Epigenomic Data Integration with Graphs: a Beginning

I am currently working in Prof. Neil Wipat’s group at Newcastle University on the ARIES project. This involves working with large amounts of epigenomics data from the ARIES project itself, as well as with all sorts of related information from external data sources.

As well as the production of an integrated data set for ARIES’ nascent genome track browser, an indispensable tool for this type of data, we’re working on something else very exciting: graph data. Specifically, we’re trying out all sorts of visualizations for relevant data sets (including the ARIES data). Here’s one I’ve been playing with recently.

littlegraphThe pink nodes represent the median beta values for methylation sites along the human genome. The lighter the pink, the less likely this particular point on the chromosome is methylated, and vice versa. At a glance, the user can see all integrated beta values (and therefore all experiments containing methylation information) for a particular chromosome location. This is a small, gene-centric graph (the gene is in green) intended for people who would like to see an overview of known experimental results for a particular gene of interest.

This is just the start; we have lots of other visualization ideas, as well as lots of ideas for the creation of novel –and interesting– types of subgraphs. Our database is huge, and the hairball of the entire thing (or just of one chromosome) is likely to not be as informative as subgraphs like this created around a particular area of interest.

But we’re not just working on the export of interesting subgraphs from our graph database: my colleague Keith Flanagan has been developing a highly scalable and incredibly neat graph database built on MongoDB.

You’ll probably see a lot of pictures like the one above in the coming weeks on this blog, as we experiment with visualizations and views of our data. If you’re into epigenomics, and have always wanted to view your data in a particular way, please leave a comment below. I’d also love to hear your ideas about this particular visualization type. Your input would be most welcome.

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