Tag Archives: Reactome


Video Tip of the Week: New Reactome Pathway Portal 3.0

new_reactomeThe Reactome pathway browser has long been a favorite of ours. We’ve watched it evolve over the years, and continue to appreciate the organization and features that it provides for exploring pathways and interactions across a range of species.

From the mailing list recently, I learned about a new version of the Reactome Pathway Portal, v3.0, that’s now available for everyone. I’ll post a piece of it here, but you can click through to their announcement for full details.

The Reactome team announces the release of our new pathway browser, accessible through our web site http://www.reactome.org/PathwayBrowser/ . The browser provides faster and more reliable navigation of our content and better access to our analysis tools. Improvements include improved and customizable color schemes, and the ability to search for terms within an individual pathway diagram using names, gene names and identifiers including identifiers for molecules hidden within complexes and molecule sets displayed in the diagram. The browser “back” and “forward” buttons allow the user to review every selection made to reach the current view. The level of detail shown in diagrams is zoom-dependent, so recurring small molecules like ATP and water fade out and colored overlays appear to identify subpathways appear as the user zooms out, providing a more legible and informative overview.

So their great foundation remains, but navigation is improved and other features have changed in this update. Delightfully, they have created a short intro video as well, and that becomes our Video Tip of the Week today. Note: there’s no audio with this, watch for the annotations.

I expect that there will be a paper to come with the new details, but I wasn’t able to locate it just yet. I’ll add back the reference when I find it (and yes, I do trawl the Advance Access section of NAR to see what’s coming in the next database issue pretty regularly….).

Quick links:

Reactome main site: http://www.reactome.org

Reactome new pathway browser: http://www.reactome.org/PathwayBrowser

Croft, D., Mundo, A., Haw, R., Milacic, M., Weiser, J., Wu, G., Caudy, M., Garapati, P., Gillespie, M., Kamdar, M., Jassal, B., Jupe, S., Matthews, L., May, B., Palatnik, S., Rothfels, K., Shamovsky, V., Song, H., Williams, M., Birney, E., Hermjakob, H., Stein, L., & D’Eustachio, P. (2013). The Reactome pathway knowledgebase Nucleic Acids Research, 42 (D1) DOI: 10.1093/nar/gkt1102

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/


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

Friday SNPpets

Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…

  • ROFL: RT @madkayaker: Bioinformatics professor: “We’re going to skip over some stuff so we can get to the cocaine…exercise.” Best last word ever. [Mary]
  • Best line in a press release-based story I evah saw: “Which pretty much proves that dinosaurs had small brains. Or lacked culinary skills.” From Genetic Analysis Reveals History, Evolution of an Ancient Delicacy — Morels hat tip @franknfoode [Mary]
  • RT @kdpru: NCBI’s updated Bookshelp is quite nice. HINT: Browse to find NCBI resource documentation by filtering onType=Documentation & Publisher=NCBI [Mary]
  • RT @Chris_Evelo: Reactome portal at WikiPathways now life! http://reactome.wikipathways.org [Mary]
  • Oh, man, some days twitter cracks me up. Quite the week for that: RT @lipscombe1: I got a warning from my employer’s computing dept as I googled ‘Naked Mole Rat genome’. Is there a less sexually appealing thing on Earth? [Mary]

Reactome Would Like Your Input – User Survey & T-Shirts

Last week some of you may have attended the Reactome webinar with Mary & I and heard about some of the great new features over there. I had already been exploring & testing the interface updates because I am currently updating our full tutorial on Reactome, but the webinar was a nice confirmation that I was hitting the ‘right stuff’. The webinar was recorded and should be released soon, so if you missed the webinar stay tuned & check it out when available. I thought it was interesting – it provides more background & theory on Reactome, plus some case studies. Our tutorial is more hands-on, step-by-step exploration of how to use many, but not all, of Reactome’s functions so the webinar and our tutorial really complement each other very well, in my opinion.

But I haven’t gotten to the t-shirts yet – my bad. Everyone has now had a week since the webinar to explore Reactome & the Reactome team is hoping that you are ready to share your thoughts, suggestions and ideas with them. As an ‘encouragement’ they are offering up 5 Reactome t-shirts to random survey participants. Here’s the call for survey takers that we got from Robin:

We appreciate your assistance in evaluating Reactome. Your responses will give us an indication of the effectiveness of the Reactome website and tools, and areas in which improvement could be made. We would like to know a bit about your background and research interests and welcome your written comments and suggestions.

The survey should take about 5 minutes to complete. The information you provide will never be made publicly available or shared with third parties.

Participation in the survey is optional. However, five lucky participants who complete the survey will receive a Reactome T-shirt.

You can access the survey at: https://www.surveymonkey.com/s/RV63355

Thank you for taking part.

