Category Archives: Tip of the Week


Video Tip of the Week: iDigBio for access to historical specimens and more

idigbio_logoUsually for Thanksgiving week posting is light. In the past, we’ve all done turkey breeding and genomics, cranberry genome, and some people have included apples, potatoes, and more. But another key aspect of the holiday is to remember the past and thank those who came before. And as I was watching this video that crossed my desk, I was thinking about our really ancient ancestors and their companions on the planet. And those who have created and preserved specimens in museum collections documenting the planet’s history. Increasingly those things are being digitized when appropriate, and sometimes even sequenced now when possible.

Some people think of these types of things as “stamp collecting”. But I think we are going to find these resources increasingly valuable as more folks can access and explore them.

So I want to thank folks who have been collecting, curating, and documenting our planet and its species over the millennia. This week I highlight iDigBio and their specimen portal that will provide access to these important curated collections for researchers to take into the future.

This week’s video is only one of the presentations at their iDigBio Summit V. They have a great Vimeo channel with other talks, but this one on Digitized Data in Biodiversity Research caught my attention first. It talks about work in wrangling information in biodiversity, making it available for further work with tools and workflows. It’s a nice overview of the goals and umbrella of topics they are interested in. And also mentions PhyloJIVE, which can integrate phylogeny data and available specimens. That’s a very cool bridge, it seems to me.

iDigBio Summit V: Digitized Data in Biodiversity Research from iDigBio on Vimeo.

So as you are eating and drinking a diverse range of species over the next few days, thank evolution. And thank folks who have been monitoring biodiversity in the past, and moving the studies into the future, on this pale blue dot we share.

Quick links:

iDigBio Portal:



Nelson, G., Sweeney, P., Wallace, L., Rabeler, R., Allard, D., Brown, H., Carter, J., Denslow, M., Ellwood, E., Germain-Aubrey, C., Gilbert, E., Gillespie, E., Goertzen, L., Legler, B., Marchant, D., Marsico, T., Morris, A., Murrell, Z., Nazaire, M., Neefus, C., Oberreiter, S., Paul, D., Ruhfel, B., Sasek, T., Shaw, J., Soltis, P., Watson, K., Weeks, A., & Mast, A. (2015). Digitization Workflows for Flat Sheets and Packets of Plants, Algae, and Fungi Applications in Plant Sciences, 3 (9) DOI: 10.3732/apps.1500065

Nelson, G., Paul, D., Riccardi, G., & Mast, A. (2012). Five task clusters that enable efficient and effective digitization of biological collections ZooKeys, 209, 19-45 DOI: 10.3897/zookeys.209.3135

Jolley-Rogers, G., Varghese, T., Harvey, P., dos Remedios, N., & Miller, J. (2014). PhyloJIVE: Integrating biodiversity data with the Tree of Life Bioinformatics, 30 (9), 1308-1309 DOI: 10.1093/bioinformatics/btu024


Video Tip of the Week: Explore Gene Pages at NCBI with Variation and Expression Information

NCBI has produced some of the most in-depth and reliable bioinformatic tools, in large part because they’ve been building them since the earliest days of the genomics era. ncbi_logo_black I once noted that I remember the oldest web interface, because it was one of the first places that I went for computational tools back in the day. Check out my post here with some of their older interfaces. I remember all of them.

But they don’t rest on their laurels. NCBI teams are always adding new tools, new features, and new data. Sometimes, though, I think that people take them for granted. Or they only keep re-visiting things they know. So recently they asked me to do a walk-through video about how I use the tools, so that I can show people ways they can go further than they might realize. This week’s Video Tip of the Week shows how I can add and explore a lot of data on a Gene page, to examine additional features: variations and expression data, right in the sequence viewer.  A lot of people may not even be aware these tracks exist.

This video provides a walk-through of how to explore variation data and expression data from Gene pages, using the sequence viewer that’s embedded right on the page. Enhance your understanding of your genes of interest quickly using these additional track options.

I hope this demonstrates how you can add more information to your genes of interest from gene pages, which you might already use. But now you can use them better. You can take advantage of the great depth at NCBI, while staying up-to-date on the tools.

