Computational and Bioinformatics Analysis Core

计算和生物信息学分析核心（核心D）以顾问身份发挥作用， 实验的方向和设置，最重要的是，在服务能力，以执行详细的 分析由三个计划项目和核心B和C产生的微阵列数据。分析 核心小组将与这些核心小组和计划项目1-3的调查人员密切合作，完善问题， 被问到。通常，微阵列数据的分析将针对阐明微阵列中的差异是否与微阵列中的差异有关。 染色质结构，即组蛋白修饰和DNA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 潜力随着来自所有三个计划项目的高通量数据集变得可用，我们将整合 数据从不同的ES细胞系和三个分化途径，并利用表达，组蛋白 修饰和DNA甲基化数据与公开的转录因子结合数据集组合， 开始理解i）细胞命运的变化如何反映在染色质结构的变化中;以及ii）如果我们 可以检测不同ES细胞系中染色质结构的细节，这些细胞系可以预测胚胎干细胞进入哪种细胞类型。 不同的ES细胞优先分化。核心得到了Matteo Pellegrini博士的大力支持， 实验室利用高通量测序仪和高密度微阵列产生的数据来建模 监管网络。除了分析微阵列数据外，核心还将执行标准的统计分析。 分析从分化细胞功能性的体内研究中获得的数据。举个例子 一种类型的分析是测试是否存在不同的重构频率和离体细胞功能 从不同亲本ES细胞系体外分化的T细胞的应答。

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.