Standardised metabolite annotation workflows for enhanced biological interpretation in metabolomic data repositories

代谢物是一类在包括新陈代谢在内的生物系统中具有许多重要作用的小型生物化学物质。在包括微生物、植物和人类在内的许多不同的生物系统中研究代谢物，以通过增加作物产量、制造药物和了解人类如何衰老以及如何修改这一过程来改善我们的健康，从而使人类群体受益。对生物系统中代谢物的研究被称为代谢组学，其目的是研究数千种代谢物并调查它们所涉及的生物过程。这些研究不知道在研究开始时会检测到哪些代谢物，而是在研究期间通过化学识别每个代谢物的化学结构，例如确定代谢物是葡萄糖或脂质，将原始分析数据转化为更有生物学意义的数据。为了得出生物学结论，需要将数据转换为代谢物，没有代谢物鉴定将导致没有生物信息被报告。许多代谢学研究在数据仓库中向所有科学界提供。一个数据储存库位于英国，被称为代谢光，另一个大型数据储存库位于美国，被称为代谢组学工作台。这两个数据库包含近2000项对微生物、植物和哺乳动物(包括人类)进行的新陈代谢研究的信息。在截至的所有研究中，所有检测到的代谢物中有%没有被鉴定，没有分配给它们的化学结构，因此存在着无法从其获得生物学知识的大量信息。将执行的项目将构建一个计算工作流程，将化学结构分配给这两个数据储存库中已经存在的数据集中的大多数代谢物，并将其应用于存入储存库的所有未来数据集。完成后，两个数据库中现有的生物信息将大大扩大，并将使进一步的生物信息得以得到。

Metabolites are small biochemicals which have many important roles in biological systems including metabolism. Metabolites are studied in many different biological systems including microbes, plants and humans to benefit the human population through increased crop yields, the manufacture of drugs and in understanding how humans age and how the process can be modified to improve our health. The study of metabolites in biological systems is called metabolomics which has the aim to study thousands of metabolites and investigate the biological processes they are involved in. These studies do not know which metabolites will be detected at the start of the study and instead translate the raw analytical data to more biologically meaningful data during the study by chemically identifying the chemical structure of each metabolite, for example to define that the metabolite is glucose or a lipid. The conversion of the data to a metabolite is required so to derive biological conclusions, no metabolite identification will result in no biological information being reported. Many metabolomic studies are made available to all of the scientific community in data repositories. One data repository is located in the UK and is called MetaboLights and the other large data repository is located in the USA and is called the Metabolomics Workbench. These two data repositories contain information from nearly 2000 metabolomic studies performed on microbes, plants and mammals including humans. Across all studies up to 89% of all detected metabolites are not identified, have no chemical structure assigned to them and so significant levels of information from which biological knowledge can not be derived are present. The project to be performed will construct a computational workflow to assign chemical structures to the majority of metabolites in datasets already present in both of these data repositories and also to be applied to all future datasets deposited to the repositories. On completion, the biological information available in the two data repositories will be greatly expanded and will allow further biological information to be derived.