Category Archives: Tip of the Week

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


Video Tip of the Week: BANDAGE for visualization of de novo assembly graphs

Typically we highlight web-based tools for our Tips of the Week. But sometimes there’s a tool that has some novel visualizations that we want to note that might require a user to do a download and installation. This week’s tool is one of those–BANDAGE, a Bioinformatics Application for Navigating De novo Assembly Graphs Easily.

I heard about it on twitter, and I was drawn to the paper right away by the visual representation in that figure:

It happened to come out around the same time I was thinking about the paths used to graphically represent variation data among individuals too. So I had been thinking a lot about ways to show alternate paths of sequence data with graphical representations. And although I don’t do assemblies myself–I focus downstream of that when the data is already refined–it’s useful to think about the challenges of assembling these data sets. And the interest in the tool suggested to me that people who do de novo assemblies might want to hear about this.

The Bandage team has several videos that help to explain the features of their tool. I’ll highlight the overview/introduction here for this weeks Tip of the Week, but if you want to learn more about the details be sure to check out their others.

So have a look at Bandage and see if it helps you to solve de novo assembly issues for your project. Or just think about how others tackle these problems before they get submitted to the databases for everyone’s use. I think people who use data downstream should be aware of the way that the genome assembly folks are faced with the data. I found it really helpful to conceptualize this.

Quick link:



Wick, R., Schultz, M., Zobel, J., & Holt, K. (2015). Bandage: interactive visualization of genome assemblies Bioinformatics DOI: 10.1093/bioinformatics/btv383

UCSC Genome Bioinformatics

Video Tip of the Week: UCSC features for ENCODE data utilization

UCSC Genome BioinformaticsAs noted in last week’s tip about the ENCODE DCC at Stanford, there was a workshop recently for the ENCODE project. There were a lot of folks speaking and a big room full of attendees. You should check out the full agenda and the playlist at the NHGRI site for all the videos, slides, and handouts: ENCODE 2015: Research Applications and Users Meeting.

This week I’m highlighting another video from this event. In this one, Pauline Fujita from the UCSC Genome Browser covers ways to work with ENCODE data in their browser.

Some of the talk includes intro stuff for brand new users, because there were certainly some in this workshop. If you are new to the tools, too, you can also see our free tutorial suites (below). Pauline also quickly highlights their Genome Browser in a Box virtual machine option for folks who have privacy sensitive or protected data, but only briefly. If you want some more info on that, check out our Tip of the Week on GBIB.

But soon she covered more detail on features like track hubs and how to use those (if you wanted to jump to that part, it begins around 20min). That extra search for items in the Track Hub is really good to know about. file_formats_helpAlso, there’s some guidance here on the types of file formats that you may want to use to structure your data. Also why you want BED vs Wiggle, for example. For the part that addresses these formats, jump to about 33min.

Towards the end there’s coverage of the Data Integrator. The idea with this feature is that maybe you’ve got some information on a region and you have this structured as a BED file–or a number of regions–and you want to find out what else is going on in those regions. The Data Integrator can help you with that by finding overlaps among different tracks of data (around 45min). The Variant Annotation Integrator does kind of a similar thing, but for VCF files with variation information (~48min). A smidge more guidance on track hubs comes in at 50min.

In our paper for Current Protocols (which is now in PubMedCentral), we talk a bit about the hubs structure too. So if it runs too quickly at the end, our paper shows some of that detail pretty much the same way. That might help you to think about how to structure them if the concept is new to you. But if you are ready to dive in, there’s a paper specifically about hubs. And there’s also more background on the browser’s tools and in the NAR database issue papers. There’s a lot of ENCODE data available to mine, and I really hope more folks can use the tools to find new insights into genomic regions they are interested in.

Quick links:

Track hubs:

Data Integrator:

Variant Annotation Integrator:

ENCODE features at UCSC:

UCSC tutorial suites:

UCSC Intro Tutorial suites (video, with our free slides + exercises):

UCSC Advanced Tutorial suites (video, slides, exercises):


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

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

Raney, B., Dreszer, T., Barber, G., Clawson, H., Fujita, P., Wang, T., Nguyen, N., Paten, B., Zweig, A., Karolchik, D., & Kent, W. (2013). Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser Bioinformatics, 30 (7), 1003-1005 DOI: 10.1093/bioinformatics/btt637

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: ENCODE Data Coordination Center, phase 3


Image via: A User’s Guide to the Encyclopedia of DNA Elements (ENCODE). doi:10.1371/journal.pbio.1001046.g001

The ENCODE project began many years ago, with a pilot phase, that examined just 1% of the human genome. But this initial exploration helped the consortium participants to iron out some of the directions for later stages–including focusing on specific cell lines, techniques, and technologies in Phase 2. There have been a number of publications that came out from consortium members, but in addition to the participant’s papers, a lot of other folks have mined this data for various investigations as well. There’s still plenty of opportunity for discovery. Some people may not realize that there’s an also ENCODE phase 3 underway.

When we had a contract with the folks at UCSC Genome Browser for outreach on ENCODE, we developed materials to help people explore the data. But we hadn’t delved into it much since phase 3 began. But the other day I got a note from my NHGRI YouTube subscription (GenomeTV) that a whole workshop of ENCODE phase 3 information had been made available. So I wanted to have a look.

There is a series of video segments that correspond to this agenda from the ENCODE workshop. I’ll be highlighting one of them here, the one that introduces the features of the Phase 3 Data Coordination Center at Stanford now. But there may be others that you want to examine for your research goals as well. Another way to work through the different segments is available from the NHGRI page here: That page offers the slides, handouts, and exercises too.

