A Machine-Learning Based Software Widget for Resolving Metabolite Identities

用于解析代谢物身份的基于机器学习的软件小部件

• 批准号: 9223450
• 负责人: KYONGBUM LEE
• 金额: $ 14.76万
• 依托单位: TUFTS UNIVERSITY MEDFORD
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9223450
• 关键词: Address; Algorithms; Attention; Automatic Data Processing; Bayesian Analysis; Biochemical Pathway; Biochemical Reaction; Biological; Cells; Chemicals; Classification; Complex; Computer Simulation; Computer software; Computing Methodologies; Data; Data Analyses; Data Analytics; Data Set; Databases; Diagnostic; Dissociation; Environment; Enzymes; Feedback; Focus Groups; Genes; Genome; Goals; Human; Knowledge; Libraries; Literature; Machine Learning; Manuals; Maps; Mass Spectrum Analysis; Measurement; Metabolic Pathway; Metabolism; Methodology; Methods; Nuclear Magnetic Resonance; Oranges; Organism; Outcome; Pathway Analysis; Pathway interactions; Pattern; Play; Process; Reproducibility; Research; Resolution; Sampling; Signal Transduction; Statistical Data Interpretation; Statistical Models; Surveys; Testing; Therapeutic Agents; Time; Uncertainty; Validation; Visual; base; biological systems; chemical standard; computerized tools; cost; database query; flexibility; functional outcomes; graphical user interface; heuristics; inhibitor/antagonist; instrument; ionization; mass spectrometer; member; metabolomics; novel; programs; protein expression; research study; small molecule; software development

Owing to recent technological advances in measurement platforms, it is now possible to simultaneously detect and characterize a very large number of metabolites covering a substantial fraction of the small molecules present in a biological sample. This presents an exciting opportunity to develop potentially transformative approaches to study cells and organisms. One major challenge in realizing this potential lies in processing and analyzing the data. A typical dataset from an untargeted experiment contains many of thousands of “features,” each of which could correspond to a unique metabolite. Analyzing such datasets to obtain meaningful biological information depends on reliably and efficiently resolving the chemical identities of the detected features. Currently, in silico fragmentation methods predict candidate metabolites that are scored and ranked based on how well the fragmentation explains the observed MS/MS spectrum, and on other factors influencing fragmentation such as bond dissociation energies and ionization conditions. Deciding which candidate metabolites is the best match for a particular feature in the context of the biological sample, however, is a daunting task. Extensive testing of candidate metabolites against chemical standards library may be prohibitive in terms of cost and efforts. We seek to develop software-enabled workflows centered on resolving metabolite identities. Our approach is to exploit knowledge of the biological context of a sample to identify the metabolites. Recognizing that the metabolites present in a sample result from enzyme-catalyzed biochemical reactions active in the corresponding biological system, we employ topological analysis and inference to best map the metabolites implied by the detected features to metabolic pathways that are feasible based on the genome(s) of cells in the biological system. Aim 1 develops a computational method based on Bayesian-inference to enhance candidate metabolite rankings that are obtained via in silico fragmentation analysis. Our method utilizes all available information (database lookups, in silico fragmentation analysis, and network/pathway context) to maximally inform and adjust the rankings. Aim 2 will build software widgets to implement the metabolite identification workflow within a data-analytics framework. As the analytics framework, we will use Orange, which allows the user to create interactive data analysis pipelines through a plug-and-play graphical user interface (GUI). Aim 3 will validate the computational method and software widget implementation. Experimental validation will utilize high-purity standards to confirm (or reject) the computationally assigned metabolite identities. Widget implementation will be evaluated through a focus group discussion with the widget users in the labs directed by the PIs. As project outcomes, we anticipate both a methodological advance in analyzing mass signature data as well as a suite of easily accessible software in the form of widgets.

由于测量平台的最新技术进步，现在可以同时检测 并表征覆盖小分子的相当大部分的大量代谢物 存在于生物样品中。这是一个令人兴奋的机会， 研究细胞和生物体的方法。实现这一潜力的一个主要挑战在于处理和 分析数据。来自非目标实验的典型数据集包含数千个“特征”， 每一种都可能对应一种独特的代谢物分析这些数据集以获得有意义的 生物信息依赖于可靠和有效地分辨被检测的生物的化学身份。 功能.目前，计算机碎片化方法预测候选代谢物， 基于碎片化对观察到的MS/MS谱的解释程度，以及其他影响 断裂，如键离解能和电离条件。决定哪个候选人 代谢物是生物样品背景下特定特征的最佳匹配，然而， 艰巨的任务根据化学标准品库对候选代谢物进行广泛测试可能是禁止的 in terms条款of cost成本and efforts努力.我们寻求开发以解析代谢物为中心的软件支持的工作流程 身份我们的方法是利用样本的生物背景知识来识别代谢物。 认识到样品中存在的代谢物是由酶催化的生化反应产生的 在相应的生物系统中，我们采用拓扑分析和推理来最好地映射 由检测到的特征暗示的代谢物与基于基因组可行的代谢途径 在生物系统中的细胞。目的1开发了一种基于贝叶斯推理的计算方法， 增强通过计算机碎片分析获得的候选代谢物排名。我们的方法 利用所有可用信息（数据库查找、计算机碎片分析和网络/通路 上下文），以最大限度地通知和调整排名。Aim 2将构建软件小部件来实现 在数据分析框架内的代谢物鉴定工作流程。作为分析框架，我们将使用 橙子，允许用户通过即插即用的图形化界面创建交互式数据分析管道 用户界面（GUI）。目标3将验证计算方法和软件小部件实现。 实验验证将使用高纯度标准品来确认（或拒绝）计算分配的 代谢物鉴别。将通过与小部件的焦点小组讨论来评估小部件的实现 实验室中的用户由PI指导。作为项目成果，我们预计， 分析大量签名数据以及一套以小部件形式提供的易于访问的软件。