Computational Techniques for Advancing Untargeted Metabolomics Analysis

推进非靶向代谢组学分析的计算技术

• 批准号: 10022125
• 负责人: Soha Hassoun
• 金额: $ 37.9万
• 依托单位: TUFTS UNIVERSITY MEDFORD
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-23 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10022125
• 关键词: Address; Adoption; Biological; Biomedical Research; Blood Circulation; Case Study; Chemical Structure; Chemicals; Complex; Computational Technique; Computing Methodologies; Consumption; Data; Data Set; Databases; Development; Disease; Engineering; Ensure; Feedback; Goals; Health; Human; Internet; Intestines; Label; Letters; Literature; Machine Learning; Maps; Mass Spectrum Analysis; MeSH Thesaurus; Measurement; Measures; Metabolic; Metabolism; Methods; Modeling; Molecular; Molecular Structure; Nutritional; Organ; Pathway interactions; Performance; Play; Probability; Property; PubChem; PubMed; Public Domains; Research; Research Personnel; Role; Running; Sampling; Statistical Models; Structure; Surveys; Techniques; Testing; Time; Tissues; Training; Uncertainty; Validation; Work; annotation system; base; biomarker discovery; chemical standard; combinatorial; computerized tools; cost; dark matter; deep learning; design; drug development; drug discovery; experimental study; gastrointestinal system; gut microbiota; interest; large datasets; metabolome; metabolomics; microbiota; microbiota metabolites; neural network; novel; nutrition; open source; physical property; small molecule; tool

PROJECT SUMMARY/ABSTRACT Detecting and quantifying products of cellular metabolism using mass spectrometry (MS) has already shown great promise in biomarker discovery, nutritional analysis and other biomedical research fields. Despite recent advances in analysis techniques, our ability to interpret MS measurements remains limited. The biggest challenge in metabolomics is annotation, where measured compounds are assigned chemical identities. The annotation rates of current computational tools are low. For several surveyed metabolomics studies, less than 20% of all compounds are annotated. Another contributing factor to low annotation rates is the lack of systematic ways of designing a candidate set, a listing of putative chemical identities that can be used during annotation. Relying on exiting databases is problematic as considering the large combinatorial space of molecular arrangements, there are many biologically relevant compounds not catalogued in databases or documented in the literature. A secondary yet important challenge is interpreting the measurements to understand the metabolic activity of the sample under study. Current techniques are limited in utilizing complex information about the sample to elucidate metabolic activity. The goal of this project is to develop computational techniques to advance the interpretation of large-scale metabolomics measurements. To address current challenges, we propose to pursue three Aims: (1) Engineering candidate sets that enhance biological discovery. (2) Developing new techniques for annotation including using deep learning and incremental build out methods to recommend novel chemical structures that best explain the measurements. (3) Constructing probabilistic models to analyze metabolic activity. Each technique will be rigorously validated computationally and experimentally using chemical standards. Two detailed case studies on the intestinal microbiota will allow us to further validate our tools. Microbiota-derived metabolites have been detected in circulation and shown to engage host cellular pathways in organs and tissues beyond the digestive system. Identifying these metabolites is thus critical for understanding the metabolic function of the microbiota and elucidating their mechanisms. The complex test cases will challenge our techniques, provide feedback during development, and allow us to further disseminate our techniques. We will work closely with early adopters of our tools, as proposed in supporting letters, to further validate our tools and encourage wide adoption. All proposed tools will be open source and made accessible through the web. Our tools promise to change current practices in interpreting metabolomics data beyond what is currently possible with databases, current annotation tools, statistical and overrepresentation analysis, or combinations thereof. The use of machine learning and large data sets as proposed herein defines the most promising research direction in metabolomics analysis.

项目摘要/摘要 使用质谱（MS）检测和量化细胞代谢的产物已经显示 生物标志物发现，营养分析和其他生物医学研究领域的巨大希望。尽管最近 分析技术的进步，我们解释MS测量值的能力仍然有限。最大 代谢组学中的挑战是注释，其中测得的化合物分配了化学身份。这 当前计算工具的注释率很低。对于几项被调查的代谢组学研究，少于 所有化合物中有20％被注释。导致低注释率的另一个促成因素是缺乏系统的 设计候选套件的方式，可以在注释期间使用的推定化学身份清单。 依靠退出数据库是有问题的，因为考虑了分子的较大组合空间 安排，存在许多与数据库中未分类或记录的生物学相关化合物 文学。次要但重要的挑战是解释测量值以了解代谢 所研究样本的活性。当前的技术在利用有关的复杂信息时受到限制 样本以阐明代谢活性。 该项目的目的是开发计算技术来推动大规模解释 代谢组学测量。为了应对当前的挑战，我们建议追求三个目标：（1）工程 候选人设定了增强生物学发现的设定。 （2）开发注释的新技术，包括使用 深度学习和逐步建立方法，推荐新型化学结构，以最好的解释 测量。 （3）构建概率模型来分析代谢活性。每种技术都是 使用化学标准对计算和实验进行严格验证。两个详细的案例研究 肠道菌群将使我们能够进一步验证我们的工具。微生物群的代谢产物已经 在循环中检测到，并显示出在消化系统以外的器官和组织中的宿主细胞途径 系统。因此，识别这些代谢物对于理解菌群的代谢功能至关重要 并阐明其机制。复杂的测试用例将挑战我们的技术，提供反馈 在开发过程中，让我们进一步传播我们的技术。我们将与早期采用者紧密合作 我们的工具（在支持信件中提议）进一步验证了我们的工具并鼓励广泛采用。全部 建议的工具将是开源的，并可以通过网络访问。我们的工具有望改变当前 解释代谢组学数据的实践超出了数据库目前可能的方法，当前注释 工具，统计和代表分析或其组合。使用机器学习和大型 本文提出的数据集定义了代谢组学分析中最有希望的研究方向。