Category Archives: Tip of the Week

Video Tip of the Week: yEd Graph Editor for visualizing pathways and networks

This week’s video tip of the week closes out a series that began last month. I started to explore one gene co-expression tool, which led me to another tool for visualization, and so on. This week’s tool is the final piece that you need to know about if you want to create the kind of interaction/network diagrams used in the modeling of a system that I covered last week.

The yEd Graph Editor is different than some of the tools. As a corporate product, it doesn’t have

yFiles layouts options in Cytoscape

yFiles layouts options in Cytoscape

the kind of scientific paper trail that some academic tools will. But if you search Google Scholar for “yED Graph Editor” you’ll see people from a wide range of disciplines have used it for their research projects. I first learned about yEd when I was using Cytoscape, and saw that some of the choices for layouts were based on the yEd features. This short overview video from the yWorks folks will explain what some of those layout styles are.

As you can see in this video, the use of yEd is not only for biological interactions–it can do a whole lot of graphing that is entirely unrelated to biology. But the features work for biological networks, and you can customize the graphics to represent your own topic of interest.

There are longer videos with more detail on the use cases for yEd. This one uses a sample flow chart to illustrate the basic editing features. It quickly covers many helpful aspects of establishing and editing a visualization.

You can also find videos from folks who use yEd for their projects on YouTube, some of which might be more specific for a given field of research. But these should give you the basics of why yEd can be used for the types of projects that you saw in the previous tips with Virtually Immune and BioLayoutExpress3D. And like I noted with Virtually Immune, you can get your hands on the files in the Pathway Models collection, and launch a yEd file to go into the features with a detailed example. The complexity you can generate with these models is astonishing.

There was no reference specifically for yEd that I was able to locate, but you can find that lots of people use yEd graph editor on a wide range of research topics in Google Scholar. So if you are looking to see if someone in your research area has used yEd, you may find some examples. If you are going to consider exploring the BioLayout and Virtually Immune tools, it will help to understand the framework. And also as I mentioned in Cytoscape–understanding yEd helped me to grasp the layout options there too. So try out yEd for pathway and network visualization if you have needs for those types of representations in your research and presentations. It’s free to download and use.

Quick links:

yED Graph Editor:

yEd Graph Editor Manual:


Wright D.W., Tim Angus, Anton J. Enright & Tom C. Freeman (2014). Visualisation of BioPAX Networks using BioLayout Express3D, F1000Research, DOI:

Smoot M.E., K. Ono, J. Ruscheinski, P.-L. Wang & T. Ideker (2010). Cytoscape 2.8: new features for data integration and network visualization, Bioinformatics, 27 (3) 431-432. DOI:

Video Tip of the Week: “Virtually Immune” computational immune system modeling

This week’s video tip of the week is the next in a series. It began when I took a look at GeneFriends, and their option to output the data for use in BioLayout Express3D. So of course we had to then take a look at BioLayout. While I was exploring BioLayout, I came across Virtually Immune. This project contains intricate network diagrams of immune-system related responses which you can load into BioLayout and explore. It is a very neat way to get further in your understanding of BioLayout functions, as well as being an amazing example of how to model a key system for human health. Here is their video overview:

Virtually Immune is developing computational models of the behavior of immune system responses, in part to help reduce the use of animal models. As part of a CrackIT project challenge, they developed a model of Influenza A lifecycle and macrophage responses that you can explore to help understand the goals of the project. On their “about” page, the overall goal includes:

By enabling scientists to run in silico experiments we hope to help them to model infectious and inflammatory disease-associated processes and thereby accelerate the development of of therapeutic agents. In so doing we hope this resource will assist in the reduction and refinement of the use of animals in immunological research.

Their text-based tutorial walks you through the basic steps of building the kinds of models they have: read the literature, draw the pathway you want to represent, initialize the conditions, and then simulate with BioLayout3D. The last step–Verify–means you go back to the bench and see if your computational model predictions make sense. Hopefully refining your ideas computationally can streamline the work in the lab.

To get the best sense of the capabilities of this project, go to their Pathway Models page. From here you can load up any of the examples in BioLayout and look around. When you hover over a pathway a “Show Me” button will appear near the bottom, and clicking that will load up the data in a larger format that you can explore it. On the bottom of the new page, you can click the BioLayout button to visualize this in 3D.

If you aren’t researching immune system features, that’s fine. But it will still help you to understand how pathways relevant to your work could be modeled.

