Computational Techniques for Advancing Untargeted Metabolomics Analysis

项目总结/摘要 使用质谱法（MS）检测和定量细胞代谢产物已经表明， 在生物标记物发现、营养分析和其他生物医学研究领域具有巨大的前景。尽管最近 尽管分析技术的进步，我们解释MS测量结果的能力仍然有限。最大的 代谢组学的挑战是注释，其中测量的化合物被分配化学身份。的 当前计算工具的注释率低。对于几项调查的代谢组学研究， 所有化合物的20%被注释。另一个导致低注释率的因素是缺乏系统的 设计候选集的方法，可以在注释期间使用的推定化学身份的列表。 由于考虑到分子的大组合空间，依赖现有的数据库是有问题的。 在这些安排中，有许多生物学相关的化合物没有在数据库中编目或记录在 文学作品一个次要但重要的挑战是解释测量结果以了解代谢 研究样品的活性。目前的技术在利用关于细胞的复杂信息方面是有限的。 样品以阐明代谢活性。 这个项目的目标是发展计算技术，以促进大规模的解释， 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 增强生物学发现的候选集。(2)开发新的注释技术，包括使用 深度学习和渐进式构建方法，以推荐最能解释 测量. (3)构建概率模型以分析代谢活动。每项技术都将 通过计算和实验使用化学标准进行严格验证。两个详细的案例研究， 肠道微生物群将使我们能够进一步验证我们的工具。微生物群衍生的代谢物已经被 在循环中检测到，并显示参与消化系统以外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微生物群的代谢功能至关重要 并阐明其机制。复杂的测试案例将挑战我们的技术，提供反馈， 在开发过程中，并允许我们进一步传播我们的技术。我们将与早期采用者密切合作 我们的工具，如支持信中所建议的，以进一步验证我们的工具并鼓励广泛采用。所有 拟议的工具将是开放源码的，可通过网络获取。我们的工具承诺改变当前 解释代谢组学数据的实践超出了目前数据库的可能性，当前注释 工具、统计和代表性过高分析或其组合。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前途的研究方向。

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.