MetaboQuest: A Suite of Tools for Metabolite Annotation

MetaboQuest：代谢物注释工具套件

• 批准号: 10570907
• 负责人: Dawit Mengistu
• 金额: $ 99.79万
• 依托单位: OMICSCRAFT, LLC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-11 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10570907
• 关键词: Acceleration; Address; Adopted; Biochemical; Biocompatible Materials; Biological; Computing Methodologies; Consumption; Coupled; Data; Data Analyses; Data Analytics; Databases; Decision Making; Detection; Development; Disease; Evaluation; Fingerprint; Genomics; Goals; Graph; Human; Isotopes; Knowledge; Libraries; Link; Liquid Chromatography; Manuals; Mass Spectrum Analysis; Measures; Methods; Network-based; Organism; Pathway Analysis; Pathway interactions; Pattern; Performance; Phase; Privacy; Proteomics; Resources; Role; Running; Sampling; Small Business Innovation Research Grant; Statistical Models; Systems Biology; Testing; Time; Uncertainty; Validation; Visualization; adduct; analysis pipeline; biomarker discovery; cloud based; computerized tools; cost; deep learning; design; drug discovery; in silico; innovation; metabolomics; tool; transcriptomics; web interface

MetaboQuest: A Suite of Tools for Metabolite Annotation PROJECT SUMMARY Metabolomics aims at high throughput detection, quantification, and identification of metabolites in biological samples. The use of liquid chromatography coupled with mass spectrometry (LC-MS) has risen in prominence in the field of metabolomics due to its ability to analyze a sizable number of metabolites with a limited amount of biological material. However, in a typical untargeted metabolomics analysis of human samples by LC-MS, about 70% of the detected peaks represent unknown analytes mainly because existing mass spectral libraries cover only a small fraction of known compounds, but also due to uncertainty in peak picking, alignment of peaks, and recognizing isotopic peaks and adduct forms. These challenges have kept at bay the pace of development of data analytics pipelines for metabolomics and its integration with other omics studies. The goal of this Phase II SBIR proposal is to make metabolomics studies on a par with other omics studies such as genomics, transcriptomics, and proteomics, for which well-established pipelines are available. By doing so, we will accelerate the role of metabolomics in systems biology approaches for various applications including biomarker and drug discovery. To achieve this goal, we propose to develop a cloud-based platform that allows customers to build pipelines for analysis of LC-MS-based untargeted metabolomics data, starting from peak detection to metabolite annotation. This will be accomplished by implementing a suite of innovative tools that can be assembled into customized pipelines and by enhancing metabolite annotation accuracy through integration of information derived from multiple resources including compound databases, pathways, biochemical networks, and mass spectral libraries. Aim 1 of this proposal will focus on developing a suite of tools to enable: (1) peak detection, alignment, and quality assessment; (2) adduct and isotopic peak recognition; (3) mass-based search against multiple compound databases; (4) expert-based evaluation of putative IDs; (5) isotopic pattern analysis; (6) network-based evaluation of putative IDs; (7) spectral matching of MS/MS data against experimental and in- silico fragmentation patterns; (8) deep learning-based prediction of compound fingerprints; and (9) integrative assessment of putative metabolite IDs via a probabilistic model. Aim 2 will assemble the tools developed in Aim 1 into a cloud-based platform, MetaboQuest, which provides users with interactive visualization of peaks, isotopic patterns, networks, and mass spectra. Furthermore, Aim 2 will focus on integrating into MetaboQuest a pipeline builder that allows users to create pipelines by linking modules and run them remotely through a modular interactive web interface. Aim 3 will perform a comprehensive evaluation of MetaboQuest in terms of metabolite annotation accuracy, number of annotated metabolites, and computational efficiency compared to other existing tools. Accuracy in metabolite annotation will be evaluated via experimental methods in which MS/MS data from unknown analytes and reference compounds are compared, and by using LC-MS/MS data from multiple metabolomics studies that consist of ground-truth information. Successful implementation and validation of MetaboQuest will contribute to addressing the major bottleneck in metabolomics - metabolite identification, thereby eliminating the need for manual verification of putative metabolite IDs and enhancing the contribution of metabolomics studies, specifically in disease biomarker and drug discovery.

MetaboQuest：代谢物注释工具套件 项目概要 代谢组学旨在高通量检测、定量和鉴定生物中的代谢物 样品。液相色谱与质谱联用 (LC-MS) 的使用日益受到重视 在代谢组学领域由于其能够用有限的量分析大量的代谢物 生物材料。然而，在通过 LC-MS 对人类样本进行的典型非靶向代谢组学分析中，大约 70% 的检测到的峰代表未知分析物，主要是因为现有的质谱库涵盖了 仅已知化合物的一小部分，而且还由于峰拾取、峰对齐的不确定性，以及 识别同位素峰和加合物形式。这些挑战阻碍了我国的发展步伐 代谢组学的数据分析管道及其与其他组学研究的集成。第二阶段的目标 SBIR 提案旨在将代谢组学研究与其他组学研究（例如基因组学、 转录组学和蛋白质组学，有完善的管道可供使用。通过这样做，我们将 加速代谢组学在包括生物标志物在内的各种应用的系统生物学方法中的作用 和药物发现。为了实现这一目标，我们建议开发一个基于云的平台，让客户 建立用于分析基于 LC-MS 的非靶向代谢组学数据的管道，从峰检测到 代谢物注释。这将通过实施一套创新工具来实现，这些工具可以 组装成定制的管道，并通过集成提高代谢物注释的准确性 来自多种资源的信息，包括化合物数据库、途径、生化网络、 和质谱库。该提案的目标 1 将重点开发一套工具来实现：(1) 峰值 检测、对准和质量评估； (2)加合物和同位素峰识别； (3)海量搜索 针对多个化合物数据库； (4) 对假定 ID 进行专家评估； (5)同位素模式分析； (6) 推定ID的网络评估； (7) MS/MS 数据与实验数据和实际数据的光谱匹配 硅片碎片模式； (8) 基于深度学习的复合指纹预测； (9)综合性 通过概率模型评估假定的代谢物 ID。 Aim 2 将组装 Aim 中开发的工具 1 进入基于云的平台 MetaboQuest，该平台为用户提供峰、同位素的交互式可视化 模式、网络和质谱。此外，Aim 2 将专注于将管道集成到 MetaboQuest 构建器，允许用户通过链接模块创建管道并通过模块化远程运行它们 交互式网络界面。 Aim 3将对MetaboQuest进行代谢物方面的综合评估 与其他现有技术相比，注释准确性、注释代谢物数量和计算效率 工具。代谢物注释的准确性将通过实验方法进行评估，其中 MS/MS 数据来自 对未知分析物和参考化合物进行比较，并使用来自多个化合物的 LC-MS/MS 数据 包含真实信息的代谢组学研究。成功实施并验证 MetaboQuest 将有助于解决代谢组学的主要瓶颈 - 代谢物鉴定、 从而消除了对推定代谢物 ID 进行手动验证的需要，并增强了 代谢组学研究，特别是疾病生物标志物和药物发现。