Quick links:

Virtually Immune:

Virtually Immune tutorial:

BioLayout Express3D:


[can't find one for Virtually Immune yet; will attach one if I find it in the future]

Enright, A., & Ouzounis, C. (2001). BioLayout–an automatic graph layout algorithm for similarity visualization Bioinformatics, 17 (9), 853-854 DOI: 10.1093/bioinformatics/17.9.853

Theocharidis A., Stjin van Dongen, Anton J Enright & Tom C Freeman (2009). Network visualization and analysis of gene expression data using BioLayout Express3D, Nature Protocols, 4 (10) 1535-1550. DOI: *cough* access from their publications page…

Wright D.W., Tim Angus, Anton J. Enright & Tom C. Freeman (2014). Visualisation of BioPAX Networks using BioLayout Express3D, F1000Research, DOI:

Video Tip of the Week: BioLayout Express3D for network visualizations

My previous Video Tip of the Week highlighted the GeneFriends tool. With GeneFriends you can search for co-expression of genes in RNA-seq data sets. But you can take these results further and visualize them with the BioLayout Express3D tool, so I wanted to bring more details about BioLayout in this tip since we haven’t covered it before.

BioLayout isn’t a new tool, it’s been around for some time. The first published report of it appeared in 2001. Their publications page reflects their progress over the years, including a new paper recently put out for open peer review (very nifty, kudos on that). BioLayout keeps getting new features as it is under active development, and it keeps incorporating the key standards like BioPax that are important for interoperability of tools in this space. You can learn more about BioPax and related standards from the The ‘COmputational Modeling in BIology’ NEtwork (COMBINE) site.

This video tip will highlight their overview video to give you a taste of what BioLayout Express can do. But they have a page with more videos that can take you further on understanding and using the features of the software.

There’s a Nature Protocols paper that they produced a few years back that helped me to grasp what they want to accomplish and how to work with BioLayout. Although some of the details will have changed, I like these kinds of papers as a way to approach the concepts of working with the tools, so I’ve included that below as well. You can access it from their publications page.

BioLayout Express can handle very impressive numbers of data sets and the corresponding nodes and edges. Their publications page also offers a look at how some researchers have used their tool to advance their research. I like when tool providers offer these kinds of published examples, it helps to see how people really are using the tools.

Quick links:

BioLayout Express3D


Enright, A., & Ouzounis, C. (2001). BioLayout–an automatic graph layout algorithm for similarity visualization Bioinformatics, 17 (9), 853-854 DOI: 10.1093/bioinformatics/17.9.853

Theocharidis A., Stjin van Dongen, Anton J Enright & Tom C Freeman (2009). Network visualization and analysis of gene expression data using BioLayout Express3D, Nature Protocols, 4 (10) 1535-1550. DOI: *cough* access from their publications page…

Wright D.W., Tim Angus, Anton J. Enright & Tom C. Freeman (2014). Visualisation of BioPAX Networks using BioLayout Express3D, F1000Research, DOI:

Thanksgiving week, light posting. One holiday genome (cranberries).

cranberry_rakeThere’s nobody reading the blog this week each year, everyone is traveling or napping, at least in the US. So I’ll just bring a holiday genome I came across recently. Cranberries. This fruit is one of very few native North American fruits that are widely cultivated. I went looking to see if a genome paper was out yet, and there it was:
The American cranberry: first insights into the whole genome of a species adapted to bog habitat

As I was reading about the project, I thought I should know a bit more about specifically how they are grown. I’ve seen the flooded harvesting images, but I didn’t know what happened prior to that–the “bog habitat”. Conveniently, one of the research sites had links to some interesting videos of how cranberries are farmed. Sand–really–sand is the foundation of the fields. These dead-looking vines are laid out, and then partially buried in the sand. In a few years you will get cranberries. It’s kind of astonishing to actually see it–it looks so barren and lifeless at first.

Planting cranberries was the part new to me, and that video is posted here, but there are several more that include harvesting and shipping.

This genome project has also been added to the Sequenced Plant Genomes wiki that James Schnable maintains at CoGePedia: And it’s on the phylogenetic tree right near the blueberries (another North American native) on that page.

Cranberry genetics and genomics research site: . They also link to other groups involved in this work, but this is the one where I found the video.

Another fun fact for you to share at the dinner table: Probing Question: What is a heritage turkey?

