Building the PTM map of the human genome through commensal computing

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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.