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

代谢谱分析提供了一个最完整的信息来源，准确地指出生理条件在某一时刻。因此，它为理解由全身性疾病引起的正常功能和功能障碍所涉及的细胞过程提供了一种工具，绘制了由遗传因素介导的生物体与环境的相互作用。鉴定被测样品中的小分子代谢物对于促进下游研究潜在机制至关重要。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，这些关于代谢组的参考知识还远远不够完整。代谢组学/代谢组学领域目前面临的主要挑战之一是，随着仪器灵敏度、动态范围、采集速度和质量准确性呈指数级增长，检测到的未知代谢物数量广泛且不断增加。了解代谢物的结构特性对于理解它们与生物靶点的相互作用至关重要，这种理解可以帮助开发与生物过程相互作用的方法，例如化学生物学。未知物的鉴定是代谢组学中尚未解决的主要问题，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，预测未知代谢物，并将包含已知和“预测未知”的代谢样本数据投射到生化网络和途径知识库中。我们将以最先进的结构解析方法为基础，并开发生物信息学推理方法，以预测结构适合代谢样本的生物学背景的可能性。该工具将在bbsrc资助的跨物种、跨平台代谢代谢数据库和代谢组学实验数据存储库中提供，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。这项工作将基于开放数据，并将作为开源软件和开放获取出版物发布，因此所有感兴趣的各方都可以访问。

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.