“Some of these varieties were the progenitors of our current commercial turkeys, and they are fairly closely related to them genetically,” explains Hulet. “Today’s commercial turkeys are white because people didn’t like the little dots of pigment left on the skin after the feathers are pulled out, so breeders selected for a white-skinned turkey.” The white color is more natural for chickens, he explains, “while it’s a mutation for turkeys.”

Enjoy your mutant foods this holiday season.

Back to regular posting next week.


Polashock J., Ehud Zelzion, Diego Fajardo, Juan Zalapa, Laura Georgi, Debashish Bhattacharya & Nicholi Vorsa (2014). The American cranberry: first insights into the whole genome of a species adapted to bog habitat, BMC Plant Biology, 14 (1) 165. DOI:

Video Tip of the Week: GeneFriends

It was just a little tweet, with hardly any information about the function or purpose of the resource mentioned. But the cute name drove a lot of people to take a look at GeneFriends from our blog recently, so I figured it was worth highlighting this tool as our Video Tip of the Week.

So here’s the original tweet, hat tip to Jack Scanlan:

I admit, I looked too. I had imagined something like a personal genomics matching site, but that’s not what it is. GeneFriends is a tool that uses gene co-expression data to try to identify which genes are “friends” with other genes in networks. These can be known genes, or they can be uncharacterized genes. The current implementation is for human data.

Not a new tool, the original implementation of GeneFriends with microarray-based data sets came out some time ago. There are 3000 data sets in that part of the previous tool. But their new paper describes a different version, now done with RNA-seq data. The paper says there are over 4000 RNA-seq samples from 240 studies, via the SRA database. In the new paper they describe the criteria for selection and their strategy for calling co-expression. They state that their goal is to help unearth leads on annotation for uncharacterized genes, and this also includes non-coding RNA sequences.

GeneFriends employs a RNAseq based gene co-expression network for candidate gene prioritization, based on a seed list of genes, and for functional annotation of unknown genes in humans.

There is a short video with their foundation and philosophy about the GeneFriends tool:

Another video goes a bit further and illustrates an example of the functionality. On the site you can try this yourself with the handy “show example” buttons they have. In addition to what you’ll find at their site, they also demonstrate that you can bring your results over to the BioLayout tool to work with them further. They also note that you can upload the results into Cytoscape.

It’s pretty straightforward to use the basic features of GeneFriends, but there is additional detail on the underpinnings from their “about” page. The papers below also cover the foundations and their new directions. You should also be aware of the limitation of the RNA-seq data that they discuss in the new paper. But check it out to see if you can discover some new relationships among transcripts of interest with GeneFriends.

Quick links:

GeneFriends main page:

GeneFriends previous microarray version:

van Dam S., Rui Cordeiro, Thomas Craig, Jesse van Dam, Shona H Wood & João de Magalhães (2012). GeneFriends: An online co-expression analysis tool to identify novel gene targets for aging and complex diseases, BMC Genomics, 13 (1) 535. DOI:

van Dam S., T. Craig & J. P. de Magalhaes (2014). GeneFriends: a human RNA-seq-based gene and transcript co-expression database, Nucleic Acids Research, DOI:

Video Tip of the Week: UpSet about genomics Venn Diagrams?

Who can forget the Banana Venn? It was one of the most talked-about visualizations in genomics that I’m aware of.

So, yeah–#NotSureWhatItMeansButDontCare, and the extended Storify of the responses are still worth reading. It even got the wider tech media’s attention: Just look at that banana genome Venn diagram, by Cory Doctorow. I remember trying to follow the diagram for about 20 minutes before I gave up. But I still loved it for its audacious attempt to genesplain. It was impenetrable. But seriously intriguing. It was awarded the title of “best genomics Venn Diagram ever” by Jonathan Eisen.

It also spawned other examples. The loblolly pine genome folks did one of their own. Recently I actually had to look up what a jujube looked like to see if resembled the Venn they just recently delivered. Um, sorta, maybe–but I don’t know that was the goal or just a happy coincidence of a kinda oval fruit. However, I did catch a fun discussion on the actual origin of the species GO Venn, and currently the evidence points to the rat genome team, however the original published image lacks whiskers and eyes:

So as amusing as this has all been, one team took another approach to this issue. They wondered if this Venn craze was the best way to tackle this data, or if there were more effective and interactive ways to explore this sort of data. Some data set visualization tools may not be right for a task. Give me the bullet One problem is scaling Venn diagrams to capture the full range of features that that genomics folks want to illustrate. They are now prepared to UpSet the applecart. In their intro video to UpSet, they summarize with this:

I’ve talked about the terrific data visualization tools around the Caleydo project a number of times. They are developing really useful and intuitive strategies for looking at numerous types of data, and you can see our previous posts on StratomeX, LineUp, Entourage and enRoute (the combo of genomics data and pathways here is particularly nifty). They work really hard with the theories and techniques of data visualization, and implement effective ways to explore data. They recently looked across various genomics data papers to see how data sets were being used, and they attempt to encourage good behavior with the right visualizations to make the necessary points (Points of View reference below):

Understanding the tasks that the diagrams are meant to support and being aware of the data structure are required to find an appropriate representation.

They also have tried to help. UpSet, for visualization of intersecting sets, is one of their new efforts, championed by Alexander Lex, with the other team members. Looking for both effective and efficient representation of the types of data genomics researchers need, this interactive tool is a really nice way to explore which items belong in which subset. And, of course, which ones don’t.  But that’s just the beginning. With this tool you can easily spot the intersections, query for ones you are interested in, and sort in various ways. There are ways to explore the attributes and elements for the items as well. The other great thing about the Caleydo team is that they make nice intro videos–I’ll embed the overview one as this week’s video Tip of the Week, but they have a shorter basic intro one as well. In this video the examples include Simpson’s characters and movie data sets, but it will certainly allow you to quickly grasp the utility of this tool. But there’s a lot more to it as well. Read the UpSet paper linked below (and you will spot a copy of the notorious banana Venn, in fact, which inspired their thoughts on a better way to illustrate sets). It has a lot of nice guidance on set theory and will help you think about the appropriate uses of different representations.

The github pages have more help, documentation, and a link to try out an installation with your own data. I also recently had the chance to meet Alexander at a talk he gave, and I know he’s interested in knowing what other visualization challenges are problems in genomics, and would be interested in any feedback you have on the tools.

My dreams for this tool: it would be embeddable in journal articles. So I could see the data as the team presented it, but then also be able to explore the underlying stuff. And if it could be a sort of a “session” so I could snap back to the original view. And I wish I could embed an image faintly on the background….

Quick links:


Live version to kick the tires:

Caleydo tools overall project:


D’Hont A., France Denoeud, Jean-Marc Aury, Franc-Christophe Baurens, Françoise Carreel, Olivier Garsmeur, Benjamin Noel, Stéphanie Bocs, Gaëtan Droc, Mathieu Rouard & Corinne Da Silva & (2012). The banana (Musa acuminata) genome and the evolution of monocotyledonous plants, Nature, 488 (7410) 213-217. DOI:

Lex A., Gehlenborg N., Strobelt H., Vuillemot R.V. & Pfister H. (2014). UpSet: Visualization of Intersecting Sets, IEEE Transactions on Visualization and Computer Graphics (InfoVis ’14), DOI: TBD

Lex A. and Nils Gehlenborg (2014). Points of view: Sets and intersections, Nature Methods, 11 (8) 779-779. DOI:

Gibbs R.A., George M. Weinstock, Michael L. Metzker, Donna M. Muzny, Erica J. Sodergren, Steven Scherer, Graham Scott, David Steffen, Kim C. Worley, Paula E. Burch & Geoffrey Okwuonu & al (2004). Genome sequence of the Brown Norway rat yields insights into mammalian evolution, Nature, 428 (6982) 493-521. DOI:

Video Tip of the Week: Genome Browser in a Box

We’ve been doing UCSC Genome Browser training workshops for a decade now. We’ve seen all sorts of situations–from places that had terrific bioinformatics and IT support, to places where the attendees had no idea if anyone provided support at their institution. Ironically, sometimes the places with little support were big-name research places where all the support was aimed at, or associated with, certain high-profile labs, and not the average researcher or post-doc. We have also seen places where although there was support, it was so hostile and dismissive that we could understand why the researchers didn’t seek them out. So when we went in, often people would deluge us with questions about problems they were having working with their own data.

Frequently a problem they were having was being able to incorporate their own data into a viewable and explorable way with other tools, where they could look at the deep context of genome annotations with their data. Over the years the options got better and better to do this with the UCSC tools: custom tracks, sessions, then hubs. But one problem still remained: some people couldn’t put their data over the intertubz–for a variety of reasons.

