Data Science Core

数据科学核心

• 批准号: 10517259
• 负责人: Jing Wang
• 金额: $ 9.53万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10517259
• 关键词: Algorithmic Software; Algorithms; Applications Grants; Basic Science; Bayesian Network; Bioconductor; Bioinformatics; Biological Models; Biology; Biomedical Engineering; Biometry; Biopsy; Cell Communication; Cell Line; Cells; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; Computational Biology; Computational algorithm; Computing Methodologies; Consult; Data; Data Analyses; Data Science Core; Databases; Documentation; Ecosystem; Ensure; Epidermal Growth Factor Receptor; Faculty; Genomics; Goals; KRAS2 gene; Laboratories; Linear Models; Malignant Neoplasms; Manuscripts; Methods; Mission; Modeling; Multiomic Data; Network-based; Non-Small-Cell Lung Carcinoma; Pathway Analysis; Play; Proteomics; Reporting; Reproducibility; Research; Research Personnel; Resistance; Role; Science; Scientist; Specimen; Statistical Algorithm; Structure; System; Testing; Therapeutic; Translational Research; Universities; Work; central database; complex data; computerized data processing; data analysis pipeline; data resource; data sharing; design; molecular targeted therapies; multiple datasets; multiple omics; mutant; programs; success; tool; transcriptomics; tumor; tumor microenvironment

Project Summary/Abstract. The goal of this BAATAAR-UP NCI ARTNet U54 application is to characterize and therapeutically counteract mechanisms of acquired resistance to molecularly-targeted therapies against mutant EGFR and KRAS in non-small cell lung cancer (NSCLC) by delineating the tumor-tumor microenvironment (TME) ecosystem and its plasticity during treatment. To achieve this goal, multi-omics data from annotated clinical specimens and several complimentary model systems will be generated. Bioinformatics, computational biology and biostatistics play an important role in this ARTNet Research Center. The major objective of the Data Science Core is to build and manage centralized multi-omics database and provide a full set of bioinformatical, computational and statistical support and integrate all 3 Project. This will include basic and translational science in systems such as clinical biopsies, PDX, PDO and cell line models, and integration of transcriptomics, spatial, genomics, proteomics and functional biology studies. We will contribute by providing computational and statistical support and applying and developing optimal bioinformatic, and statistical algorithms, tools and pipelines. The Core is staffed by expert faculty and computational scientists from the Bioinformatics and Computational Biology and Biostatistics Departments at MD Anderson and from the Bioengineering and Therapeutic Sciences Department at UCSF. This program’s PI and Co-investigators have previously worked closely and synergistically with Data Science Core’s investigators in other projects and grant applications. The Data Science Core will work closely with Projects 1–3 and the Administrative Core to manage and analyze the data resources utilizing the existing, robust IT structure in place at MD Anderson and UCSF. The Data Science Core has built various pipelines and algorithms for “-omics” and functional biology data processing and analyses. The Core will apply these pipelines and algorithms to all types of data generated. The Core will utilize standard design principles, bioinformatical, computational and statistical algorithms, and will develop new methods as needed to analyze all data collected in these projects, including spatial transcriptomics, cell-cell interaction analysis, and CRISPR- and proteomic profiling. Parametric and nonparametric methods will be used for parameter estimation and hypothesis testing. Linear models and generalized additive models will be used to find the best models to fit complex data structures. The Core will facilitate hypothesis testing across projects by integrating datasets from multiple laboratories using various algorithms, including Bayesian network-based models and Modular Analysis of Genomic NET works In Cancer (MAGNETIC). All data analyses will be performed using R and Bioconductor packages. The Core will document all analyses and produce HTML or PDF reports (using R packages: Sweave, knitR, markdown) for documentation and reproducibility and to facilitate data sharing internally and with the external ARTNet and scientific communities. Through its capabilities, the Data Science Core serves as a central hub to ensure success and integration across BAATAAR-UP and ARTNet.

项目概要/摘要。此BAATAAR-UP NCI ARTNet U 54应用程序的目标是表征和 对突变型抗肿瘤分子靶向治疗获得性耐药的治疗抵消机制 EGFR和KRAS在非小细胞肺癌（NSCLC）中通过描绘肿瘤-肿瘤微环境 (TME)生态系统及其可塑性。为了实现这一目标，多组学数据从注释 临床标本和几个免费的模型系统将被生成。生物信息学，计算 生物学和生物统计学在这个ARTNet研究中心发挥着重要作用。数据的主要目的 Science Core是建立和管理集中的多组学数据库，并提供全套生物信息学， 计算和统计支持，并整合所有3个项目。这将包括基础科学和转化科学 在诸如临床活组织检查、PDX、PDO和细胞系模型的系统中，以及转录组学、空间、 基因组学、蛋白质组学和功能生物学研究。我们将通过提供计算和 统计支持和应用和开发最佳的生物信息学，统计算法，工具和 管道该核心由来自生物信息学和计算机科学的专家教师和计算科学家组成， MD安德森大学的计算生物学和生物统计学系以及生物工程和 加州大学旧金山分校的治疗科学系。该项目的PI和合作研究者以前曾在 与Data Science Core的研究人员在其他项目和拨款申请中密切合作。的 数据科学核心将与项目1-3和行政核心密切合作， 利用MD安德森和UCSF现有的强大IT结构的数据资源。数据 Science Core为“组学”和功能生物学数据处理建立了各种管道和算法， 分析。核心将把这些管道和算法应用于生成的所有类型的数据。核心将利用 标准设计原则，生物信息学，计算和统计算法，并将开发新的 分析这些项目中收集的所有数据所需的方法，包括空间转录组学，细胞-细胞 相互作用分析以及CRISPR和蛋白质组学分析。将使用参数和非参数方法 用于参数估计和假设检验。线性模型和广义加性模型将用于 找到适合复杂数据结构的最佳模型。核心将通过以下方式促进跨项目的假设检验： 使用各种算法整合来自多个实验室的数据集，包括基于贝叶斯网络的 模型和模块化分析的基因组网络工程在癌症（磁性）。所有数据分析将 使用R和Bioconductor软件包进行。核心将记录所有分析并生成HTML或PDF 报告（使用R软件包：Swave、WARDIR、Markdown），用于文档化和再现性， 在内部以及与外部非洲热带农业研究和培训网络和科学界共享数据。通过其能力， 数据科学核心作为一个中心枢纽，确保BAATAAR-UP和ARTNet的成功和集成。