Computational and Bioinformatics Analysis Core

计算和生物信息学分析核心（核心 D）以咨询身份发挥作用，以确定 实验的方向和设置，最重要的是，执行详细的服务能力 分析三个计划项目以及核心 B 和 C 生成的微阵列数据。 核心将与这些核心和计划项目 1-3 的研究人员密切合作，以完善问题 被问到。一般来说，微阵列数据的分析将旨在阐明是否存在差异 染色质结构，即组蛋白修饰和 DMA 甲基化，存在于不同的人类之间 胚胎干细胞系，进而影响其造血、神经和生殖系分化 潜力。随着所有三个计划项目的高通量数据集变得可用，我们将整合 来自不同 ES 细胞系和三种分化途径的数据，并利用表达、组蛋白 修饰和 DMA 甲基化数据与已发表的转录因子结合数据集相结合 开始了解 i) 细胞命运的变化如何反映在染色质结构的变化上； ii) 如果我们 可以检测不同 ES 细胞系中染色质结构的细节，从而预测细胞类型 不同的ES细胞优先分化。该核心得到了 Matteo Pellegrini 博士的大力支持，他 实验室利用高通量测序仪和高密度微阵列生成的数据来建模 监管网络。除了分析微阵列数据外，核心还将执行标准统计 分析从分化细胞功能的体内研究中获得的数据。举一个例子来说明这一点 分析类型是测试是否存在不同的重构频率和离体细胞功能 体外从不同亲代 ES 细胞系分化而来的 T 细胞的反应。

The Computational and Bioinformatics Analysis Core (Core D) functions in an advisory capacity to determine the direction and set-up of experiments, and, most importantly, in a service capacity to perform detailed analyses of the microarray data generated by the three Program Projects and Cores B and C. The analysis core will closely work with these cores and investigators from Program Projects 1-3 to refine the questions to be asked. Generally, the analysis of microarray data will be directed at elucidating whether differences in chromatin structure, i.e. histone modifications and DMA methylation, exist between different human embryonic stem cell lines, which in turn influence their hematopoietic, neural, and germ line differentiation potential. As high-throughput datasets from all three Program Projects become available, we will integrate data from the different ES cell lines and the three differentiation pathways and utilize the expression, histone modification and DMA methylation data in combination with published transcription factor binding data sets to begin to understand i) how change cell fate are reflected in changes in chromatin structure; and ii) if there we can detect details in chromatin structure in the different ES cell lines that predict into which cell type the different ES cells preferentially differentiate. The Core is strongly supported by Dr. Matteo Pellegrini, who's laboratory utilizes the data generated by high throughput sequencers and high-density microarrays to model regulatory networks. In addition to analyzing microarray data, the core will perform standard statistical analysis of data obtained from in vivo studies on the functionality of differentiated cells. One example for this type of analysis is to test if there are different reconstitution frequencies and ex vivo cellular functional responses of T cells differentiated in vitro from different parental ES cell lines.