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

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 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 对代谢组学的影响，化合物鉴定和相对定量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。 前处理包括峰检测、峰分组和LC/MS注释或光谱去卷积 GC/MS数据，峰对齐。现有的软件工具总是会产生大量的误报 和假阴性峰，产生不准确的峰群，错误对准检测峰，并且提取不准确 相对代谢物定量信息。这些错误可能会转化为下游错误或丢失 化合物鉴定并导致对代谢组的误导解释。此外，用户需要 指定大量参数，以使现有软件工具正常工作。不幸的是，普通用户通常 不了解如何优化这些参数，并且最大化一个方面(例如，敏感度)通常具有 对另一个人的有害影响(例如，专一性)。我们将通过开发更准确的 提高数据预处理的严密性和重复性的算法。建议的算法将是 用Java实现，并与广泛使用的MZmine 2集成，使软件具有跨平台和 界面友好，具有丰富的可视化功能。此外，该实施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。

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.