Building the PTM map of the human genome through commensal computing

通过共生计算构建人类基因组的 PTM 图谱

• 批准号: BB/L005239/1
• 负责人: Andrew Jones
• 金额: $ 29.65万
• 依托单位: University of Liverpool
• 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%2FL005239%2F1
• 关键词: Building; PTM; map; human; genome

In recent years, the concept of "crowd-sourcing" has emerged as an exciting new paradigm for engaging large groups of people to solve a common task - one particularly high-profile example is Wikipedia. Crowd-sourcing can also be applied to data analysis - engaging many distributed machines to solve problem. This is a hugely exciting development for science, since massive data sets are now commonplace. It is a major unsolved problem as to how research organisations should fund the computing equipment to analyse the data explosion. The traditional route has been to purchase large farms of computers (clusters) in a dedicated location. This route is expensive to purchase, and expensive to keep up-to-date as a cluster of 10 computers purchased in 2003, would have a similar computing power as a modern desktop PC today available at fraction of the cost. An alternative model that has received attention recently is cloud computing, in which companies such as Amazon and Google provide access to massive compute farms hosted in distributed locations, on a pay-as-you-use basis. This model is attractive for high-powered, short term jobs, as purchasing 1 hour of analysis time on 1000 computers costs approximately the same as 1000 hours on 1 computer. This model does not ultimately save any cost in real terms though, since the service providers are aiming to profit from the cluster provision. The crowd-sourcing model aims to take advantage of the fact that devices containing CPUs are now ubiquitous - not just in PCs, but also in tablets and mobile phones. The vast majority of CPU time on these devices goes un-used. In this application, we are going to put the crowd-sourcing model to work to help annotate the human genome. The completion of the genome sequence was indeed an important scientific landmark, but the important part is now to study the functional units within the genome - the genes, and the protein(s) encoded by each gene. We wish to understand the basic function of each protein, what happens if a protein malfunctions, for example if the gene encoding it contains a mutation in some individuals, and how these proteins change in the cell. An important process that happens to proteins is post-translational modification. These are chemical changes that happen after the protein has been produced from the genetic code, altering the function- making it active or inactive, and influencing which other proteins it can interact with. The genetic code gives us no clues as to which sites in proteins can or will be modified with particular chemical groups, and so we must study these modifications experimentally. Mass spectrometry is widely used to study proteins on a very large scale, with a single experiment producing data on thousands of proteins at once. The computational analysis of the data is difficult to perform optimally, so most researchers often ignore data on modifications on proteins because they do not have access to sufficient computing power to analyse these properly. In this project, we are going to build a tool that runs in any browser platform (PC, tablet, phone etc), which will perform massive analysis of proteomics data. Our tool can be embedded in social media platforms, such as Facebook, so that the public can get personally involved in an important scientific endeavour, simply by having a near-silent application in existing browser windows they have open or by playing an interactive game we will build to map the problem to a solvable puzzle. This will provide us with a very large amount of CPU time for analysing the data fully, as well as engaging human brains to interpret challenging data. All results will be fed back into the genome annotation effort, so we can start to fully understand how every protein encoded in the human genome can be modified in different cell types. Other researchers will be able to mine this important data for their own studies in a wide variety of biological and biomedical contexts.

