基因组组装是基因组数据分析的基础。结构变异预测和功能区域发现是基因组数据分析的核心，是从基因组结构分析走向功能分析的前提。本项目针对从一维到高维的基因组数据，围绕基因组组装、结构变异预测、功能区域发现等内容开展基因组数据分析基础理论与算法研究。寻找基因组数据质量控制方法，降低噪声对基因组数据分析结果的影响。根据基因组数据分析的计算需求，挖掘表达数据分析质量的特征参量。利用基因组数据特征参量建立组合优化问题模型，面向基因组数据特征，设计基因组数据分析组合优化问题的精确算法、参数算法和近似算法。取得一批在国内外有影响的基础理论成果。设计高效并行算法实现基因组数据分析计算需求。最后，开发具有自主知识产权的生物信息学软件，应用于肺癌等恶性肿瘤的基因组数据分析中。项目实施，在国际基因组数据分析基础理论与算法研究领域取得重大突破，为人类认识肿瘤疾病的发病机理，寻找诊断与治疗肿瘤疾病的手段做出贡献。

Genome assembly is the basis of genome data analysis. Structural variant prediction and functional area finding are crucial in genome data analysis, and the premise for going from genome structure analysis to genome function analysis. The research aims at one dimensional as well as three dimensional genome data, launches to develop foundational theory and algorithms for genome data analysis such as genome assembly, structural variant prediction, functional area finding, et al. In hope of excluding noises in all kinds of genome data, the research start with finding effective methods to control the genome data quality, and then go to mine the genome data characteristics based on the computational requirements of the concrete genome data analysis. Using the genome data characteristics, the research sets to capture combinational problems to account for the computational requirements for genome data analysis, then sets to design exact, parameterized, and approximation algorithms for those problems used to formalize the computational requirements, so that a number of foundational high quality theoretical results on genome data analysis will be achieved. Finally, a bioinformatics software with proprietary intellectual property rights will be developed, and used in cancer genome data analysis. Putting the project into effect, we aim to make breakthroughs in the area of genome data analysis theory and algorithms, and contribute to explore the principles of cancer disease, and find diagnosis and treatment methods of it.