Bioinformatics Core

C O R E C： 生物信息学核心(‘Core C)的目标是识别和表征基因和途径激活 胶质瘤形成或基底膜动态平衡的模式。核心C将使用DNA的常用方法 复制分析和基因表达分析以识别和表征基因和通路激活 对胶质瘤发生或GBM动态平衡至关重要的模式，基于各自开发的模型系统的数据 所有的项目。我们将与项目调查人员密切合作，a)为他们的 研究和b)建议后续实验以我们的基因组数据分析为指导。 我们将应用常用的分析方法对Affymetrix 3‘非编码区和外显子进行前处理 表达式数组数据，如分位数归一化和稳健多数组平均(RMA)；参数 微阵列显著性分析(SAM)等鉴定差异表达基因的方法 和LIMMA，以及在R(http://www.r-project.org)和 生物导体(http://www.bioconductor.org).)为了分析DNA拷贝数数据，我们将使用 在Matlab中实现的GIST1C2.0方法(Mermel C等人基因组生物学2011)获取数据 基因组靶标的标准化和鉴定。对于测序数据的分析，我们将使用FAST 短读对齐方法，如BWA，以及SamTools等工具的处理能力 (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).在分析之前 异种移植数据，我们将纠正小鼠mRNA与Affymetrix平台的交叉反应 通过将每个基因芯片的探针映射到mm 10来使用。然后，我们将为每个人类生成探针集 仅包括未显示与mMLO有显著比对的探针。我们将从 通过使用EnSembl将小鼠基因映射到人类基因来建立项目2中的小鼠模型。 我们将使用我们对来自癌症基因组图谱的数据的深入了解来预测我们的发现 人类GBM数据集。我们的核心人员在分析基因组数据类型和 在更大的研究背景下对结果进行解释。此外，我们一直与 TCGA分析工作组，并表现出协作和沟通的能力。这确保了 富有成效的研究环境，可以有效地实现本项目提案的不同目标 果子。 相关性(请参阅说明)： 恶性胶质瘤现在被理解为由多种亚型组成，而不是单一的疾病。这 CORE将努力阐明这些肿瘤亚型的区别特征，以努力确定 他们发起治疗和对治疗敏感或抵抗的基础。

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.