Speaking of staying up-to-date, which is a big need in this field: you should definitely keep an eye out for new features from NCBI. My favorite way is the announcement mailing list, because it has details of new stuff, upcoming webinars, data releases, etc. But you can also watch their blog and twitter for new information all the time. They’ve been doing a lot of outreach–webinars, quick videos, and more. You should sign up to be notified, so you can stay current with the best data and tools. Check out their learning/webinars pages to see what I mean.

We are planning another video, and we’d love any feedback you have on this one.

Quick links:

NCBI Gene (subject of the video):

NCBI-announce mailing list:

NCBI Insights blog:

NCBI twitter: @NCBI

Learn (see webinars link):

NCBI YouTube channel:


NCBI Resource Coordinators (2014). Database resources of the National Center for Biotechnology Information Nucleic Acids Research, 43 (D1) DOI: 10.1093/nar/gku1130

Disclosure: This video was sponsored and completed under contract to NCBI. 


Video Tip of the Week: UCSC Table Browser and Custom Tracks

UCSC IntroThis week’s video tip is longer than usual. But if you want to dig deeper into all the data that you know is coming in to the UCSC Genome Browser, you want to use the Table Browser. If you’ve only used the genome browser interface, you are missing a lot of opportunity to mine for great data.

The UCSC Genome Browser folks have been continuously adding new features and data over the years since they’ve been sponsoring our free training materials. But the look of the Table Browser hadn’t changed all that much. However, with the move to the new hg38 assembly as the default human genome, it was a good time for us to update the shots of the training materials for the Table Browser. It also gave us the chance to include track hubs, which we hadn’t covered in the previous version. And we also touch briefly on Genome Browser in a Box.

As before, we have a recorded video. And we have also included updated slides that you can use to train others. And our exercises are there for more practice, too.


We’ve also mentioned before that we have a handy paper that also can help people to get up to speed on UCSC features. Our Current Protocols paper is now un-paywalled in PMC, so if you want to supplement the video with this other piece, there’s a bit more detail on how to do hubs of your own. But you should also see the specific papers on that. And there’s more guidance in this video tip from before.

So check out the new materials for the advanced topics. And see the new preprint (linked below) for more details and upcoming features as well.

Quick links:

UCSC Genome Browser homepage:

Training suite:


Rosenbloom, K., Armstrong, J., Barber, G., Casper, J., Clawson, H., Diekhans, M., Dreszer, T., Fujita, P., Guruvadoo, L., Haeussler, M., Harte, R., Heitner, S., Hickey, G., Hinrichs, A., Hubley, R., Karolchik, D., Learned, K., Lee, B., Li, C., Miga, K., Nguyen, N., Paten, B., Raney, B., Smit, A., Speir, M., Zweig, A., Haussler, D., Kuhn, R., & Kent, W. (2014). The UCSC Genome Browser database: 2015 update Nucleic Acids Research, 43 (D1) DOI: 10.1093/nar/gku1177

Matthew L. Speir, Ann S. Zweig, Kate R. Rosenbloom, Brian J. Raney, Benedict Paten, Parisa Nejad, Brian T. Lee, Katrina Learned, Donna Karolchik, Angie S. Hinrichs, Steve Heitner, Rachel A. Harte, Maximilian Haeussler, Luvina Guruvadoo, Pauline A. Fujita, Christopher Eisenhart, Mark Diekhans, Hiram Clawson, Jonathan Casper, Galt P. Barber, David Haussler, Robert M. Kuhn, W. James Kent. (2015). The UCSC Genome Browser database: 2016 update bioRxiv DOI: 10.1101/027037

Mangan ME, Williams JM, Kuhn RM, & Lathe WC (2014). The UCSC Genome Browser: What Every Molecular Biologist Should Know Current Protocols in Molecular Biology., 107 (19.9), 199-199 DOI: 10.1002/0471142727.mb1909s107

Disclosure: UCSC Genome Browser tutorials are freely available because UCSC sponsors us to do training and outreach on the UCSC Genome Browser.


Video Tip of the Week: RNACentral, wrangling non-coding RNA for simplifying access

Non-coding RNA data can be tricky to locate in public data sources. Sometimes it is handled with other gene sets, other times it’s not. Some ncRNA may be found in databases of one type or one species, but it’s not always clear what the best route to find them would be. The folks from RNACentral want to help solve this problem. They aim to create a resource that is essentially “UniProt for non-coding RNAs” as they described in their recent webinar.

