Tag Archives: Intermine

Video Tip of the Week: TargetMine, Data Warehouse for Drug Discovery

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

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).

Quick Links:

TargetMine: http://targetmine.mizuguchilab.org/ [note: their domain name has changed since the publications, this is the one that will persist.]

InterMine: http://intermine.github.io/intermine.org/

References:

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

Video Tip of the Week: InterMine for complex queries

We’ve been fans of InterMine for a long time. We did a tip-of-the-week in a while ago that highlighted ways that this software can be used to mine from big data projects of many types. The generic framework of InterMine can be customized for use at different projects–today I’ll include videos from the FlyMine installation and the YeastMine flavor–but you may find versions of this handy tool in many other places as well.

The first video is a broader overview of different types of things you can do–and although this is FlyMine, you’ll find similar behavior at the other Mines too.

This next video is more specific about a task that people need to accomplish–working with a list of genes. This example was recently produced by the YeastMine folks, but again this should work in a similar way across other Mines. You should also read the SGD blog post on it–Create, Analyze, Save: the Power of Gene Lists in YeastMine.

The other thing that I noticed about this framework is the effort of several of these model organism Mines to coordinate into this InterMOD structure. Although I am often wary of “one search to rule them all” sorts of efforts, there can be value in this as a central organizing principle as we keep adding more species genomes that may not have as well-developed communities and infrastructure to support them.

I certainly use a lot of query tools that are similar to these–like the UCSC Table Browser, and BioMartUniProt offers ways to build queries that’s different but conceptually similar. Using these interfaces you can construct some clever and complex ways to extract information out of data repositories.

Quick links:

InterMine: http://intermine.github.io/intermine.org/

FlyMine: http://www.flymine.org/

YeastMine: http://yeastmine.yeastgenome.org/

InterMOD: http://intermod.intermine.org

References:

Smith R.N., Aleksic J., Butano D., Carr A., Contrino S., Hu F., Lyne M., Lyne R., Kalderimis A. & Rutherford K. & (2012). InterMine: a flexible data warehouse system for the integration and analysis of heterogeneous biological data., Bioinformatics (Oxford, England), DOI:

Lyne R., Smith R., Rutherford K., Wakeling M., Varley A., Guillier F., Janssens H., Ji W., Mclaren P. & North P. & (2012). FlyMine: an integrated database for Drosophila and Anopheles genomics., Genome biology, PMID:

Balakrishnan R., Park J., Karra K., Hitz B.C., Binkley G., Hong E.L., Sullivan J., Micklem G. & Cherry J.M. (2012). YeastMine–an integrated data warehouse for Saccharomyces cerevisiae data as a multipurpose tool-kit., Database : the journal of biological databases and curation, PMID:

Sullivan J., Karra K., Moxon S.A.T., Vallejos A., Motenko H., Wong J.D., Aleksic J., Balakrishnan R., Binkley G. & Harris T. & (2013). InterMOD: integrated data and tools for the unification of model organism research., Scientific reports, 3 (1802) PMID:

Tip of the Week: InterMine for mining “big data”

Integrating large data sets for queries within–and across–various collections is one of the arenas that has lately been pretty active in bioinformatics. As more and more “big data” projects yield huge numbers of data points and data types, this is only becoming more necessary.  I love to browse data, but there are times when a large-scale customized query is what you’ll want to make some broader discoveries.

Right now there are a number of resources and interfaces that I turn to for structured and customized queries of data collections. The UCSC Table Browser, BioMart, Galaxy–these are the ones I have my hands on almost continuously. But there is another warehouse and interface system that we’re seeing more and more: InterMine.

My first real encounter with InterMine was for the modENCODE data. There’s some really terrific data flowing out of that project now (I talked a bit about that recently here), and the interface and storage system they are using is InterMine.

FlyMine was the initial impetus for the “Mine” system. Some years back FlyMine was created as a warehouse and query system for the increasing amounts of fly data that was coming from various projects. The goal was to have a system powerful enough for bioinformatics + super users, but also a friendly yet powerful interface for bench biologists to use.

The initial paper described the basic components: a user interface with 3 primary components: a Quick Search that’s great for browsing; a Template library that lets users access some pre-defined standard or likely query types that they can tweak for their needs; and a fully customizable Query Builder for the most advanced access. Since this paper development has continued, and there are other new and cool features present as well.

Another big goal of the FlyMine effort was to be able to deal with lists. One of the most common questions we still get in workshops is: “I have a list of _____.  What’s the best way to deal with that?” FlyMine–and the InterMines in general–help people to query and manage their explorations with lists of stuff.

