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.

项目摘要/摘要。此Baataar-Up NCI Artnet U54应用程序的目的是表征和 在治疗上抵抗对突变体分子靶向疗法的抗性的机制 通过描述非小细胞肺癌（NSCLC）中的EGFR和KRAS （TME）生态系统及其在治疗过程中的可塑性。为了实现这一目标，来自注释的多摩斯数据 将生成临床标本和几个免费模型系统。生物信息学，计算 生物学和生物统计学在这个Artnet研究中心起着重要作用。数据的主要目标 科学核心是构建和管理集中式多摩斯数据库，并提供一套完整的生物信息学， 计算和统计支持并整合了所有3个项目。这将包括基本和转化科学 在临床活检，PDX，PDO和细胞系模型等系统中，转录组学的整合，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，应用和开发最佳生物信息学以及统计算法，工具和 管道。核心由来自生物信息学的专家教师和计算科学家组成 MD Anderson以及来自生物工程的计算生物学和生物统计学部门 UCSF的治疗科学系。该计划的PI和共同研究人员以前曾使用过 与数据科学核心在其他项目和赠款应用程序中的研究人员紧密相关。这 数据科学核心将与项目1-3和管理核心紧密合作，以管理和分析 利用现有的，强大的IT结构的数据资源在MD Anderson和UCSF上进行了。数据 科学核心已为“ - 组”和功能生物学数据处理以及 分析。核心将将这些管道和算法应用于生成的所有类型的数据。核心将使用 标准设计原理，生物信息学，计算和统计算法，并将开发新的 根据需要分析这些项目中收集的所有数据的方法，包括空间转录组学，细胞细胞 相互作用分析以及CRISPR和蛋白质组织分析。将使用参数和非参数方法 用于参数估计和假设检验。线性模型和广义添加剂模型将用于 找到适合复杂数据结构的最佳模型。核心将在项目之间喜欢的假设测试 使用各种算法集成了来自多个实验室的数据集，包括基于贝叶斯网络 基因组净净作业的模型和模块化分析（磁）。所有数据分析将是 使用R和生物导管包进行。核心将记录所有分析并产生HTML或PDF 报告（使用r软件包：Sweave，Knitr，Markdown）用于文档和可重复性，并促进 内部以及外部Artnet和科学社区的数据共享。通过它的能力， 数据科学核心是确保在Baataar-Up和Artnet中成功和集成的中心枢纽。