The RNACentral team is working with database providers to generate a centralized access point to these disparate collections. RNAcentral_featuresThey have uniform, stable IDs and and also syntax so you can search by specific species. They aren’t species-restricted, but the rate of them incorporating your favorite data sets may vary. They are incorporating more collections as we speak and have plans for more in their upcoming releases. This summary slide offers some of the other main features of their services.

The members of this consortium (three dozen at this time) are working on getting all of the data in. I should say, though, that this doesn’t replace the member databases. You will still want to go to places like miRBase or the WormBase for deeper details on the items, or specific tools to work with that subset of the data. But with RNACentral you get centralized searching of everything, so it’s a great place to start.

The best way to get a sense of this, though, would be to watch this recent webinar, which will be this week’s Tip of the Week.

They also provided their slides, which you can access at the EBI training page. And they put the question segment in a separate file, but I almost always learn something from the good questions that are asked, and if you have questions you might want to see if they covered them. If you find your project requires some information about non-coding RNAs, you should know about the tools at RNACentral.

Quick link:



RNAcentral Consortium. (2014). RNAcentral: an international database of ncRNA sequences Nucleic Acids Research, 43 (D1) DOI: 10.1093/nar/gku991


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 . 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:

Reactome new pathway browser:

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

Genomics England is responsible for the 100,000 Genomes Project

Video Tip of the Week: PanelApp, from the 100000 Genomes Project

Genomics England is responsible for the 100,000 Genomes Project

Genomics England is responsible for the 100,000 Genomes Project

Last week I talked about the 100,000 Genomes Project in the UK. That video tip was an introduction and overview of the project. This week, though, we’ll highlight one software piece that is in place now. PanelApp is a collection of gene lists, with information about the evidence associated with variations in these genes. The goal is to inform clinical interpretation based on the best quality evidence.

It’s a straigtforward interface, with a traffic-light based system (green, yellow, red) to assist with quick diagnostic assessments. The lists are based on a set of rare disease categories and phenotypes, and the idea is that qualified reviewers will create a definitive list of evidence for certain variations, and rate the genes accordingly. Ultimately they want to create a consensus gene panel collection. They describe the evidence thresholds this way:

  • Green = highest level of confidence; a gene from 3 or 4 sources.
  • Amber = intermediate; a gene from 2 sources.
  • Red = lowest level of confidence; 1 of the 4 sources or from an expert list.

You can learn more about the strategy from their announcement: New rare disease gene tool launched – PanelApp. This first video will give you an idea of how this looks. A public interface is available for anyone to look around and download the information. You don’t need to log in, you can click on the “Browse Panels” tab for an idea of the organization and information content.

So crowd-sourcing this evidence is the idea here. And they are interested in having people contribute to these panels. A separate video describes the roles for people who are interested in contributing to the collection and curation of the information. Reviewers will evaluate the existing items and also add new genes. They have detailed instructions and guidelines available to help. However, the first round of review ended already (October 19 2015). The panels will remain available to explore and download. But there will be another review period opening in the future. So if you are considering a contribution to this work, not to worry–your chance will come and it won’t hurt to be prepared. Also, if you are considering using the panels, I think it’s a good idea to know how the curation and review is done.

The information in the panels now has been seeded by many key sources that you may be familiar with. They have used data from OMIM, OrphaNet, Genetics Home Reference, the Decipher DDD project, HGNC, ClinVar, UniProt, NCBI Nucleotide, and ENSEMBL among others to add phenotype and gene details of many types. So for any gene you can keep digging for addtional information.

There are a long more details over at the announcment post:   New rare disease gene tool launched – PanelApp. But I think the structure and color cues are helpful. (Wait, colour, right? UK variation). Check it out yourself, though, with the link below. You can hear more in this podcast as well.

