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: 9615762
• 负责人: Rehan Akbani
• 金额: $ 44.86万
• 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/9615762
• 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; Data Quality; Detection; Development; Diagnosis; Disease; Documentation; Educational workshop; Environmental Risk Factor; Faculty; Feedback; Funding; Galaxy; Genomics; Goals; Imagery; 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; Work; base; built environment; cloud based; computerized tools; computing resources; experience; experimental study; flexibility; improved; innovation; instrument; interoperability; member; metabolomics; metabolomics resource; multidisciplinary; 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