The MyMine feature of the InterMines is also a nice component. You can create a login and store things you want to have repeated access to: queries, lists, etc.

There are other people using InterMine for their systems too–a recent paper on TargetMine, for “Gene Prioritization and Target Discovery” is available, and might appear as an upcoming tip! Jennifer did a tip on YeastMine from SGD once as well.

But what triggered me to do this tip is that a letter came from the RGD mailing list last week that said this:

Effective Friday, May 20th, 2011 the MCW BioMart tool will be retired by RGD and the MCW Proteomics Center.  For mining rat data, we have found that the RatMIne tool is easier to use, more flexible and incorporates more types of data than BioMart.  In addition, RatMine includes analysis tools not found in BioMart, giving RatMine users a single, intuitive interface for both obtaining and analyzing data.

So they are moving fully to InterMine and retiring the Rat BioMart, exclusively using RatMine at their installation. So this tip of the week will explore InterMine, RatMine, and some other Mines. That’s a lot of ground to cover–but it’s probably worth your time to know about InterMine as it becomes more broadly available.  It’s also important to understand how to query with the Mines if you want to bring the data to Galaxy for further analysis. If you visit Galaxy you’ll see that their “Get Data” section lets you access Mine tools–but you still need to know how to do the basic queries at the host site first.

Although this tip will touch on RatMine, the focus is the more general InterMine suite. RGD also said this in their notice:

For an overview of RatMine and how to use it, go to the RGD tutorial video, “An Introduction to the RatMine Database”, at http://rgd.mcw.edu/wg/home/rgd_rat_community_videos/an-introduction-to-the-ratmine-database2.  Alternatively, follow the “self-guided tour” of RatMine by clicking the “Take a tour” link at the top of any RatMine page.

To try out RatMine for yourself, go to http://ratmine.mcw.edu/ and get started with simplified data mining and analysis.

So if you want to have more specific information about using RatMine, be sure to check out their introduction.

Quick Links:

InterMine: http://intermine.org/

RatMine: http://ratmine.mcw.edu/

modENCODE: http://www.modencode.org/

Galaxy: http://usegalaxy.org/

Reference:
Lyne, R., Smith, R., Rutherford, K., Wakeling, M., Varley, A., Guillier, F., Janssens, H., Ji, W., Mclaren, P., North, P., Rana, D., Riley, T., Sullivan, J., Watkins, X., Woodbridge, M., Lilley, K., Russell, S., Ashburner, M., Mizuguchi, K., & Micklem, G. (2007). FlyMine: an integrated database for Drosophila and Anopheles genomics Genome Biology, 8 (7) DOI: 10.1186/gb-2007-8-7-r129

Tip of the Week: YeastMine


For this week’s tip I would like to take you over to the Saccharomyces Genome Database (SGD) & from there try out the beta release of YeastMine. YeastMine is based on the InterMine open source data warehouse system. We’ve featured other incarnations of InterMine, such as RatMine from RGD in this tip and modMine (associated with the modENCODE project) in this tip, so you’ve already seen some of its capabilities. The aspect that I want to focus on when we look at YeastMine is the interoperability of InterMine resources.

Mary noticed the beta YeastMine release notice first & mentioned it to me. When I got over to SGD, not only did I see the notice on YeastMine, but also noticed that they are now linking to GeneMANIA in some of their interaction resources. I think that’s cool because soon we will be releasing a new GeneMANIA-sponsored tutorial. I’ll head back over to SGD & maybe do a tip on that too, some other day, but for now enjoy today’s tip on how to make a gene list and then link to gene homolog information on FlyMine, the FlyBase/Drosophila version of InterMine.

Tip of the Week: Ratmine

Ratmine is a ‘data warehouse’ that allows the user to construct queries across different areas of biological knowledge from SNPs to Pathways. It’s developed by the people at RGD and uses Intermine a project developed for Flymine and as part of a project between RGD, SGD and ZFIN to implement Intermine for these databases and ” develop new methods of interoperability for cross-organism research.” We’ve mentioned Intermine before and it’s also used in ModEncode Intermine is going to have to be a subject of a later post I think :).

This tip is actually a video done by the RGD group and one of those gems I’ve found at SciVee in our attempts to integrate our tips at SciVee (which will be coming). We occasionally will highlight a short tutorial done by someone else here at our tips (occasionally) and since I’ve found this gem and just got back from vacation in Florida :)…
Btw, while you are at it, you might want to check out this interesting set of tutorials on biomedical ontologies.