Unifying metabolome and proteome informatics

生物学家越来越希望了解控制每一个生物体功能的基因、代谢物和蛋白质组成部分之间复杂的相互作用。系统生物学领域的出现是为了克服传统还原论方法的缺陷，传统还原论方法已经确定了构建块本身和许多个体相互作用，但无法推断这些块的系统如何一致行动和反应。系统生物学的应用是广泛的，因为它有望彻底改变我们对植物、动物和人类健康过程的理解，以及它们如何在疾病下分解以及如何避免这种分解。通常，系统生物学方法从一个特定生物样本的“快照”开始。质谱法是一种普遍的获取样品快照的技术，它通过电离样品，然后根据产生的电荷测量每种成分化合物的质量和数量。这通常不足以完全分离出样品，因此使用液相或气相色谱的前一相来提供初始分离。不同种类的蛋白质和代谢物需要不同的样品制备、电离和色谱方法。这些都增加了不同种类的偏差和变异，这使得推断化合物之间的联系变得极具挑战性，特别是当化合物来自不同的类别时。更糟糕的是，需要许多快照来捕捉被调查的生物过程的不同“角度”，而且仪器条件本身并不能随着时间的推移完全重现。所有这些都使系统生物学逐渐成为一门计算学科。研究蛋白质的整体模式（“蛋白质组学”）和代谢物（“代谢物”）的学科广泛地起源于不同的领域，因此两者之间几乎没有协同作用。计算方面也是如此，尽管事实上两者都应用于质谱数据。因此，方法和思想的交叉融合有可能产生新的、有效的分析方法。项目组分别参与开发了著名的代谢组学信息包mzMatch和蛋白质组学信息包ProteoSuite。它们是英国最活跃的学术代谢组学和蛋白质组信息学软件包。因此，有一个及时的机会来领导一个协调一致的努力，将信息学社区、代谢组学和蛋白质组学的方法和软件结合在一起，以：(a)建立一个新的、强大的统一信息学工作流程，在两个领域的方法进步中“借鉴力量”，大于其各部分的总和，并具有一致的统计特性，能够优化集成到系统生物学研究中；(b)支持代谢组学和蛋白质组学领域之间的跨学科合作、新的理解和流动性；(c)支持制定联合数据交换和报告标准，以实现代谢组学和蛋白质组学数据的最佳整合。为了实现这一目标，我们将首先将mzMatch集成到ProteoSuite中，实现统一的数据交换和报告。这将使新的统一信息学管道的开发成为可能。关键是对两种类型的组学使用相同的基础统计方法，分析仅在所使用的生物模型中有所不同，从而支持下游系统生物学建模的连贯传递。我们亦会率先推行一项社区参与计划，鼓励社区长期参与统一资讯方法。这将包括一个为期一天的国际研讨会，首次吸引来自代谢组学和蛋白质组学学科的领导小组，以促进统一这两个领域的共同思维模式。

Biologists are increasing wishing to understand the complex interactions between the building blocks of genes, metabolites and proteins that control the function of every living organism. The field of systems biology has emerged to overcome the deficiencies of the traditional reductionist approach, which has identified the building blocks themselves and many of the individual interactions but has not been able to deduce how systems of these blocks act and react in unison. The application of systems biology is widespread, as it promises to revolutionise our understanding of healthy processes in plants, animals and humans, as well as how they break down under disease and how this breakdown can be averted.Often the systems biology approach starts with a 'snapshot' of a particular biological sample. Mass spectrometry is a pervasive technique for gaining a snapshot of a sample, and it does this by ionising the sample and then measuring each constituent compound's mass and quantity based on the resulting charge. This is often not enough to separate out the sample fully and therefore a preceding phase of liquid or gas chromatography is used to provide an initial separation. Classes of protein and metabolite require different sample preparation, ionisation and chromatography approaches. These all add different kinds of biases and variation which make it extremely challenging to infer links between compounds, especially if the compounds are from different classes. To make matters worse, many snapshots are needed to capture different 'angles' of the biological process under investigation, and the instrumental conditions themselves are not entirely reproducible over time. All this has led systems biology to become a progressively computational discipline.The academic disciplines for studying global patterns of proteins ('proteomics') and metabolites ('metabolites') have broadly originated from different fields, and therefore there is little synergy between the two. This is also the case for the computational aspect, despite the fact both are applied to mass spectrometry data. Cross-fertilisation of methodology and ideas therefore has the prospect of seeding novel, effective new approaches of analysis. The project team is involved in the development of the prominent mzMatch and ProteoSuite informatics packages for metabolomics and proteomics respectively. They are the most actively developed academic metabolome and proteome informatics packages in the UK. Therefore there is a timely opportunity to lead a concerted effort bringing together the informatics community, methodology and software for metabolomics and proteomics to: (a) Establish a new, powerful unified informatics workflow 'borrowing strength' in methodology advancements across both fields, greater than the sum of its parts and with coherent statistical properties enabling optimal integration into systems biology research; (b) Underpin cross-disciplinary collaborations, new understanding and mobility between metabolomics and proteomics fields; and (c) Support development of joint data exchange and reporting standards for optimal integration of metabolomics and proteomics data. To achieve this, we will first integrate mzMatch into ProteoSuite with unified data exchange and reporting. This will then enable the development of the novel unified informatics pipeline. The key is to use the same underlying statistical methodology for both types of omics, with analysis differing only in biological models utilised, thus underpinning coherent delivery to downstream systems biology modelling. We will also spearhead a programme of community involvement to encourage long-term community participation in the unified informatics approach. This will include an international one-day workshop drawing in leading groups from both metabolome and proteome informatics disciplines for the first time, in order to foster a shared mind-set towards unifying the two fields.