Quick links:


100,000 Genomes Project:


Siva, N. (2015). UK gears up to decode 100 000 genomes from NHS patients The Lancet, 385 (9963), 103-104 DOI: 10.1016/S0140-6736(14)62453-3

Caulfield M., Davies J., Dennys M., et al “The 100,000 Genomes Project Protocol” London: Genomics England (2014) Available at: (Accessed: Oct 8 2015)
Genomics England is responsible for the 100,000 Genomes Project

Video Tip of the Week: 100,000 Genomes Project

Genomics England is responsible for the 100,000 Genomes Project

Genomics England is responsible for the 100,000 Genomes Project

Software tools are certainly our focus for most of our tips of the week. But a key aspect of using the software and data repositories is that they rely on quality data. So sometimes we’ll highlight specific projects that will provide data to researchers, and this tip is one of those cases. Researchers should be aware of the data, understand the project goals, and that they may benefit from access to this information in the future. So this week, we’ll highlight the 100,000 Genomes Project as our video tip of the week.

Genomics England is the organization that is in charge of this project. From their “about” page, you can learn more about the organization and structure. But I’ll post their specific goals here:

  • to bring benefit to patients
  • to create an ethical and transparent programme based on consent 
  • to enable new scientific discovery and medical insights
  • to kickstart the development of a UK genomics industry

Oh, how I yearn for a national health system that has this kind of opportunity and security for patients. I hope they can keep it. </aside over>

I saw their outreach video the other day, and I thought it was well done. So to get an overview of their efforts, that video is this week’s Tip of the Week:

Another reason I wanted to highlight this project is that there is a deadline for researchers to apply for access to this project that is coming up soon:

Deadline for researchers:

So if you are in a position to help with this project, consider this call to action and sign up. It seems to be very valuable in getting personalized medicine to patients in specific ways, while also benefiting the genomics-to-clinic transition that all of us will need. You can learn more about the need to input on the data here in a second video. They have emphasized the aspect for training people in this topic, which is music to my ears as well. Young researchers who can get involved with this work should benefit for many years as the data comes along and we can follow patients over time.

Quick links:

Genomics England:



Siva, N. (2015). UK gears up to decode 100 000 genomes from NHS patients The Lancet, 385 (9963), 103-104 DOI: 10.1016/S0140-6736(14)62453-3

Caulfield M., Davies J., Dennys M., et al “The 100,000 Genomes Project Protocol” London: Genomics England (2014) Available at: (Accessed: Oct 8 2015)
Grinstein on dataviz at VIZBI.

Video Tip of the Week: Weave, Web-based Analysis and Visualization Environment

At the recent Discovery On Target conference, a workshop on data and analytics for drug discovery contained several informative talks. This week’s Video Tip of the Week was inspired by the first speaker in that session, Georges Grinstein. Not only was the software he talked about something I wanted to examine right away (Weave)–his philosophy on visualization of data was so in line with my informal thoughts on the topic that I just connected with it immediately. But also–stay for the “living figures” down below.

Grinstein on dataviz at VIZBI.

Grinstein on dataviz at VIZBI.

Grinstein has been working on dataviz for a long time. And he’s been working with big data since long before big data was trendy. For some of his background and philosophy, check out this talk at a VIZBI conference. Because so many of the problems are the same across big data types, the software that he’s been working on could really be useful for the new issues facing big data in biology. But I don’t know that I’ve heard about it among the genoscenti just yet. (In this talk he also covers RadViz, a radial visualization tool that some folks might find useful. It was also mentioned in the workshop.)

One of the key things that he wanted us to take away from the workshop was that we need to offer people multiple, interactive visualizations for them to get the most of out the data. This is something I’ve been looking for quite a bit. I fell in love with an early version of the Caleydo stuff for exactly this reason. But I understand that it can be tricky.

Weave, or the Web-based Analysis and Visualization Environment, gets closer to this with super responsiveness than I’ve seen elsewhere. This week’s Video Tip is a short intro to this platform, but I’ll link you below to a longer form that you should watch if you want to dive into this tool. Here you’ll see that just by dragging a CSV file in, you can then set up a scatter plot, bar chart, parallel coordinates, a color histogram, and a table. In seconds. Really.

This brief intro doesn’t do full justice to this tool, of course. I joined the Weave-users discussion group and found a recent webinar recording that you should watch. But you’ll have to grab it from the group, it doesn’t appear to be stored on a video platform site (search for the thread called IVPR Update on Weave Monday 3/23). It goes into more detail on the features, of course. And sharing data, and reproducibility of the visualizations with the session history options.

