Bioinformatics Core

摘要 生物信息学核心（CORE B）将与每个项目、核心和UTSW联合体对接，以提供（i） 集中的生物信息学和生物统计支持;（ii）集中的数据库;（iii）跨学科的综合数据分析 不同的平台;以及（iv）用于编写手稿的分析性和方法性报告。核心B负责人，Dr. Wang和联合领导人Baladandayuthapani博士一直在密切合作， 与核心和项目负责人和共同负责人合作多年，能够支持研究设计，数据分析， 以及整个PDTC计划的数据资源管理。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 SMP系统。此外，核心B将依赖于两个主要的机构计算资源，一个HPC 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），具有双核12核Opteron 处理器（共8，064个CPU）和一台配备32个CPU和128 GB RAM的Itanium-2 SMP计算服务器。核心 Leader和Co-Leader已经建立了各种管道，用于测序数据和蛋白质表达数据处理 和分析，将用于分析数据。标准设计原则和统计算法将 将根据需要开发新的和旧的方法。这些方法包括：a）参数和非参数方法 用于估计和假设检验; B）线性模型和广义加性模型，以找到最佳模型 c）Kaplan-Meier方法估计事件发生时间结局的分布; d）对数秩检验，以比较不同PDX组之间的分布;以及e）比例风险模型 以测试PDXs治疗与单一药物和组合。王博士和Baladandayuthapani将在 与UTPDTC调查人员密切合作，通过整合来自 多个实验室使用各种算法，包括主成分，偏最小二乘法， 贝叶斯网络模型。将使用R和Bioconductor软件包进行数据分析。的 Core将记录所有的分析并生成HTML或PDF报告（使用R包：Swave、WooPR和 R markdown）。

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).