XCMS Metabolomic Data Technology

 描述(由申请人提供)：这份XCMS代谢方案对生物医学计算、信息学和大数据科学技术的扩展开发、强化和传播的资金机会具有高度的响应性。《自然》杂志最近的一份报告预测，代谢组学将是未来十年影响人类疾病治疗和诊断的顶级研究领域之一1，基于质谱学的代谢组学特别是成为阐明新的生物标记物、执行诊断测试和确定疾病的生化机制的最广泛使用的平台。拟议的XCMS代谢数据技术研究将开发现有的基于云的XCMS Online(https://xcmsonline.scripps.edu))代谢分析技术，以满足超过4,500名现有用户的需求，使其能够扩展到远远超出当前日益增长的用户基础。进行新陈代谢分析的主要挑战是数据的生物信息处理，Siuzdak实验室的名为XCMS和Metlin的软件和数据库已经解决了这一问题。这些资源是该领域最广泛使用的生物信息学技术，这里介绍的发展对于强化和传播代谢组界的这一资源至关重要。我们将通过促进大规模数据上传、快速数据处理、长期数据存储和TB级数据分析，包括一套新的统计分析方法、可视化技术和代谢组界基于云的数据共享，为其用户开发XCMS在线。我们还将开发一种新的数据流方法，以允许生物依赖数据的获取。一项关键的发展将包括通过创建生物信息学解决方案来处理从对已进行同位素浓缩的样品进行定向和非定向分析而获得的代谢组数据，从而对稳定同位素进行分析。这些数据包含大量关于细胞代谢的信息，将有助于XCMS在线平台的强化和传播。

 DESCRIPTION (provided by applicant): This XCMS metabolomic proposal is highly responsive to this funding opportunity for the extended development, hardening and dissemination of technologies in biomedical computing, informatics and big data science. A recent report in the journal Nature predicts that metabolomics will be one of the top areas of research to impact the treatment and diagnosis of human disease over the next ten years1, and mass spectrometry-based metabolomics specifically has emerged as the most widely used platform to elucidate novel biomarkers, perform diagnostic testing, and identify biochemical mechanisms of disease. The XCMS Metabolomic Data Technology proposed research will develop the existing cloud-based XCMS Online (https://xcmsonline.scripps.edu) technology for metabolic analyses to meet the needs of the over 4500 current users, allowing it to be expandable well beyond the current user base which is growing daily. The major challenge in performing metabolomic analyses is the bioinformatic processing of data, which has been addressed by the Siuzdak lab's software and database called XCMS and METLIN. These resources are the most widely used bioinformatic technologies in the field and the developments presented here are crucial to hardening and disseminating this resource for the metabolomic community. We will develop XCMS Online for its users by facilitating large scale data uploads, rapid data processing, long- term data storage and terabyte scale data analysis, including a new suite of statistical analysis approaches, visualization technologies and cloud-based data sharing for the metabolomic community. We will also develop a novel data streaming approach to allow for biological dependent data acquisition. A key development will include the analysis of stable isotopes by creating a bioinformatic solution to process metabolomic data acquired from targeted and untargeted analysis of samples that have been isotopically enriched. These data have a tremendous amount of information about cellular metabolism and will help facilitate the hardening and dissemination of the XCMS Online platform.