I downloaded the Weave Desktop and ran it on my little system. I grabbed some transcription factor score data from the ENCODE project with the UCSC Table Browser, got it in csv format, pulled it in, and within seconds was looking over all the data on the X chromosome for this TFBS I was interested in. Clicking an item in my table highlighted it in my histogram. And that was just to kick the tires. According to the video, you could have had a tile of Cytoscape (because you can integrate with Cytoscape–I didn’t get that far yet though) and checked out interaction data as well. Although I mention Cytoscape because readers here probably know it, that’s just one of the linkable tools. R is embedded, and other stats tools, and you can modify your scripts right from Weave. Some of these additional features may be part of the Analyst Workstation sub-project. I couldn’t always tell which tool had which features in my early explorations.

But if there’s one thing I’d like you to do after reading this post (if you read this far) is look at this paper that is just out. As I was noodling on Weave, I thought to myself that it was PERFECT to create the kind of “living figures” that I want to see in more papers. Now go see Dynamic Data Visualization with Weave and Brain Choropleths. I don’t care if you aren’t interested in brain choropleths–go look at the figures. In each one, there’s a link to a Weave demo, like this:

Weave demo PLOS

Click on those demos to load them. You can be interacting with the data on the brain maps, with pre-set Weave tiles of different features of the data set for you. Open the gears icons to change the settings. Now imagine this with gene expression maps in C. elegans bodies. Or with transcription factors and scores in mouse embryos. Or Venns with big piles of GO terms (but what I really want there is UpSet anyway). Or any of a dozen other types of data we get in big data papers now that are really impossible to explore in traditional publication format. I want this for genomics papers in the future, okay?

This software has a lot of potential for analysis, visualization, and sharing of data. I can’t cover it all in a brief blog post. The Weave team has thought carefully about sharing with colleagues, reusable templates, and provenance of data, and all this is built right into to this tool. If you are analyzing data for others, you can set up dashboards for them to see specific views. See their help and info docs for more details, and check out the longer videos in the forum.  I think it would connect with a lot of people–and could benefit the genomics community greatly. Have a look. I think you’ll like it.

Quick links:



Weave-users discussion:!forum/weave-users

Weave desktop:

More videos, Weave IVPR channel:


Patterson, D., Hicks, T., Dufilie, A., Grinstein, G., & Plante, E. (2015). Dynamic Data Visualization with Weave and Brain Choropleths PLOS ONE, 10 (9) DOI: 10.1371/journal.pone.0139453

Daniels, K., Grinstein, G., Russell, A., & Glidden, M. (2012). Properties of normalized radial visualizations Information Visualization, 11 (4), 273-300 DOI: 10.1177/1473871612439357


Video Tip of the Week: Global Biotic Interactions database, GloBI

otter_lunchAnd now for something completely different. Typically we highlight software that’s nucleotide or amino acid sequence related in some way. But this software is on a whole ‘nother level. It looks at interactions between species. This week we highlight GloBI, the Global Biotic Interactions database.

Before you start thinking of Bambi and butterflies, though, consider the image shown right at the beginning of the slide presentation about this project (slide 2). It includes interactions such as lunch. Here’s where it started to get me thinking about the implications for genomics. There have been some papers talking about sequences from other species, which may or may not have been eaten, appearing in various samples. Are these contaminants, or are they real? If they are real, we might expect to see some of these “interactions” reflected in sequence repositories. So it struck me that knowledge of these might be helpful in sussing out some of those situations. In fact, from this project, I learned about a whole bunch of “diet” databases that were new to me (see slide 5, for example, Avian Diet Database).


But also, for ecological purposes, there’s a lot of value in this data. I loved this quip on their “about” page:

Now that folks have mapped the human genome, put a man on the moon, isn’t it time to provide easy access to how, when and where organisms interact with each other so that we can better understand and better preserve our ecosystems? Perhaps GloBI can become the OpenStreetMap of ecology: a global map that shows how organisms rely on each other . . .

Certainly that’s worthwhile. And I’m glad to see this effort to capture and share this information. And the structure of the data, using a number of ontologies including some that were new to me, looks very helpful. The GloBI data is subsequently used in the Encyclopedia of Life to connect people with information about food sources for species, too.

