。然后，基于蚁群算法的网络聚类，提出识别癌症DNA三维结构变异亚型的手段。最后，整合DNA三维结构和多组学数据，基于贝叶斯网络识别癌症发生的关键突变位点，并通过小鼠模型进行生物实验验证，探索癌症的生物靶点。本项目成果将有助于探索癌症的发生机理，为精准诊断和治疗提供理论支持。

In the last few years, huge amounts of high-throughput sequencing multi-omics data have　been generated to investigate the mechanism underlying occurrence and developpment of cancers. Study of mutation sites in cancer genomes is a hot and difficult problem. Conventional methods are mainly based on primary DNA sequence, and ignore the contribution of DNA three-dimensional structure. In this project, we integrate various types of DNA three-dimensional structures and high-throughput multi-omics data into the study of cancer mutation sites by developing effective computational methods. Firstly, based on hidden variable decomposition model, we will study the reduction of noise in various types of DNA three-dimensional structures data. Secondly, based on ant colony algorithms in network clustering, we will develop new approaches for identifying subtypes of DNA three-dimensional structural mutation in cancers. Finally, by integrating DNA three-dimensional structures and high-throughput multi-omics data, we will identify critical mutation sites in cancers based on Bayes network. We will validate these sites using biological experiments based on mouse model to explore cancer markers. The outcome of this project will be helpful for exploiting the mechanism underlying occurrence of cancers, and provide further theoretical support for the precise diagnosis and treatment.