新一代测序技术比基因芯片技术更高通量和更精确，已经逐渐成为基因遗传和疾病分析的重要手段。对新一代测序数据统计分析的难点在于样本量少，但数据量十分巨大且观测信号离散。目前，对新型数据分析中出现的标准化，错误发现率控制及分类判别等难点问题几乎没有相应的统计解决方法。本项目将重点研究如下迄今还没有得到解决的问题：（1）不同物种的RNA-seq数据测序深度的标准化问题；（2）离散的多组p-value如何统一估计到q-value的问题；（3）RNA-seq数据中出现过多零的分类判别问题；（4）利用CpG信息来考虑MeDIP-seq数据的分类判别问题。为解决上述难题，本项目将利用离散分布等来拟合新数据，提出符合离散分布的模型并构建标准化和分类判别等问题的统计框架。进一步，本项目也将研究新的估计方法所得估计量的大样本性质，并分析最新的食管癌数据。上述问题的解决将为新数据分析提供可靠的统计方法和理论依据。

Next-generation sequencing techniques, which are higher throughput and more accurate than the Gene Chip technique, have gradually become an important means of genetics and diseases analysis. It is hard to analyze the new data because of small sample size, huge amount of a sample and discrete observation signals. So far, difficult problems appeared in the processing of next generation sequencing data analysis, such as normalization, false discovery rate controlling and classification, is almost no corresponding statistical methods. The project will mainly study the follow problems which remains a controversial issue until now, that is, (1) the statistical method of the sequencing depth normalization for different species; (2) how to estimate q-value with several different discrete p-value groups; (3) the study of classification for RNA-seq data with zero inflated; (4) considering the classification method of MeDIP-seq data with CpG information. To solve these problems, the project will consider mixed discrete distribution to fit new data, propose the discrete distribution model and structure statistical framework for normalization and classification problems. Furthermore, the projector will study the large sample theory of the estimations and apply it to analyze the newest real esophagus cancer data. The solution of these problems will provide reliable statistical methods and theoretical basis to analyze new data.