So this week’s video tip of the week is the intro video that the team has provided:

Global Biotic Interactions Introduction (2 Minutes, March 2014) from Jorrit Poelen on Vimeo.

Interesting side note about the data that’s currently available to use–seems there’s a lot of proprietary data that’s been collected in this field, and they have created a “Dark GloBI” to allow people to access that restricted stuff within their framework (see the discussion in the paper below). How can that US government data not be public?? But they hope to entice a lot of this data to come out of the dark side and be publicly available.

So check out this resource for species interactions, and contribute data if you have it. There have been some classroom projects collecting information that might be great for people in teaching situations too. It looks very valuable on a number of levels.

Hat tip to Esther Martinez on G+

Quick link:

Global Biotic Interactions:


Poelen, J., Simons, J., & Mungall, C. (2014). Global biotic interactions: An open infrastructure to share and analyze species-interaction datasets Ecological Informatics, 24, 148-159 DOI: 10.1016/j.ecoinf.2014.08.005

Comparison of cancer genomics tools, via: Swiss Med Wkly. 2015;145:w14183

Video Tip of the Week: UCSC Xena System for functional and cancer genomics

When we go out and do workshops, we get a lot of requests from researchers who would like some guidance on cancer genomics tools. Our particular mission has been to aim more broadly at tools that are of wide interest and not to focus on a particular disease or condition area. But certainly the cancer genomics arena is going to be one of the ones that’s got so much opportunity for great bioinformatics-based outcomes in the near term. So I keep an eye out for tools researchers may want to explore.

When the “genomics” twitter column in my Tweetdeck dropped this new mini-review of cancer genomics tools on my desktop, I went to look right away: Data mining The Cancer Genome Atlas in the era of precision cancer medicine. TCGA is the focus of the data source they are talking about, but the tools included may have more data sets and wider utility, of course. Most of the tools described were familiar to me (cBioPortal, GDAC Firehose, UCSC Cancer Genomics Browser, canEvolve), but a couple of them were new. I had never explored the ProGeneV2 tools before. And the UZH Cancer Browser was also new to me.

Comparison of cancer genomics tools, via: Swiss Med Wkly. 2015;145:w14183

Comparison of cancer genomics tools, via: Swiss Med Wkly. 2015;145:w14183

One thing that’s very helpful to me is the kind of table they provided as Table 2. It’s a comparison of the main tools they are discussing, with different features of each compared. That’s handy for choosing the tool to spend time on, depending on your own research needs.

But they also referred to another tool that was new to me, Xena. “The UCSC cancer browser will be updated in the future, with the new Xena platform for visualisation and integration with Galaxy“. I can never resist new genomics visualization tools, and as a giant fan of Galaxy, I certainly need to know more about this.

So I went to look around for some information on it, and their introductory video is this week’s Tip of the Week.

So Xena is designed to let you combine your own data with large public resource collection data, without leaving your firewall or without being too onerous to pull down all the public data and manage it locally. You can explore functional genomics data and related phenotype and clinical data. It uses the “hubs” strategy that is becoming increasingly adopted as a way to integrate across data collections. We were just talking about hubs in another recent tip if these are new to you. It supports a wide range of data types to examine and visualize. If you want to go deeper, there’s a lot more information over at the Xena homepage. They have documentation, presentation slides, and a step-by-step demo available from a recent workshop.

Certainly one of the key features appears to be that you can integrate your own research data–which might be subject to strict privacy regulations–on your own computer with all the other key information from public data providers. Increasingly researchers I talk to at workshops need this aspect very much.

So try out Xena, and explore the other tools in the cancer genomics space, to see what’s right for your research.

Hat tip to Oscar:

And you can follow Xena on twitter for news and updates:

Quick links:



Cline, M., Craft, B., Swatloski, T., Goldman, M., Ma, S., Haussler, D., & Zhu, J. (2013). Exploring TCGA Pan-Cancer Data at the UCSC Cancer Genomics Browser Scientific Reports, 3 DOI: 10.1038/srep02652

Cheng PF, Dummer R, & Levesque MP (2015). Data mining The Cancer Genome Atlas in the era of precision cancer medicine. Swiss Med Wkly. (145) : 10.4414/smw.2015.14183