In some cases they had patient data, and HIPAA  or grant agency privacy compliance issues, that restricted them to working behind their firewall. Sometimes their data sets were so huge they couldn’t get it loaded without timing out. Some places had the capacity to install a local UCSC mirror, but many didn’t. But UCSC has now solved this problem as well. Using their new Genome Browser in a Box (GBIB), you can download an installation of the UCSC Genome Browser to your own computer, use your own files, and they never have to leave your laptop or your firewall. You have your own personal mirror site. This might be a great solution for some folks at small companies too.

To accomplish this, you use a tool called VirtualBox to set up a virtual machine on your computer, you pull down the UCSC components, and you are ready to roll. I have an older and under-powered computer and it worked fine for me. It also is supported on Windows, Mac, or Linux, so it should serve most people.

This week’s video tip-of-the-week is a quick introduction to that setup. Although there is a paper already (below), good documentation (linked), and the ever-helpful mailing lists at UCSC, I thought some folks who were less likely to seek out (or have access to) the help might benefit from a walk-through of this process. I show where and how to get the GBIB, an overview of the steps, and then illustrate how this runs on my computer. You also get the benefit of my mistakes–I did testing for this before it was released, and I had installation issues, so I highlight where to get the help with that (Pro-tip: I should have printed the documentation before installing–it was all in there. And don’t forget to check the “troubleshooting” section at the end.).

So if you’ve wanted to load your own data in to the UCSC Genome Browser and use the suite of tools there to visualize and query–but haven’t been able to–give the Browser in a Box a try.

You can learn more about the concept and the implementation from the UCSC blog, see announcements, and a press release with a sweet photo of some members of the terrific team who delivered it. And, of course, the publication below.

In this overview video, I don’t go into more detail on how to use the browser–with your own mirror you are really using the same features that our regular training materials cover–the introduction to the browser and the advanced tools features are mostly the same.

Note: “GBiB is free for non-commercial use by non-profit organizations, academic institutions, and for personal use. Commercial use requires purchase of a license with setup fee and annual payment.” At OpenHelix we have a contract to do general training and outreach, we do not benefit from any license fees associated with the UCSC browser. Checking your status for licensing GBIB or the required tools is in your hands.

Quick links:

Get the Genome Browser in a Box at their Store: This has the system requirements detailed as well.


GBIB help (print this to help you with the installation):


Haeussler M., B. J. Raney, A. S. Hinrichs, H. Clawson, A. S. Zweig, D. Karolchik, J. Casper, M. L. Speir, D. Haussler & W. J. Kent (2014). Navigating protected genomics data with UCSC Genome Browser in a Box, Bioinformatics, DOI:


Video Tip of the Week: PaleoBioDB, for your paleobiology searches

Yeah, I know, it’s not genomics–but it’s the history of life on this planet–right?  The Paleobiology Database has been keeping records of this ancient biology for a while now, and they have some really nice tools to explore the fossil records and resources that have become available. It’s also interesting to me to see the informatics needs of this type of project. It has a lot of overlap with databases of more recent biology, like the GOLD one–they need taxonomy for the organisms, they need literature links–but they have other needs to capture both geographical regions and the layers of time as well.

There are a couple of ways to access the data. When you arrive at the main landing page, you have the choice to “Launch PBDB”, or “Launch Navigator”. PBDB is a “classic” interface, with typical search boxes and query results. Since this is the internet, I used that “quick search” and looked for paleo cats, and found a lot of Felis in there. But that’s not the only way to look around. They have a newer graphical access mechanism that’s called the Navigator. You can use the navigator to search the world, filter for specific items or time periods–but my favorite thing is you can reset the planet to be what it looked like eons ago. This is covered in their intro video that is this week’s Tip of the Week:

They have other videos as well, you can see that they have both this Navigator interface and help with the classic style. Their “apps” offer other types of searches too. You can even search for insect size. Another way to access information is via R. I began to look around at this because David Bapst on Google+ pointed to their new publication announcement (linked below), offering their R package for accessing their underlying data.

According to their publications page, this resource supports a wide range (and copious amount) of research in this field. It was really neat to have a look at a rather different scale of bioinformatics across the time horizon. Check out the Paleobiology Database resources for your fossil needs.

