Closing the gaps in metabolomics - Identifying unknown metabolites and mapping onto biochemical pathways

缩小代谢组学的差距 - 识别未知代谢物并绘制生化途径

• 批准号: BB/K004301/1
• 负责人: Christoph Steinbeck
• 金额: $ 15.15万
• 依托单位: European Bioinformatics Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FK004301%2F1
• 关键词: Closing; gaps; metabolomics; Identifying; unknown

Metabolic profiling provides one of the most complete sources of information for pinpointing physiological conditions at a moment in time. It therefore provides a vehicle for understanding the cellular processes involved in both normal functioning and dysfunctions caused by systemic diseases, mapping the interactions of the organism with its environment as mediated by genetic factors. Identification of the small molecular metabolites in the measured samples is essential to facilitate downstream research into underlying mechanisms.Current techniques for analysis rely on the identification of known metabolites which are already mapped to reference metabolic networks and pathways, yet, such reference knowledge about the metabolome is far from complete. One of the major current challenges for the metabolomics/metabonomics community is the extensive and increasing number of unknown metabolites detected, as instrumental sensitivity, dynamic range, acquisition speed and mass accuracy increase exponentially. Knowledge about the structural identity of metabolites is essential to understand their interaction with their biological targets - an understanding that can then aid the development of methods to interact with biological processes in, for example, chemical biology. Identification of unknowns is a major unsolved problem in metabolomics, requiring time consuming further experimental work. We are proposing to develop a tool which addresses this gap, predicting unknown metabolites and projecting metabolic sample data containing knowns and "predicted unknowns" onto biochemical network and pathway knowledge bases. We will build on state of the art methods for structure elucidation and develop methods for bioinformatics reasoning about the likelihood that predicted structures fit into the biological context of the metabolic sample. This tool will be made available within the context of the BBSRC-funded cross-species, cross-platform MetaboLights database and repository for metabolomics experimental data, and will additionally be provided as open source, thus benefiting the broadest number of academic and industrial researchers. The work will be based on open data and will be published as open source software and open access publications, thus will be accessible to all interested parties.

代谢分析提供了一个最完整的信息来源，以查明在某个时刻的生理条件。因此，它提供了一种工具，用于了解系统性疾病引起的正常功能和功能障碍所涉及的细胞过程，绘制遗传因素介导的生物体与其环境的相互作用。目前的分析技术依赖于已知代谢物的鉴定，这些代谢物已经被映射到参考代谢网络和途径，然而，关于代谢组学的参考知识还远远不够。代谢组学/代谢组学社区当前面临的主要挑战之一是检测到的未知代谢物的数量广泛且不断增加，因为仪器灵敏度、动态范围、采集速度和质量准确度呈指数级增加。了解代谢物的结构特性对于了解它们与其生物靶点的相互作用至关重要-这种了解可以帮助开发与化学生物学等生物过程相互作用的方法。未知物的鉴定是代谢组学中一个主要的未解决的问题，需要耗时的进一步实验工作。我们建议开发一种工具来解决这一差距，预测未知的代谢物，并将包含已知和“预测未知”的代谢样本数据投影到生化网络和途径知识库上。我们将建立在最先进的结构解析方法的基础上，并开发用于生物信息学推理的方法，以确定预测的结构是否符合代谢样品的生物学背景。该工具将在BBSRC资助的跨物种，跨平台MetaboLights数据库和代谢组学实验数据库的背景下提供，并将作为开源提供，从而使最广泛的学术和工业研究人员受益。这项工作将以开放数据为基础，并将作为开源软件和开放获取出版物出版，因此所有感兴趣的各方都可以访问。

项目成果

MassCascade: Visual Programming for LC-MS Data Processing in Metabolomics.

• DOI: 10.1002/minf.201400016
• 发表时间: 2014-04
• 期刊: Molecular informatics
• 影响因子: 3.6
• 作者: Beisken S;Earll M;Portwood D;Seymour M;Steinbeck C
• 通讯作者: Steinbeck C

Building blocks for automated elucidation of metabolites: natural product-likeness for candidate ranking.

• DOI: 10.1186/1471-2105-15-234
• 发表时间: 2014-07-05
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Jayaseelan KV;Steinbeck C
• 通讯作者: Steinbeck C