Computational and Bioinformatics Analysis Core

计算和生物信息学分析核心(核心D)在咨询能力中的职能，以确定 试验的指导和设置，以及最重要的是，在服务能力方面执行详细的 三个计划项目和B、C核心产生的微阵列数据分析 CORE将与这些核心人员和项目1-3的调查人员密切合作，以提炼问题以 被邀请了。通常，对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，进而影响它们的造血系、神经系和生殖系分化 潜力。随着来自所有三个计划项目的高通量数据集可用，我们将集成 来自不同ES细胞系和三种分化途径的数据并利用组蛋白的表达 修饰和DMA甲基化数据与已公布的转录因子结合数据集相结合，以 开始了解i)细胞命运的改变如何反映在染色质结构的变化中；ii)如果我们 可以在不同的ES细胞系中检测染色质结构的细节，这些细胞系预测细胞类型 不同的ES细胞优先分化。核心得到了马泰奥·佩莱格里尼博士的大力支持，他是 实验室利用高通量测序仪和高密度微阵列产生的数据来模拟 监管网络。除了分析微阵列数据外，核心还将执行标准统计 对体内分化细胞功能研究获得的数据进行分析。这方面的一个例子 分析的类型是测试是否存在不同的重建频率和体外细胞功能 不同亲本ES细胞系体外分化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.