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. !

* * * 摘要 * * 在所有类型的组学研究中（例如，基因组学、转录组学、蛋白质组学、代谢组学）、技术批次效应 对质量控制和再现性提出了根本性的挑战。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，有必要进行例行监测 以及在代谢组学实验室和技术平台内部和之间校正批次效应。 因此，我们建议在这里开发MetaBatch算法，计算工具和门户网站。 对于MetaBatch的开发，我们将利用我们在开发MBatch方面的经验，MBatch是一个成为 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 第一个目标是通过定制和 扩展MBatch管道，用于批次的检测、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，如我们的交互式下一代加密热图。的 第三个目标是将MetaBatch作为开源软件和基于云的软件分发给研究社区。 Galaxy版本。第四个目标是提供插件功能，用于将MetaBatch与其他 代谢组学资源，主要包括代谢组学研究（与Shankar博士合作 Subramaniam）和其他在共同基金代谢组学计划内开发的。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安德森学院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，以加快使用速度并征求反馈 关于限制、可能的改进以及在现实工作流中有用的其他模块。 我们为项目带来了各种资产，包括：作为软件起点的MBatch资源 开发;生物信息学，生物统计学，软件工程，生物学和 临床医学;具有21年临床疾病分子谱研究经验的PI (in一个财团的背景下）;在批量效应分析的国际领导地位;一个软件工程团队， 生产高端，高度可视化的生物信息学软件包和网站的跟踪记录;一个由20名分析师组成的团队 其专业知识可以调用;广泛的计算资源，包括最强大的 世界上以学术为基础的机器;强大的机构支持;以及与 一流的基础，翻译，和临床研究人员在整个MD安德森，最重要的癌症之一， 中心在该国。我们的底线使命将是帮助研究界努力提高严谨性 以及代谢组学的可重复性，以帮助科学理解和减轻疾病。 !