Bioinformatics Core

摘要 生物信息学核心(核心B)将与每个项目、核心和UTSW联盟对接，以提供(I) 集中生物信息学和生物统计支助；(2)集中数据库；(3)综合数据分析 不同的平台；(4)编写手稿的分析性和有条不紊的报告。核心B领导，Dr。 Wang和共同领导者Baladandayuthapani博士已经密切合作并协同工作了几年 与核心和项目负责人及联合负责人共事多年，有能力支持研究设计、数据分析、 和整个PDTC节目的数据资源的管理。核心B将使用强大的IT结构和 广泛的计算环境，包括Windows、Unix/Linux、Mac OS X和两个四处理器Sun SPARC SMP系统。此外，核心B将依赖两个主要的机构计算资源，即HPC 包含336个计算节点的群集(每个节点具有32 GB RAM；每个核心1.3 GB RAM)，配备双12核皓龙 处理器(总计8,064个CPU)，以及具有32个CPU和128 GB RAM的安腾-2 SMP计算服务器。其核心是 Leader和Co-Leader为测序数据和蛋白质表达数据处理建立了各种管道 和分析，这将被应用于数据分析。标准设计原则和统计算法将是 将根据需要开发旧的和新的方法。其中包括：a)参数方法和非参数方法 用于估计和假设检验；b)用于寻找最佳模型的线性模型和广义加性模型 适合复杂数据结构；c)估计事件间隔时间结果分布的Kaplan-Meier方法； D)对数等级检验以比较不同PDX组之间的分布；以及e)比例风险模型 用单一药物和联合用药测试PDX的治疗情况。王博士和巴拉丹达尤萨尼博士将会工作 与UTPDTC调查人员密切合作，通过整合来自以下项目的数据集来促进跨项目的假设检验 多个实验室使用各种算法，包括主成分、偏最小二乘和 基于贝叶斯网络的模型。数据分析将使用R和BioConductor程序包进行。这个 CORE将对所有分析进行记录，并生成HTML或PDF报告(使用R包：Sweave、Coenet R和 R降价)。

Abstract The Bioinformatics Core (CORE B) will interface with each projects, cores, and UTSW consortium to provide (i) centralized bioinformatics and biostatistical support; (ii) centralized database; (iii) integrative data analysis across different platforms; and (iv) analytical and methodical reports for preparation of manuscripts. Core B Leader, Dr. Wang, and Co-Leader, Dr. Baladandayuthapani, have been working closely and synergistically for several years with core and projects leaders and co-leaders, and are capable of supporting study designs, data analysis, and management of data resources of the entire PDTC program. Core B will use robust IT structure and an extensive computing environment that includes Windows, Unix/Linux, Mac OS X, and two quad-processor Sun SPARC SMP systems. In addition, Core B will rely on two primary institutional computing resources, an HPC cluster of 336 compute nodes (each node with 32GB RAM; 1.3 GB RAM per core) with dual, 12-core Opteron processors (8,064 CPUs total), and an Itanium-2 SMP compute server with 32CPUs and 128GB RAM. The core Leader and Co-Leader have built various pipelines for sequencing data and protein expression data processing and analysis, which will be applied to analyze data. Standard design principles and statistical algorithms will be used and new methods will be developed as needed. These include: a) Parametric and nonparametric methods for estimation and hypothesis testing; b) Linear models and generalized additive models to find the best models that fit complex data structures; c) Kaplan-Meier method to estimate the distributions of time-to-event outcomes; d) Log-rank test to compare the distributions among different PDX groups; and e) Proportional hazards models to test for PDXs treatment with single drugs and combinations. Drs. Wang and Baladandayuthapani will work closely with UTPDTC investigators to facilitate hypothesis testing across projects by integrating datasets from multiple laboratories using various algorithms, including principal components, partial least squares, and Bayesian network-based models. Data analyses will be performed using R and Bioconductor packages. The Core will document all the analyses and produce HTML or PDF reports (using R packages: Sweave, knitR, and R markdown).