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.

项目概要/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，因为它是第一个 理解数据的信息学步骤。尽管现有软件工具做出了巨大贡献 尽管代谢组学已经取得进展，但化合物鉴​​定和相对定量中的错误仍然困扰着该领域。 随着 LC/MS 和 GC/MS 平台的灵敏度不断提高，这个问题变得越来越严重。 预处理涉及 LC/MS 的峰检测、峰分组和注释或光谱解卷积 GC/MS 数据和峰对齐。现有的软件工具总是会产生大量误报 和假阴性峰，产生不准确的峰组，未对齐检测到的峰，并提取不准确的峰 相对代谢物定量信息。这些错误可能会在下游转化为虚假或缺失 化合物鉴定并导致对代谢组的误导性解释。此外，用户需要 指定大量参数以使现有软件工具能够工作。不幸的是，一般用户通常 不了解如何优化这些参数，并且最大化某一方面（例如灵敏度）通常会导致 对他人产生有害影响（例如，特异性）。我们将通过开发更准确的技术来应对这些挑战 用于提高数据预处理的严谨性和可重复性的算法。所提出的算法将是 采用Java实现，并与广泛使用的MZmine 2集成，使得软件跨平台、 用户友好，具有丰富的可视化功能。此外，该实现还将针对内存进行优化 效率和计算速度允许大规模数据预处理。将对软件进行广泛的测试 与世界各地的代谢组学核心设施和用户密切合作进行。