Robin Haw

Robin Haw, PhD
Scientific Associate
Manager of Reactome Outreach

Ontario Institute for Cancer Research
MaRS Centre, South Tower
101 College Street, Suite 800
Toronto, Ontario, Canada M5G 0A3

I will definitely be taking the survey, so probably will Mary. I’ve got a few thoughts to share & a lot of complements on what they’ve done. Fingers crossed that they get a lot of feedback & find a few more t-shirts to pass out – wouldn’t it be cool if we could all be walking Reactome billboards? :)

Reactome Webinar coming up; Wed Feb 2

We were on the road last week doing workshops, so this is a few days old now. But if you aren’t on the GO Friends mailing list it’s possible it’s new for you. A quick word about GO Friends list: because so many tools rely on Gene Ontology and have some kind of GO components, there are quite a range of things that come over that mailing list. It’s not just for GO developers per se. You might want to check it out.

Anyway, what I wanted to focus on today is this notice about the upcoming Reactome webinar. There have  been BIG changes to the interface, but the underlying coolness and high quality of all those biological pathways remains intact, of course! Reactome is a tool we have loved for a long time, and we’ve coordinated with the Reactome folks around the next updates for our tutorial. We’re working on that update now.

If you want to learn more about Reactome and these new changes, there’s going to be a webinar soon. You have to register, and I’ll only give some of the details here. Head over to the GO Friends message link to see the rest.

The Ontario Genomics Institute (OGI) and the Ontario Institute for Cancer Research (OICR) are co-hosting a one hour web conference/webinar about the Reactome Pathway Database (http://www.reactome.org) – a freely available, manually-curated resource of core biological pathways. The Reactome database offers pathway data encapsulating areas of human biology ranging from basic pathways of metabolism to complex events such as GPCR signaling and apoptosis.

This follow up webinar will introduce the updated website with a more intuitive user interface and a new suite of data analysis tools. Learn to use this database through case studies from various research groups.

The presentation will be given by Dr. Robin Haw, Manager of Reactome Outreach, OICR, and will cover how to use the updated Reactome resource for:

• Browsing and searching pathway knowledge,
• Integrating network and pathway data,
• Using Pathway and Expression Analysis tools to analyze experimental datasets,
• Annotating experimental datasets with Reactome BioMart,
• Discovering network patterns related to cancer and other diseases using the Reactome Functional Interaction Network Cytoscape plug-in,
• Introducing use cases for Reactome data and analysis tools.


Go to the link for the registration details. I’ll be listening in (if we don’t schedule a workshop for that day!)

New Reactome Release Announced Today

Mary got an announcement about a new Reactome release today through the Gofriends mailing list. You can read the announcement, and I’ll share my brief analysis of the release here. I’ve been playing with the new functions this afternoon.

If you check out the release, or examine the image I’ve included, you’ll notice immediately that the homepage has been changed significantly. The homepage previously displayed a huge interactive map and a listing of superpathways under that, with navigation options along the top. The homepage is now much more an information source rather than an immediate entry into the pathways. The top navigation tabs are still available with much the same tools, except the SkyPainter tool is no longer listed. The main panel of the page now contains an ‘About’ area, an image of ‘the pathway of the month’ (which can be clicked for immediate access to the pathway display), and a News and Notes area. On the left is a new navigation area with a keyword search window, buttons to tools (Pathway browser, Pathway Analysis, Species Comparison and Expression Analysis), then multiple download options, a ‘try this’ area that takes you to the Pathway Analysis tool interface, and an area to add your comments about the new release.

The new Pathway Browser interface opens with a left panel listing pathways, and the right panel empty. A user clicks on any desired pathway & then selects the subpathway that they want to be displayed in the main panel. The main panel displays a zoomed in area of the pathway. Additionally a tiny panel opens between the list and the pathway panel with an overview image of the full pathway. This can be used to navigate to the desired area of the pathway in the larger, zoomed-in panel. There are also a variety of analysis, annotation and uploading functions available from a tab in the left panel, and a details report can be opened as a lower (4th) panel by clicking a small arrow head.

The Pathway Analysis tool appears to have replaced the SkyPainter tool. Like the SkyPainter it takes a list of IDs & analyzes them for associated pathways. While the SkyPainter tool color-coded pathway diagrams with over represented pathways and listed statistically over represented pathways, the analysis tool provides a list of all associated pathways for each gene or protein in the users ID list. From the report users can link to the Pathway Browser displays to further explore the individual pathways.

The Compare Species and Expression Analysis tools allow users to either compare pathways across two species, or to map expression data onto pathway images. Both have example datasets that you can click & run. Other powerful tools, such as the advanced search, BioMart search function, PathFinder and Small Molecule Search all appear to be available and largely unchanged, which is cool.

We’ll of course be updating our full Reactome tutorial to reflect the changes in detail – after we give the release a bit of time to be sure everything is stable. :)

Tip of the Week: PathCase for pathway data

We spend a lot of time exploring genomic data, variations, and annotations. But of course a linear perspective on the genes and sequences is not the only way to examine the data. Understanding the pathways in which genes and molecular entities interact is crucial to understanding systems biology.

There are a number of tools which can help you to visualize and explore this kind of data. KEGG is one of the most venerable tools in bioinformatics, BioCyc is well known and used, Reactome is one of our favorites. Recently NCBI BioSystems has come along, and the BioModels tool at EBI provides more data of this type as well. Pathway Interaction Database is another place to try. What you’ll find is that each one has different emphasis, species focus or data sets available, and different tools to use to graphically display the databases. The ways to customize or interact with the data will vary as well. So you may need to try several to find the one you want for your purposes.

