Data Management and Analysis Core

项目总结（数据管理和分析核心：Aikseng Ooi和Nirav Merchant） 亚利桑那大学超级基金研究计划（UA SRP）将产生大量和类型的数据， 在传统的实验室环境中是无法控制的。数据管理和分析核心（DMAC） 作为UA SRP的主要服务，将其转化为大型生物、地球物理和化学数据集，包括 但不限于RNA测序，染色质免疫沉淀测序，外显子组测序， 代谢组学，宏基因组学，微生物组扩增子测序，地理空间定位，分析化学， 和成像。DMAC通过执行三个核心功能使调查员能够：（1）DMAC将领导住房 将所有数据存储在一个易于访问的数据存储系统中：CyVerse。Cyverse是一个计算基础设施 由硬件、软件和人员组成，旨在处理庞大的数据集和复杂的 分析，并在亚利桑那大学维护。DMAC将使用参考实现（RI）， 将数据分为五个不同的级别，以便于数据共享，处理和分析。最低等级（1级） 将是原始数据，而更高级别（级别5）将是图形可视化中可利用的文件格式。DMAC将 在统计学家和生物信息学家的帮助下支持这些过程， 个体研究者的策略。除了数据存储，DMAC还将协调样品管理 使用Fulfill软件。Fulfilling允许生物样品的条形码、全球定位和注释， 一个易于使用的应用程序，可在传统工作站和移动的平台上使用。氟喹诺酮类药物对于 由于其流动性，可用于生成点样本跟踪。(2)除了数据和样品管理，DMAC还将 执行数据的标准和自定义计算分析。这将包括DMAC-lead 调查“特征签名”，解决UA SRP项目中数据的可预测性; 例如，与特定砷处理相关的基因表达变化能否预测宏基因组学 在类似处理的样品中的变化？与UA SRP调查人员一起，DMAC将采用传统的 算法，或根据需要开发新的算法，以识别所收集的不同数据类型的签名。 (3)DMAC的存储和分析能力将被集成到一个用户友好的网络应用程序中， 允许个体研究者检索、操作和可视化UA SRP数据。Web应用程序将 使用内部维护的服务器结合R统计环境实现。DMAC并用 因此，这是普遍服务供应商改革方案的一个组成部分，该方案利用最先进的技术， 发现砷暴露及其在健康和疾病中的作用的新见解。

PROJECT SUMMARY (Data Management and Analysis Core: Aikseng Ooi and Nirav Merchant) The University of Arizona Superfund Research Program (UA SRP) will generate volumes and types of data that are not manageable in traditional laboratory settings. The Data Management and Analysis Core (DMAC) will function as the primary service for UA SRP into large biological, geophysical, and chemical datasets, including but not limited to RNA sequencing, chromatin immunoprecipitation sequencing, exome sequencing, metabolomics, metagenomics, microbiome amplicon sequencing, geospatial positioning, analytical chemistry, and imaging. DMAC enables investigators by performing three core functions: (1) DMAC will lead the housing of all data in an easy-to-access data repository system: CyVerse. Cyverse is a computational infrastructure consisting of hardware, software, and personnel that are designed to handle huge datasets and complex analyses, and is maintained at the University of Arizona. DMAC will utilize a reference implementation (RI) that divides data into five different levels for easy data sharing, processing, and analyzing. Lowest levels (level 1) will be raw data, while higher levels (level 5) will be file formats utilizable in graphics visualizations. DMAC will support these processes with help from on-staff statisticians and bioinformaticians who can devise analysis strategies for individual investigators. In addition to data storage, DMAC will orchestrate sample management using Fulcrum software. Fulcrum allows barcoding, global positioning, and annotation of biological samples in an easy-to-use application available on both traditional workstations and mobile platforms. Fulcrum is critical for point-of-generation sample tracking due to its mobility. (2) Beyond data and sample management, DMAC will perform both standard and custom computational analyses of the data. This will include DMAC-lead investigations into “feature signatures”, which address the predictability of data across UA SRP projects; for example, can the gene expression changes associated with a particular arsenic treatment predict metagenomics changes in a similarly treated sample? In conjunction with UA SRP investigators, DMAC will apply traditional algorithms, or develop novel algorithms as needed, to identify signatures for the different data types collected. (3) The storage and analytical capabilities of DMAC will be integrated into a user-friendly web application that allows individual investigators to retrieve, manipulate, and visualize UA SRP data. The web application will be implemented using an in-house maintained server in conjunction with the R statistical environment. DMAC is thus an integral component of the UA SRP proposal that utilizes state-of-the-art technologies to enable the discovery of novel insights into arsenic exposure and its role in health and disease.