Integrative Pipeline for Analysis & Translational Application of TCGA Data (GDAC)

拟议的基因组数据分析中心B（GDAC B）将与其他GDAC合作， 通过癌症基因组图谱（TCGA）项目，（i）为系统开发创新的综合管道- 水平分析TCGA对许多不同类型的人类肿瘤的分子谱数据，以及（ii）应用 管道及其组件模块到TCGA数据，以解决重要的生物学和临床问题。一个 总体目标是根据新的肿瘤，对患者的癌症进行“个性化”管理。 生物标记和生物特征。这是第一次，在肿瘤上生成数百万个数据点比 分析或解释这些数据，因此生物信息学的挑战是艰巨的。该管道将 使用敏捷软件开发范式和语义Web查询架构构建。将 基于GDAC参与者开发的新算法和模块。将包括以下模块： 数据集成、数据可视化、途径分析和系统生物学解释，所有这些都旨在 对实验室研究人员和临床医生来说是用户友好的。这些模块将与其他模块连接 由其他GDAC开发，所有开发将遵循TCGA和癌症生物医学标准 信息网格（caBIG），并将提供受控访问，以确保个人身份信息的保密性。 数据拟议中的GDAC团队将为该项目带来生物信息学、生物统计学、软件 工程，高通量分子分析技术，系统导向生物学，生物标志物研究， 病理学和临床研究。三个共同PI（生物信息学、系统生物学和临床研究） 自TCGA成立以来，我和团队的其他成员都积极参与了TCGA，包括 首席软件工程师一个主要的优势是得克萨斯大学M。D.安德森癌症中心 （MDACC）作为一个机构。MDACC一直是，并可能继续是，最大的来源， 肿瘤标本进行TCGA。作为全国最重要的癌症中心之一， 临床研究计划，MDACC拥有无与伦比的专业知识，跟进医学上重要的线索， 这是TCGA数据流水线开发和应用的结果。

The proposed Genome Data Analysis Center B (GDAC B) will work cooperatively with other GDACs funded by The Cancer Genome Atlas (TCGA) project to (i) develop an innovative, integrative pipeline for systems- level analysis of TCGA's molecular profiling data on many different types of human tumors and (ii) apply that pipeline and its component modules to TCGA data to address important biological and clinical questions. An overarching goal is to 'personalize' the management of patients' cancers on the basis of new tumor biomarkers and biosignatures. For the first time, it is easier to generate millions of data points on tumors than to analyze or interpret those data, hence the bioinformatic challenge is formidable. The pipeline will be constructed using the Agile software development paradigm and semantic web query architecture. It will be based on novel algorithms and modules developed by participants in the GDAC. Included will be modules for data integration, data visualization, pathway analysis, and systems biological interpretation, all designed to be user-friendly for the bench researcher and clinician. Those modules will be interfaced with additional ones developed by other GDACs, All development will adhere to standards of TCGA and the Cancer Biomedical Informatics Grid (caBIG) and will provide controlled access to ensure confidentiality of personally identifiable data. The proposed GDAC team brings to this project expertise in bioinformatics, biostatistics, software engineering, high-throughput molecular profiling technologies, systems-oriented biology, biomarker studies, pathology, and clinical research. The three co-PIs (for bioinformatics, systems biology, and clinical research) have each participated actively in TCGA since its inception, as have other members of the team, including the lead software engineer. A major strength is the University of Texas M. D. Anderson Cancer Center (MDACC) as an institution. MDACC has been, and presumably will continue to be, the largest source of tumor specimens for TCGA. As one of the country's foremost cancer centers, with by far the largest cancer clinical research program, MDACC has unparalleled expertise for follow up on medically important leads that result from the development and application of the pipeline to TCGA data.