But for today’s tip of the week I will highlight PathCase, a Pathways Database System from Case Western Reserve University. This is a  tool I’ve  had my eye on for a number of years, and they continue to add new features and data sets to their visualization and search interface which are very nicely done.

PathCase offers you several ways to browse and search for pathways, processes, organisms, and also molecular entities (such as ATP, ions, etc) as well as genes and proteins. It’s all integrated into the system, so when you find an item of interest you can move to the other related pieces.  For example, from the Pathways you can find genes and learn more about the genes. From genes you can load the pathways in which they participate.

When you have the pathway graphics loaded, you can interact with that pathway by clicking, dragging, re-organizing and more. Right-clicking offers more details about the items and ways to visualize the data. One option I didn’t have time to show in the movie is that you can use the H20/CO2 box to load up pathways that are linked to the one you are looking at and load those up, going even further along any route that you might be interested in. Here’s just a quick sample of that: from the NARS2 gene page I loaded the alanine pathway, and then added the fatty acid metabolism pathway. Now I can explore both of them with all the standard PathCase tools and understand many of their relationships. Once you start exploring these pathways you be amazed at how complex visualizations are possible.

So if you are interested in biological pathways, exploring them and representing them, check out PathCase.

PathCase site: http://nashua.cwru.edu/pathwaysweb/

Elliott, B., Kirac, M., Cakmak, A., Yavas, G., Mayes, S., Cheng, E., Wang, Y., Gupta, C., Ozsoyoglu, G., & Meral Ozsoyoglu, Z. (2008). PathCase: pathways database system Bioinformatics, 24 (21), 2526-2533 DOI: 10.1093/bioinformatics/btn459

Friday SNPpets

Welcome to our Friday feature link dump: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…


Guest Post: New features at CTD – Allan Peter Davis

This next post in our continuing semi-regular Guest Post series is from Allen Peter Davis, of Comparative Toxicogenomics Database (CTD) at Mount Desert Island Biological Laboratory (MDIBL). If you are a provider of a free, publicly available genomics tool, database or resource and would like to convey something to users on our guest post feature, please feel free to contact us at wlathe AT openhelix DOT com.

The Comparative Toxicogenomics Database (CTD) is a free, public resource that promotes understanding about the effects of environmental chemicals on human health.  Since Trey’s original Tip of the Week about CTD, we’ve added many new features we’d like to highlight.

* The redesigned CTD homepage makes navigation easier and more intuitive.  Check out the keyword quick search box on every page, and try the “All” setting to see the scope of information available at CTD.

* A new Data Status page uses tag clouds to display the updated content for that month.

* We are particularly pleased to announce new statistical analyses of CTD data.  Chemical pages now feature enriched Gene Ontology (GO) terms, garnered from the genes that interact with a chemical.  In this release, CTD connects over 5,000 enriched GO terms to more than 4,500 chemicals.  As well, now our inferred chemical-disease relationships are also statistically scored and ranked.  Both new features will help users explore and generate testable hypotheses about the biological effects of chemicals.

* GeneComps and ChemComps discover genes or chemicals with a similar toxicogenomic profile to your molecule of interest.  Learn more about this feature in our recent publication.

* Reactome data are now also included with KEGG, for a more comprehensive view of pathways affected by chemicals.

VennViewer and MyGeneVenn are new tools that compare datasets for chemicals, diseases, or genes (including your own gene list) using Venn diagrams to discover shared and unique information.  These two visualization tools are a nice accompaniment to our original Batch Query tool for meta-analysis.

* The FAQ section under the “Help” menu provides examples of how to maximize your experience with CTD.

* Download our Resource Guide (pdf link) to keep as a handy reference card for CTD.

From the homepage, you can also subscribe to our monthly email newsletter to keep current with CTD’s growing content and features.  You can always contact us to request curation of your favorite chemical or paper.  And with our new “Author Alert” email program, we’ll even contact you to let you know when we’ve curated data from one of your publications in CTD.

We strive to be the best possible resource of chemical-gene-disease networks for the biological community, so feedback and input from users are of great importance to us.

- Allan Peter Davis

Tip of the Week: WAVe, Web Analysis of the Variome

Today’s Tip of the Week is a short introduction to WAVe, or the Web Analysis of the Variome. The tool was recently introduced to us, and I’ve found it a welcome introduction to the tools available to the researcher to analyze human variation. This is apropos considering the recent paper we’ve been discussing on the clinical assessment of a personal genome (here, here and here) and that papers implications for personalized medicine and the use of online variation resources. WAVe also has introduced me to some additional tools I’ve either not been aware of, or haven’t used, which might be of use such as: LOVD (Leiden Open Variation Database), QuExT (Query Expansion Tool, also from the same developers as WAVe), and others. Of course there are also database information pulled in from Ensembl, Reactome, KEGG, InterPro, PDB, UniProt, NCBI and many others. Take some time to check it out.