Cross-Platform and Graphical Software Tool for Adaptive LC/MS and GC/MS Metabolomics Data Preprocessing

用于自适应 LC/MS 和 GC/MS 代谢组学数据预处理的跨平台和图形化软件工具

• 批准号: 10005903
• 负责人: Xiuxia Du
• 金额: $ 33.9万
• 依托单位: UNIVERSITY OF NORTH CAROLINA CHARLOTTE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10005903
• 关键词: 3-Dimensional; Address; Algorithmic Software; Algorithms; Award; Collaborations; Computer software; Core Facility; Data; Data Files; Detection; Dimensions; Disease; Error Sources; Grouping; Individual; Informatics; Ions; Isotopes; Java; Laboratories; Mass Fragmentography; Mass Spectrum Analysis; Measures; Memory; Methodology; Nature; Reproducibility; Scientist; Software Tools; Specificity; Speed; Structure; Testing; Therapeutic; Time; To specify; Translating; Visualization; Work; adduct; base; design; improved; large scale data; liquid chromatography mass spectrometry; metabolome; metabolomics; molecular mass; novel strategies; success; user-friendly

Project Summary / Abstract Data preprocessing is critical for the success of any MS-based untargeted metabolomics study, as it is the first informatics step for making sense of the data. Despite the enormous contributions that existing software tools have made to metabolomics, errors in compound identification and relative quantitation are still plaguing the field. This issue is becoming more serious as the sensitivity of LC/MS and GC/MS platforms is constantly increasing. Preprocessing involves peak detection, peak grouping and annotation for LC/MS or spectral deconvolution for GC/MS data, and peak alignment. Existing software tools invariably yield an immense number of false positive and false negative peaks, produce inaccurate peak groups, mis-align detected peaks, and extract inaccurate information of relative metabolite quantitation. These errors can translate downstream into spurious or missing compound identifications and cause misleading interpretations of the metabolome. Furthermore, users need to specify a large number of parameters for existing software tools to work. Unfortunately, general users usually do not understand how to optimize these parameters, and maximizing one aspect (e.g., sensitivity) often has deleterious effects on another (e.g., specificity). We will address these challenges by developing more accurate algorithms for improving the rigor and reproducibility of data preprocessing. The proposed algorithms will be implemented in Java and integrated with the widely-used MZmine 2, making the software cross-platform and user-friendly with rich visualization capabilities. In addition, the implementation will be optimized for memory efficiency and computing speed allowing large-scale data preprocessing. Extensive testing of the software will be conducted in close collaborations with metabolomics core facilities and users around the world.

项目摘要 /摘要 数据预处理对于任何基于MS的非靶向代谢组学研究的成功至关重要，因为这是第一个 了解数据的信息步骤。尽管现有软件工具有巨大的贡献 已经对代谢组学，复合识别和相对定量的错误仍在困扰该领域。 随着LC/MS和GC/MS平台的敏感性不断增加，这个问题变得越来越严重。 预处理涉及LC/MS的峰值检测，峰分组和注释或光谱反卷积的峰值检测 GC/MS数据和峰对齐。现有的软件工具总是产生大量的误报 和假阴性峰，产生不准确的峰基，错误的对准峰并提取不准确 相对代谢物定量的信息。这些错误可以将下游转化为虚假或缺失 复合识别并引起代谢组的误导性解释。此外，用户需要 为现有的软件工具指定大量参数。不幸的是，普通用户通常 不了解如何优化这些参数，并最大化一个方面（例如敏感性）通常具有 对另一个的有害影响（例如特异性）。我们将通过开发更准确的来解决这些挑战 改善数据预处理的严格性和可重复性的算法。提出的算法将是 在Java中实施并与广泛使用的Mzmine 2集成，使软件跨平台和 具有丰富可视化功能的用户友好型。此外，该实现将用于内存 效率和计算速度允许大规模数据预处理。该软件的大量测试将是 与代谢组学核心设施和世界各地的用户密切合作。