MetaboQuest: A Suite of Tools for Metabolite Annotation

一套代谢物标注工具--MetobQuest 项目总结 代谢组学的目标是高通量地检测、量化和鉴定生物中的代谢物 样本。液-质联用技术(LC-MS)的应用日益受到重视 在代谢组学领域，由于它能够分析相当数量的代谢物和有限数量的 生物材料。然而，在用LC-MS对人体样本进行典型的非靶向代谢组学分析时，大约 70%的检测峰代表未知分析物，主要是因为现有的质谱库涵盖 仅为已知化合物的一小部分，但也是由于峰选择、峰对齐和 识别同位素峰和加合物形式。这些挑战阻碍了中国的发展步伐 代谢组学及其与其他组学研究的整合的数据分析管道。这一阶段的目标是 SBIR建议将代谢组学研究与基因组学等其他组学研究相提并论， 转录组学和蛋白质组学，这些都有完善的管道可用。通过这样做，我们将 加速代谢组学在包括生物标记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，使客户能够 为分析基于LC-MS的非靶向代谢组学数据建立管道，从峰值检测到 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 组装到定制管道中，并通过集成以下技术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将侧重于开发一套工具，以实现：(1)峰值 检测、比对和质量评估；(2)加合物和同位素峰识别；(3)基于质量的搜索 针对多个化合物数据库；(4)基于专家的推定ID的评估；(5)同位素模式分析； (6)基于网络的推定ID的评估；(7)MS/MS数据与实验和实验数据的光谱匹配。 电子碎片模式；(8)基于深度学习的复合指纹预测；以及(9)综合 通过概率模型评估推定的代谢物ID。AIM 2将组装在AIM中开发的工具 1进入了一个基于云的平台MetabQuest，该平台为用户提供了峰、同位素 图案、网络和质谱图。此外，Aim 2将专注于将一条流水线整合到MetobQuest中 该构建器允许用户通过链接模块创建管道，并通过模块化的 交互式Web界面。Aim 3将从代谢物的角度对MetobQuest进行全面评估 注释的准确性、注释代谢物的数量和计算效率与其他现有的 工具。代谢物注释的准确性将通过实验方法进行评估，在实验方法中，MS/MS数据来自 对未知分析物和参比化合物进行了比较，并利用来自不同国家的 由事实信息组成的代谢组学研究。成功实施和验证 MetobQuest将有助于解决代谢组学中的主要瓶颈-代谢物鉴定， 从而消除了对推定代谢物ID的人工验证的需要，并增强了 代谢组学研究，特别是在疾病生物标志物和药物发现方面。

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.