Pathway-based Metabolomics Data Analysis

基于通路的代谢组学数据分析

• 批准号: 9222825
• 负责人: PETER D KARP
• 金额: $ 19.22万
• 依托单位: SRI INTERNATIONAL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-14 至 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9222825
• 关键词: Advertising; Algorithms; Antibiotics; Attention; Biochemical Pathway; Biochemical Reaction; Bioinformatics; Cognitive; Collaborations; Collection; Companions; Computer software; Data; Data Analyses; Data Set; Databases; Development; Exposure to; Graph; Human; International; Knowledge; Maps; Measures; Metabolic; Metabolic Pathway; Metabolism; Motivation; Organism; Pathway interactions; Pattern; Plants; Published Comment; Reaction; Request for Applications; Research; Scientist; Site; Software Tools; Source; Structure; Trees; Visit; Work; antimicrobial; base; insight; interest; metabolomics; pathogen; pathway tools; small molecule; software development; tool; web services; web site

7 Project Summary This application requests support for the development of new software tools for analyzing metabolomics data. The proposed tools will analyze metabolomics data in the context of metabolic pathways obtained from the BioCyc database collection. A key motivation underlying our work is that it is advantageous to interpret metabolomics data in the context of existing metabolic knowledge, and that pathway databases that accurately map the metabolism of thousands of organisms are a logical source of pathway information. Known metabolic networks provide a valuable framework for interpreting metabolomics data because patterns in metabolomics data often result from patterns among the biochemical reactions that interconnect metabolites. However, the large size and high connectivity of organism metabolic networks, and the large number of path- ways within most organisms, present significant cognitive barriers to this notion of using existing metabolic knowledge to analyze metabolomics data. A major motivation of this work is to lower these cognitive barri- ers by identifying subsets of the metabolic network for the user to focus their attention on. We will evaluate the following two proposed software tools by applying them to re-analyze existing metabolomics datasets previously generated by our group. Our first aim is to develop software to compute minimal pathway covering sets for a set of metabolites, that is, a minimal number of metabolic pathways that contain the metabolites within a metabolomics dataset submitted by a user. Our second aim is to develop software to compute reaction spanning trees for metabolite sets to eluci- date mechanistic relationships among metabolites. As a companion to the preceding pathway-centric algorithm, this algorithm will consider the full metabolism of the organism as a graph, and will compute a minimal tree that spans the metabolites in a metabolomics dataset. Both the minimal pathway-covering sets and the reaction spanning trees will be displayed using existing pathway display algorithms in our Pathway Tools software. Fur- thermore, our new software will be integrated into Pathway Tools, which is a highly used software package in bioinformatics.

7项目总结 该应用程序要求支持用于分析代谢组学数据的新软件工具的开发。 拟议的工具将在从以下方面获得的代谢途径的背景下分析代谢组学数据 BioCyc数据库集合。我们工作背后的一个关键动机是，它有利于解释 在现有代谢知识的背景下的代谢组学数据，以及准确地 绘制数千个生物体的新陈代谢图是途径信息的逻辑来源。已知代谢 网络为解释代谢组学数据提供了一个有价值的框架，因为代谢组学中的模式 数据通常来自相互连接代谢物的生化反应之间的模式。 然而，生物体代谢网络的巨大规模和高度连通性，以及大量的路径- 在大多数生物体内，利用现有新陈代谢的概念存在着显著的认知障碍 具有分析代谢组学数据的知识。这项工作的一个主要动机是降低这些认知障碍-- ERS通过识别供用户关注的代谢网络的子集来实现。我们将评估 遵循两个建议的软件工具，通过应用它们来重新分析现有的代谢组学数据集 由我们组生成。 我们的fi第一个目标是开发软件来计算一组代谢物的最小路径覆盖集，即， 在提交的代谢组学数据集中包含代谢物的最少数量的代谢途径 由用户创建。我们的第二个目标是开发软件来计算代谢物集合的反应生成树，以阐明- 代谢物之间的数据机制关系。作为前面的以路径为中心的算法的伙伴， 该算法将生物体的全部新陈代谢视为一个图，并将计算一棵最小树 这涵盖了代谢组学数据集中的代谢物。最小路覆盖集与反应 生成树将使用我们的路径工具软件中的现有路径显示算法来显示。毛皮- 此外，我们的新软件将集成到Path Tools中，这是一个在 生物信息学。