新一代测序技术比基因芯片技术更高通量和更精确， 而且也能够应用到相应的生物领域， 如DNA测序技术(ChIP-seq)， RNA 测序技术(RNA-seq) 和甲基化测序技术(MeDIP-seq)。 由于新数据与基因芯片数据有本质的区别，包括样本量少但数据量大和信号是离散的等特点. 因此原有的统计方法不再适用于新数据和需要提出新的统计方法来解决新的问题。本项目将研究：（1） 基于Medip-seq 和MRE-seq互补数据，本项目研究不同条件下重复样本的甲基化水平的差异性比较。（2）针对Medip-seq数据的甲基化水平的估计和变点的问题，本项目研究建立新的理论模型来推导甲基化水平，并且提出寻找甲基化水平的变化位置的检验统计量。本项目也将研究新提出方法的理论性质，包括估计的大样本性质，检验的收敛性等。

The next-generation sequencing technology is higher throughput and more accurate than gene chip technology. The next-generation sequencing technology also applied to the corresponding field of biology, such as DNA sequencing (ChIP-seq), RNA sequencing (RNA-seq) and methylated sequencing (MeDIP-seq). The new data are essentially different with gene chip data, including small sample size and large amount of a sample and data signals are discrete. Therefore, the old statistic methods can't apply to the new data and we need develop new statistic methods to solve the coming up problems. The project will study: (1) Based on Medip-seq and MRE-seq complementary data, the project study the difference of methylation level under the multi-Samples of different conditions. (2) Aim at the estimation and change point of the methylation level of Medip-seq, the project trying to build a novel model to deduce the methylation level of CpG, and to develop a test statistic to detect the change point of the methylation level. The project will consider the theoretical character of the methods.