近年来，“众包”的概念作为一种令人兴奋的新模式出现了，它让大量的人来解决一个共同的任务——一个特别引人注目的例子是维基百科。众包也可以应用于数据分析——使用许多分布式机器来解决问题。对于科学来说，这是一个非常令人兴奋的发展，因为海量数据集现在已经很常见了。研究机构应该如何资助计算设备来分析数据爆炸，这是一个尚未解决的主要问题。传统的方法是在专用地点购买大型计算机群（集群）。这条路线的购买成本很高，而且保持更新的成本也很高，因为2003年购买的10台计算机组成的集群将具有与今天可用的现代台式PC相似的计算能力，而成本只是其中的一小部分。最近受到关注的另一种模式是云计算，亚马逊和b谷歌等公司在按使用付费的基础上，提供对托管在分布式位置的大规模计算场的访问。这种模式对于高性能的短期工作很有吸引力，因为在1000台计算机上购买1小时的分析时间与在1台计算机上购买1000小时的成本大致相同。然而，这种模式最终并没有节省任何实际成本，因为服务提供商的目标是从集群提供中获利。众包模式旨在利用包含cpu的设备现在无处不在的事实——不仅在个人电脑中，而且在平板电脑和手机中。这些设备上的绝大多数CPU时间都没有被使用。在这个应用程序中，我们将使用众包模式来帮助注释人类基因组。基因组序列的完成确实是一个重要的科学里程碑，但现在重要的部分是研究基因组内的功能单位——基因，以及每个基因编码的蛋白质。我们希望了解每种蛋白质的基本功能，如果蛋白质发生故障会发生什么，例如，如果编码它的基因在某些个体中包含突变，以及这些蛋白质如何在细胞中发生变化。蛋白质发生的一个重要过程是翻译后修饰。这些化学变化是在蛋白质从遗传密码中产生后发生的，改变了功能——使其活跃或不活跃，并影响它可以与哪些其他蛋白质相互作用。遗传密码没有给我们线索，告诉我们蛋白质中的哪些位点可以或将被特定的化学基团修饰，因此我们必须通过实验来研究这些修饰。质谱法被广泛用于大规模研究蛋白质，一次实验就能产生数千种蛋白质的数据。数据的计算分析很难达到最佳效果，因此大多数研究人员经常忽略蛋白质修饰的数据，因为他们没有足够的计算能力来正确地分析这些数据。在这个项目中，我们将构建一个可以在任何浏览器平台（PC，平板电脑，手机等）上运行的工具，它将对蛋白质组学数据进行大量分析。我们的工具可以嵌入社交媒体平台，比如Facebook，这样公众就可以亲自参与一项重要的科学努力，只需在他们打开的现有浏览器窗口中拥有一个近乎无声的应用程序，或者通过玩一个我们将构建的互动游戏来将问题映射为可解决的谜题。这将为我们提供大量的CPU时间来全面分析数据，以及让人类大脑来解释具有挑战性的数据。所有的结果都将反馈到基因组注释工作中，因此我们可以开始充分了解人类基因组中编码的每种蛋白质如何在不同的细胞类型中被修改。其他研究人员将能够在各种各样的生物学和生物医学背景下为他们自己的研究挖掘这些重要数据。

项目成果

Comparative qualitative phosphoproteomics analysis identifies shared phosphorylation motifs and associated biological processes in evolutionary divergent plants.

• DOI: 10.1016/j.jprot.2018.04.011
• 发表时间: 2018-06-15
• 期刊: Journal of proteomics
• 影响因子: 3.3
• 作者: Al-Momani S;Qi D;Ren Z;Jones AR
• 通讯作者: Jones AR

Proteomics Standards Initiative: Fifteen Years of Progress and Future Work.

• DOI: 10.1021/acs.jproteome.7b00370
• 发表时间: 2017-12-01
• 期刊: Journal of proteome research
• 影响因子: 4.4
• 作者: Deutsch EW;Orchard S;Binz PA;Bittremieux W;Eisenacher M;Hermjakob H;Kawano S;Lam H;Mayer G;Menschaert G;Perez-Riverol Y;Salek RM;Tabb DL;Tenzer S;Vizcaíno JA;Walzer M;Jones AR
• 通讯作者: Jones AR

Evaluation of Parameters for Confident Phosphorylation Site Localization Using an Orbitrap Fusion Tribrid Mass Spectrometer

• DOI: 10.1021/acs.jproteome.7b00337
• 发表时间: 2017-09-01
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Ferries, Samantha;Perkins, Simon;Eyers, Claire E.
• 通讯作者: Eyers, Claire E.

The mzIdentML Data Standard Version 1.2, Supporting Advances in Proteome Informatics.

• DOI: 10.1074/mcp.m117.068429
• 发表时间: 2017-07
• 期刊: Molecular & cellular proteomics : MCP
• 作者: Vizcaíno JA;Mayer G;Perkins S;Barsnes H;Vaudel M;Perez-Riverol Y;Ternent T;Uszkoreit J;Eisenacher M;Fischer L;Rappsilber J;Netz E;Walzer M;Kohlbacher O;Leitner A;Chalkley RJ;Ghali F;Martínez-Bartolomé S;Deutsch EW;Jones AR
• 通讯作者: Jones AR

Computational phosphoproteomics: from identification to localization.

• DOI: 10.1002/pmic.201400372
• 发表时间: 2015-03
• 期刊: PROTEOMICS
• 影响因子: 3.4
• 作者: Lee, Dave C. H.;Jones, Andrew R.;Hubbard, Simon J.
• 通讯作者: Hubbard, Simon J.