Unifying metabolome and proteome informatics

统一代谢组和蛋白质组信息学

• 批准号: BB/L018616/1
• 负责人: Andrew Dowsey
• 金额: $ 18.39万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FL018616%2F1
• 关键词: Unifying; metabolome; proteome; informatics

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.

生物学家越来越希望了解基因、代谢物和蛋白质之间的复杂相互作用，这些基因、代谢物和蛋白质控制着每一个活着的有机体的功能。系统生物学领域的出现是为了克服传统简化论方法的缺陷，传统的简化论方法已经确定了构成要素本身和许多个体相互作用，但无法推断这些要素的系统如何协调行动和反应。系统生物学的应用非常广泛，因为它有望彻底改变我们对植物、动物和人类的健康过程的理解，以及它们如何在疾病中分解以及如何避免这种分解。系统生物学的方法通常从特定生物样本的“快照”开始。质谱学是一种普遍存在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个组成化合物的质量和数量来实现这一点。这通常不足以将样品完全分离出来，因此使用前一阶段的液体或气相色谱来提供初始分离。不同种类的蛋白质和代谢物需要不同的样品制备、电离和层析方法。所有这些都增加了不同种类的偏见和变异，使得推断化合物之间的联系变得极其困难，特别是如果化合物来自不同的类别。更糟糕的是，需要许多快照来捕捉正在研究的生物过程的不同角度，而仪器条件本身并不能随着时间的推移而完全重现。所有这些都导致系统生物学逐渐成为一门计算学科。研究蛋白质(蛋白质组学)和代谢物(代谢物)的全球模式的学术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，尽管这两种方法都适用于质谱学数据。因此，方法论和思想的交叉融合有望催生新的、有效的新分析方法。该项目团队分别参与了代谢组学和蛋白质组学的著名mzMatch和ProteoSuite信息学包的开发。它们是英国最活跃的学术代谢组和蛋白质组信息学软件包。因此，现在有一个及时的机会领导一项协调一致的努力，将代谢组学和蛋白质组学的信息学社区、方法和软件聚集在一起，以：(A)建立一个新的、强大的统一信息学工作流程，在这两个领域的方法进步中“借用力量”，超过其部分的总和，并具有一致的统计特性，使之能够最佳地整合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；以及(C)支持为代谢组学和蛋白质组学数据的最佳整合开发联合数据交换和报告标准。为了实现这一点，我们将首先将mzMatch集成到具有统一数据交换和报告的ProteoSuite中。这将使新的统一信息学管道的开发成为可能。关键是对这两种类型的组学使用相同的基本统计方法，只在所使用的生物模型中分析不同，从而支持向下游系统生物学建模提供连贯的交付。我们还将带头推行社区参与计划，鼓励社区长期参与统一信息学方法。这将包括一次为期一天的国际讲习班，首次吸收代谢组和蛋白质组信息学学科的领导小组参加，以培养统一这两个领域的共同心态。

项目成果

MetAssign: probabilistic annotation of metabolites from LC-MS data using a Bayesian clustering approach.

• DOI: 10.1093/bioinformatics/btu370
• 发表时间: 2014-10
• 期刊: Bioinformatics (Oxford, England)
• 作者: Daly R;Rogers S;Wandy J;Jankevics A;Burgess KE;Breitling R
• 通讯作者: Breitling R

Incorporating peak grouping information for alignment of multiple liquid chromatography-mass spectrometry datasets.

• DOI: 10.1093/bioinformatics/btv072
• 发表时间: 2015-06-15
• 期刊: Bioinformatics (Oxford, England)
• 作者: Wandy J;Daly R;Breitling R;Rogers S
• 通讯作者: Rogers S

Unsupervised Discovery and Comparison of Structural Families Across Multiple Samples in Untargeted Metabolomics.

• DOI: 10.1021/acs.analchem.7b01391
• 发表时间: 2017-07-18
• 期刊: Analytical chemistry
• 影响因子: 7.4
• 作者: van der Hooft JJJ;Wandy J;Young F;Padmanabhan S;Gerasimidis K;Burgess KEV;Barrett MP;Rogers S
• 通讯作者: Rogers S