Computational Tools for Analysis and Visualization of Quality Control Issues in Metabolomic Data

用于代谢组数据质量控制问题分析和可视化的计算工具

• 批准号: 10005202
• 负责人: Rehan Akbani
• 金额: $ 43.36万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10005202
• 关键词: Address; Adoption; Affect; Algorithmic Software; Algorithms; Attention; Beech; Bioinformatics; Biological; Biology; Biomedical Research; Biometry; Cancer Center; Clinical; Clinical Medicine; Collaborations; Communities; Computer software; Core Facility; Country; Custom; DNA; Data; Detection; Development; Diagnosis; Disease; Documentation; Educational workshop; Environmental Risk Factor; Faculty; Feedback; Funding; Galaxy; Genomics; Goals; International; Laboratories; Leadership; Letters; Maps; Mass Spectrum Analysis; Mission; Modeling; Molecular Profiling; Plug-in; Process; Proteins; Proteomics; Quality Control; RNA; Reproducibility; Research; Research Personnel; Resources; Sampling; Software Engineering; Source; System; Testing; The Cancer Genome Atlas; Training; Translating; Variant; Visual; Visualization; Work; base; bioinformatics tool; built environment; cloud based; computerized tools; computing resources; data quality; experience; experimental study; flexibility; improved; innovation; instrument; interoperability; member; metabolomics; metabolomics resource; multidisciplinary; multiple data sources; next generation; open source; programs; software development; tool; transcriptomics; translational scientist; trend; user-friendly; web portal; web site; webinar; working group

* * * Abstract * * * In omic studies of all types (e.g., genomic, transcriptomic, proteomic, metabolomic), technical batch effects pose a fundamental challenge to quality control and reproducibility. The possibilities for serious error are greatly magnified in metabolomics, however, due to a range of possible platform, operator, instrument, and environmental factors that can cause batch (or trend) effects. Hence, there is a need for routine surveillance and correction of batch effects within and across metabolomics laboratories and technological platforms. Accordingly, we propose here to develop the MetaBatch algorithms, computational tool, and web portal. For development of MetaBatch, we will leverage our experience in developing MBatch, a tool that became indispensible for quality-control of data in all 33 projects of The Cancer Genome Atlas (TCGA) program. Our first aim is to translate the successful quality control model from TCGA to metabolomics by customizing and extending the MBatch pipeline for detection, quantitation, diagnosis, interpretation, and correction of batch and trend effects. The second aim is to develop and incorporate innovative metabolomics-specific algorithms, including major visualization resources such as our interactive Next-Generation Clustered Heat Maps. The third aim is to distribute MetaBatch to the research community as open-source software and in cloud-based and Galaxy versions. The fourth aim is to provide plug-in capability for integration of MetaBatch with other metabolomic resources, prominently including Metabolomics Workbench (in collaboration with Dr. Shankar Subramaniam) and others developed within the Common Fund Metabolomics Program. Our fifth aim is to promote MetaBatch actively and interact extensively with other Consortium members and the metabolomics research community. With active support from MD Anderson Faculty and Academic Development, we will provide documentation, tutorials, videos, demonstrations, and training to accelerate use and to solicit feedback on limitations, possible improvements, and additional modules that would be useful in real-world workflows. We bring a variety of assets to the project, including: the MBatch resource as a starting point for software development; multidisciplinary expertise in bioinformatics, biostatistics, software engineering, biology, and clinical medicine; PIs with a combined 21 years of experience in molecular profiling studies of clinical disease (in a consortial context); international leadership in batch effects analysis; a software engineering team with a track record of producing high-end, highly visual bioinformatics packages and websites; a team of 20 Analysts whose expertise can be called on; extensive computing resources, including one of the most powerful academically based machines in the world; strong institutional support; and close working relationships with first-class basic, translational, and clinical researchers throughout MD Anderson, one of the foremost cancer centers in the country. Our bottom-line mission will be to aid the research community's effort to improve rigor and reproducibility in metabolomics for scientific understanding and to alleviate disease. !

* * * 抽象的 * * * 在所有类型的组学研究（例如基因组学、转录组学、蛋白质组学、代谢组学）中，技术批次效应 对质量控制和再现性提出了根本性挑战。出现严重错误的可能性是 然而，由于一系列可能的平台、操作员、仪器和 可能导致批次（或趋势）效应的环境因素。因此，需要进行日常监测 以及代谢组学实验室和技术平台内部和之间的批次效应校正。 因此，我们在此建议开发 MetaBatch 算法、计算工具和门户网站。 对于 MetaBatch 的开发，我们将利用我们开发 MBatch 的经验，该工具已成为 对于癌症基因组图谱 (TCGA) 计划的所有 33 个项目的数据质量控制不可或缺。我们的 第一个目标是通过定制和分析将成功的质量控制模型从 TCGA 转化为代谢组学 扩展 MBatch 管道，用于批次和批次的检测、定量、诊断、解释和校正 趋势效应。第二个目标是开发和整合创新的代谢组学特定算法， 包括主要的可视化资源，例如我们的交互式下一代集群热图。这 第三个目标是将 MetaBatch 作为开源软件和基于云的方式分发给研究社区 和银河版本。第四个目标是提供 MetaBatch 与其他集成的插件功能 代谢组学资源，主要包括代谢组学工作台（与 Shankar 博士合作） Subramaniam）和其他在共同基金代谢组学计划中开发的项目。我们的第五个目标是 积极推广MetaBatch并与其他联盟成员和代谢组学广泛互动 研究社区。在 MD 安德森教授和学术发展部的积极支持下，我们将 提供文档、教程、视频、演示和培训，以加速使用并征求反馈 关于限制、可能的改进以及在现实工作流程中有用的附加模块。 我们为该项目带来了各种资产，包括： MBatch 资源作为软件的起点 发展;生物信息学、生物统计学、软件工程、生物学等多学科专业知识 临床医学； PI 在临床疾病分子谱研究方面拥有总计 21 年的经验 （在财团背景下）；批次效应分析领域处于国际领先地位；一个软件工程团队 制作高端、高度可视化生物信息学软件包和网站的记录； 20 名分析师组成的团队 可以利用其专业知识；广泛的计算资源，包括最强大的计算资源之一 世界上基于学术的机器；强有力的制度支持；和密切的工作关系 MD 安德森癌症中心拥有一流的基础、转化和临床研究人员，MD 安德森是最重要的癌症研究中心之一 国内的中心。我们的底线使命是帮助研究界提高严谨性 以及代谢组学的可重复性，以实现科学理解和减轻疾病。 ！