Quick link:

Paleobiology DB:

Varela S., González-Hernández J., Sgarbi L., Marshall C., Uhen M., Peters S. & McClennen M. (2014). paleobioDB: an R package for downloading, visualizing and processing data from the Paleobiology Database, Ecography, DOI: 10.1111/ecog.01154

Video Tip of the Week: SeqMonk

Always on the lookout for effective visualization tools, I recently came across a series of videos about the SeqMonk software. It’s not software that I had used before, so I wanted to look at the videos, and then try it out. It downloaded quickly, offered me an extensive list of genomes to load up, and then right away I was kicking the tires. And I was impressed. It was easy to locate and explore different regions and the different tracks that were available. And it appears to be very straightforward to load up your own data as well. The video I’ll highlight here is called “Creating Custom Genomes with SeqMonk” which gives a nice intro to their setup.

But they have a whole BabrahamBioinf channel with helpful videos, including a nice short one on how to export graphical representations to use for presentations and publications and such. This is a request I hear a lot from people, and this is a nice guide.

Then I went to look for references for the software to learn more. The group that has developed it–Babraham Bioinformatics–hasn’t published papers specifically on their tools, apparently. They are a services and support group for an institution and not a research group. But they make many of their tools available to the public.

As I’ve noted, though, I really like to get a sense of how people are using the tools, and who is using tools, by looking deeply at the literature. When something has no official citation, it’s harder to assess. And as I’ve pointed out, many papers don’t even cite the tools in the main paper, sometimes it’s in figure legends, or supplements.

A lot of folks have found SeqMonk useful. But it took me 3 different site searches to figure out how useful. I searched at PubMed, PubMedCentral, and Google Scholar. The results were pretty interesting, actually. Just a basic search for SeqMonk yields these differences:

Literature search site number of results
PubMed 1
PubMedCentral 53
Google Scholar 110

The paper in PubMed wasn’t in PubMedCentral, but it was among the 100+ in Google Scholar. Of the 53 in PMC, 2 were absent from Scholar–one had SeqMonk in a figure legend, one had SeqMonk in supplemental procedures. Google Scholar obviously had the biggest range–it also included meeting abstracts, theses, and patent documents, and also a few false positives (from 1840?, 1929, and a couple of other things I couldn’t figure out). Oddly, sometimes the titles differed between PMC and Scholar, but they appeared to be the same paper.  As I’ve noted before, it’s challenging to find out where software is being used, since the way people reference it can be so variable. This was another interesting example of this variability.

But that aside, I was certainly impressed by the various types of data and species that SeqMonk has supported. The variety of species included archaea, chloroplast genome studies, bacteria, ancient maize, yeast, medicinal mushroom mitochondria, zebrafish, and a lot of mammalian research. It has supported a wide range of explorations and topics–lots of epigenetics, PCR techniques, telomere erosion, methylomes of tumors, and even comparison of sequence alignment software. Figure 1 of that aligners paper gives you a nice look at SeqMonk in the wild.

So have a look at the features of SeqMonk for visualization, analysis, and display of existing genomes or your own data. It’s a flexible and effective tool for many purposes.

Quick links:


Their video channel:

Their training materials:

Follow them on twitter:


Chatterjee A., P. A. Stockwell, E. J. Rodger & I. M. Morison (2012). Comparison of alignment software for genome-wide bisulphite sequence data, Nucleic Acids Research, 40 (10) e79-e79. DOI:

Video Tip of the Week: MedGen, GTR, and ClinVar

The terrific folks at NCBI have been increasing their outreach with a series of webinars recently. I talked about one of them not too long ago, and I mentioned that when I found the whole webinar I’d highlight that. This recording is now available, and if you are interested in using these medical genetics resources, you should check this out.

I was reminded of this webinar by a detailed post over at the NCBI Insights blog: NCBI’s 3 Newest Medical Genetics Resources: GTR, MedGen & ClinVar. There’s no reason for me to repeat all of that–I’ll conserve the electrons and direct you over there for more details about the features of these various tools. There is a lot of information in these resources, and the webinar touches on these features and also describes the relationships and differences among them.

I’ve been catching the notice of their webinars by following their Twitter announcements. The next one is coming up on October 15th, announced here, on E-Utilities. Follow them to keep up with the new offerings: @NCBI.

Quick links:


GTR, Genetic Testing Registry:



Acland A., R. Agarwala, T. Barrett, J. Beck, D. A. Benson, C. Bollin, E. Bolton, S. H. Bryant, K. Canese, D. M. Church & K. Clark & (2013). Database resources of the National Center for Biotechnology Information, Nucleic Acids Research, 42 (D1) D7-D17. DOI: