Browsing around genomic regions, layering on lots of associated data, and beginning to explore new data types I might come across are things that really fire up my brain. For me, visualization is key to forming new ideas about the relationships between genomic features and patterns of data. But frequently I want to take this to the next step–asking where else these patterns appear, how many other instances of this situation are there in a data set, and maybe adding additional complexity to the problem and refine the quest. This is not always easy to do with primarily visual software tools. This is when I turn to tools like the UCSC Table Browser, BioMart, and InterMine to handle some list of genes, or regions, or features.
We’ve touched on all of these before–sometimes with full tutorial suites (UCSC, BioMart), and sometimes as a Tip of the Week, InterMine and InterMine for complex queries. Learning about the foundations of these tools will let you use various versions or flavors of them at other sites. I love to see tools that are re-used for different topics when that’s possible, rather than building a whole new system. There are ModENCODE, rat, yeast mines, and more. This week’s tip is about one of those others–TargetMine is built on the InterMine foundation, with a specific focus on prioritizing candidate genes for pharmaceutical interventions. From their site overview, I’ll add this description they use:
TargetMine is an integrated data warehouse system which has been primarily developed for the purpose of target prioritisation and early stage drug discovery.
For more details about their framework and philosophy, you should see their papers (linked below). The earlier one sets out the rationale, the data types, and the data sources they are incorporating. They also establish their place in the ecosystem of other databases in this arena, which helps you to understand their role. But you should see the next paper for a really good grasp of how their candidate prioritization work with the “Integrated Pathway Clusters” concept they’ve added. They combined data from KEGG, Reactome, and NCI’s PID collections to enhance the features of their data warehouse system.
This week’s Video Tip of the Week highlights one of the tutorial movies that the TargetMine team provides. There’s no spoken audio with it, but the captions that help you to understand what’s going on are in English. I followed along on a browser with their example–they have a sample list to simply click on, and you can see various enrichments of the sets–pathways, Gene Ontology, Disease Ontology, InterPro, CATH, and compounds. They call these the “biological themes” and I find them really useful. You can create new lists from these theme collections. They also illustrate the “template” option–pre-defined queries with typical features people may wish to search. The example shows how to go from the list of genes you had to pathways–but there are other templates as well.
Another section of the video has an example of a custom query with the Query Builder. They ask for structural information for proteins targeted by acetaminophen. It’s a nice example of how to go from a compound to protein structure–a question I’ve seen come up before in discussion threads.
In their more recent paper (also below), they have some case studies that illustrate the concepts of prioritizing targets for different disease situations with their system. They also expand on the functions with additional software to explore the pathways: http://targetmine.mizuguchilab.org/pathclust/ .
So have a look at the features of TargetMine for prioritization of candidate genes. I think the numerous “themes” are a really useful way to assess lists of genes (or whatever you are starting with).
TargetMine: http://targetmine.mizuguchilab.org/ [note: their domain name has changed since the publications, this is the one that will persist.]
Chen, Y., Tripathi, L., & Mizuguchi, K. (2011). TargetMine, an Integrated Data Warehouse for Candidate Gene Prioritisation and Target Discovery PLoS ONE, 6 (3) DOI: 10.1371/journal.pone.0017844
Chen, Y., Tripathi, L., Dessailly, B., Nyström-Persson, J., Ahmad, S., & Mizuguchi, K. (2014). Integrated Pathway Clusters with Coherent Biological Themes for Target Prioritisation PLoS ONE, 9 (6) DOI: 10.1371/journal.pone.0099030
Kalderimis A., R. Lyne, D. Butano, S. Contrino, M. Lyne, J. Heimbach, F. Hu, R. Smith, R. Stěpán, J. Sullivan & G. Micklem & (2014). InterMine: extensive web services for modern biology, Nucleic Acids Research, 42 (W1) W468-W472. DOI: http://dx.doi.org/10.1093/nar/gku301