MetaboQuest: A Suite of Tools for Metabolite Annotation

MetaboQuest：一套代谢物注释工具 项目摘要 代谢组学旨在高通量检测、定量和鉴定生物样品中的代谢物。 样品液相色谱-质谱联用技术的应用日益突出 在代谢组学领域，由于其能够用有限量的代谢物分析相当大数量的代谢物， 生物材料。然而，在通过LC-MS对人类样品进行的典型非靶向代谢组学分析中，约 70%的检测到的峰代表未知的分析物，主要是因为现有的质谱库涵盖了 只有一小部分已知化合物，但也由于峰拾取、峰对齐和 识别同位素峰和加合物形式。这些挑战阻碍了发展的步伐， 代谢组学的数据分析管道及其与其他组学研究的整合。第二阶段的目标是 SBIR的建议是使代谢组学研究与其他组学研究，如基因组学， 转录组学和蛋白质组学，对于这些，已经建立了良好的管道。通过这样做，我们将 加速代谢组学在系统生物学方法中的作用，用于各种应用，包括生物标志物 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， 建立基于LC-MS的非靶向代谢组学数据分析管道，从峰值检测开始， 代谢物注释。这将通过实施一套创新工具来实现， 组装到定制的管道中，并通过集成 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。本提案的目标1将侧重于开发一套工具，以实现： 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 对多个化合物数据库进行比对;（4）基于专家的推定ID评估;（5）同位素模式分析; (6)（7）MS/MS数据与实验和非实验数据的光谱匹配， 计算机碎片模式;（8）基于深度学习的化合物指纹预测;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Aim开发的工具 1到一个基于云的平台，MetaboQuest，它为用户提供交互式可视化的峰值，同位素 模式网络和质谱此外，Aim 2将专注于在MetaboQuest中集成管道 一个允许用户通过链接模块来创建管道并通过模块化的 交互式Web界面。目标3将在代谢物方面对MetaboQuest进行全面评价 注释准确性、注释代谢物的数量以及与其他现有技术相比的计算效率。 工具.代谢物注释的准确性将通过实验方法进行评价，其中MS/MS数据来自 比较了未知分析物和参比化合物，并通过使用来自多个样品的LC-MS/MS数据， 代谢组学研究，包括地面实况信息。成功实施和验证 MetaboQuest将有助于解决代谢组学中的主要瓶颈-代谢物鉴定， 从而消除了人工验证推定代谢物ID的需要，并提高了 代谢组学研究，特别是在疾病生物标志物和药物发现。

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.