Bioinformatics Core

C O R E C： 生物信息学核心（“核心C”）的目的是识别和表征基因和途径激活 神经胶质瘤形成或GBM稳态的细胞模式。核心C将使用DNA的常用方法 拷贝分析和基因表达分析，以识别和表征基因和途径激活 对胶质瘤发生或GBM稳态至关重要的模式，基于来自每个开发的模型系统的数据， 的项目。我们将与项目调查人员密切合作，a）为他们的项目提供分析支持。 研究和B）建议由我们的基因组数据分析指导的后续实验。 我们将应用常用的分析方法对AffyR 3' UTR和外显子进行预处理， 表达式数组数据，如分位数归一化和鲁棒多数组平均（RMA）;参数 用于鉴定差异表达基因的方法，例如微阵列显著性分析（SAM）， 和limma，以及如在R（http：//www.example.com）中实现的基因集富集分析（GSEA），和www.r-project.org Bioconductor（http：www.bioconductor.org）。为了分析DNA拷贝数数据，我们将使用 GIST1C2.0方法，如在Matlab中实现的（Mermel C等人，Genome Biology 2011），用于数据 标准化和鉴定基因组靶标。对于测序数据的分析，我们将利用快速测序。 短读段比对方法如BWA，以及工具如samtools的处理能力 (http：samtools.sourceforge.net）和基因组分析工具包 (http：//www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit）。之后分析 根据异种移植数据，我们将校正小鼠mRNA与正在进行的Affytron平台的交叉反应性。 通过将探针从每个基因芯片映射到mm 10。然后，我们将为每个人生成探测集 仅包括未显示与mmlO的显著比对的探针的基因。我们将预测来自 通过使用Ensembl将小鼠基因映射到人类基因，从项目2获得小鼠模型。 我们将利用我们对癌症基因组图谱数据的深入了解， 人类GBM数据集。我们的核心人员在分析基因组数据类型方面经验丰富， 在更大的研究背景下解释结果。此外，我们一直与 TCGA分析工作组，并已显示出合作和沟通的能力。这确保了 富有成效的研究环境，在其中本项目提案的不同目标可以有效地来 结果 相关性（参见说明）： 恶性神经胶质瘤现在被理解为由多种亚型组成，而不是单一疾病。这 核心将致力于阐明这些肿瘤亚型的区别特征，以确定 他们对治疗的起始和敏感或抵抗的基础。

C O R E C : The aim of the Bioinformatics Core ('Core C ) is to identify and characterize genes and pathway activation patterns cnjcial for gliomagenesis or GBM homeostasis. Core C will use commonly applied methods for DNA copy analysis and gene expression analysis to identify and characterize genes and pathway activation patterns crucial for gliomagenesis or GBM homeostasis, on data from the model systems developed by each of the Projects. We will work closely with the project investigators to a) provide analytical support to their research and b) suggest follow up experiments guided by our genomic data analysis. We will apply commonly used analytical methods for preprocessing of Affymetrix 3' UTR and exon expression array data, such as quantile normalization and Robust Multi-array Averaging (RMA); parametric methods for identifying differentially expressed genes such as Significance Analysis of Microarrays (SAM) and limma, and Gene Set Enrichment Analysis (GSEA) as implemented in R (http://www.r-project.org) and Bioconductor (http://www.bioconductor.org). For analyzing DNA copy number data, we will use the GIST1C2.0 approach as implemented in Matlab (Mermel C et al. Genome Biology 2011) for data normalization and identification of genomic targets. For analysis of sequencing data we will make use of fast short read alignment methods such BWA, and the processing abilities of tools such as samtools (http://samtools.sourceforge.net) and the Genome Analysis Toolkit (http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit). Prior to analysis of xenograft data, we will correct for crossreactivity of mouse mRNA to the Affymetrix platforms that are being used, by mapping probes from each GeneChip to mm 10. We will then generate probe sets for each human gene only including probes that did not show significant alignment to mmlO. We will project findings from the mouse model from Project 2 through mapping of mouse genes to human genes using Ensembl. We will use our intimate knowledge of the data from The Cancer Genome Atlas to project our findings on human GBM data sets. Our core personnel are experienced in analysis of genomic data types and interpretation of the results in the context of larger studies. Moreover, we have been closely working with the TCGA analysis working groups and have shown the ability to collaborate and communicate. This ensures a productive research environment in which the different aims of this project proposal can effectively come to fruition. RELEVANCE (See instructions): Malignant gliomas are now understood to consist of a variety of subtypes rather than a single disease. This Core will sen/e to elucidate the distinguishing signatures of these tumor subtypes in an effort to determine the underiying basis of their initiation and sensitivity or resistance to therapy.