The video is longer than our typical tips, but it’s worth seeing for the context and framework details. There’s also a section on searching and filtering, which explains how to locate precisely the things you want to find. There’s a helpful and funny analogy to searching for shoes as you would at Zappos. I’ve used the Zappos tool exactly that way, and I also like it very much. If you want more details on how their ontology structure helps them to accomplish this, check out the paper linked below. Also in the video, there’s a piece about how the metadata is structured, and what you can expect to find there.

There’s also a part about how to visualize the things you find. You end up loading them as a UCSC Genome Browser track hub, which is integrated with all they other data at UCSC. There’s another video with Pauline Fujita on the hubs which I’ll address separately later.

The playlist for the whole meeting is here. I won’t be highlighting all of them, but I may select more of them for future tips.

Quick link:

ENCODE portal:


Malladi, V., Erickson, D., Podduturi, N., Rowe, L., Chan, E., Davidson, J., Hitz, B., Ho, M., Lee, B., Miyasato, S., Roe, G., Simison, M., Sloan, C., Strattan, J., Tanaka, F., Kent, W., Cherry, J., & Hong, E. (2015). Ontology application and use at the ENCODE DCC Database, 2015 DOI: 10.1093/database/bav010

ENCODE Project Consortium (2012). An integrated encyclopedia of DNA elements in the human genome Nature, 489 (7414), 57-74 DOI: 10.1038/nature11247

ENCODE Project Consortium. (2011). A User’s Guide to the Encyclopedia of DNA Elements (ENCODE) PLoS Biology, 9 (4) DOI: 10.1371/journal.pbio.1001046

ENCODE Project Consortium (2004). The ENCODE (ENCyclopedia Of DNA Elements) Project Science, 306 (5696), 636-640 DOI: 10.1126/science.1105136


Video Tip of the Week: Human Metabolome Database, HMDB

HMDB_logoThe HMDB, or Human Metabolome DataBase, is another nice data collection and tools from the Wishart lab. Although we have mentioned it in the past, because of it’s emphasis more on small molecules it isn’t something we covered in detail. But with this new video that’s available, I thought it was a good time to include it in our database resources for folks who might be seeking out this kind of metabolomics data.

Their overview video that will be our tip of the week notes that currently their resource contains over 40,000 metabolites. They introduce the types of information contained within, including not only chemical names and structures, but also descriptions, taxonomies, concentrations in biological fluids, reactions and pathways and the roles in human disease. The video goes on to describe the ways to interact with the data, via browsing or searching, and more.

There’s a tremendous amount of information on the pages, with appropriate links to many other useful sources as well. Of course you can also search with sequences using BLAST, and the gene pages  will offer lots of detail and links to enzyme and metabolite products that may be useful to think about. And in the never-ending search for appropriate biomarkers for medical situations, this is a really useful repository of knowledge.

While preparing for this tip, I also happened to notice a tweet from their group that was useful for folks who are trying to learn about these topics.

It appears to be a popular and effective guide to exploring translational biomarker discovery topics, with another tool that was new to me: ROCCET, ROC Curve Explorer & Tester. The tutorial with links is listed below as well. So have a look if you are interested in evaluating this type of biomarker data.

Quick links:




Wishart, D., Jewison, T., Guo, A., Wilson, M., Knox, C., Liu, Y., Djoumbou, Y., Mandal, R., Aziat, F., Dong, E., Bouatra, S., Sinelnikov, I., Arndt, D., Xia, J., Liu, P., Yallou, F., Bjorndahl, T., Perez-Pineiro, R., Eisner, R., Allen, F., Neveu, V., Greiner, R., & Scalbert, A. (2012). HMDB 3.0–The Human Metabolome Database in 2013 Nucleic Acids Research, 41 (D1) DOI: 10.1093/nar/gks1065

Xia, J., Broadhurst, D., Wilson, M., & Wishart, D. (2012). Translational biomarker discovery in clinical metabolomics: an introductory tutorial Metabolomics, 9 (2), 280-299 DOI: 10.1007/s11306-012-0482-9

Video Tip of the Week: gene.iobio for genome and variation browsing

Twitter erupted recently with some chatter about a new tool that people seemed to really like. The iobio team from the Marth lab had launched a new gene “app” on their iobio framework. Here was some of the response:

So of course I wanted to have a look. And I agree–it is a very slick tool, and fun to explore. If you want to get started, check out their gene.iobio announcement blog post for a bit of the goals and features in text form:

Gene.iobio is designed to help medical and clinical researchers hunt for disease-causing genetic variants through a combination of real-time genomic data analysis and intuitive visualization.

And they invite you to watch their intro video to get a sense of how it works. Their overview video is our tip this week:

They have other videos as well, on more specific use cases, via their YouTube channel. There is one publication I could find, too, which gives you some of the background on their goals and intentions for their software and app ecosystem (linked below). I like this summary of their goals from the paper, I think it helps you to understand these nifty and speedy tools:

We have developed and are continually expanding a web-based analysis system, iobio (, to empower all biological researchers to analyze—easily, interactively and in a visually driven manner—large biomedical data sets that are essential for their research, without onerous resource requirements.

On their blog they also talk about some of the other apps they already have–bam.iobio and vcf.iobio. They also note that they provide Docker containers to make it very easy for you to deploy your own installation if you’d like to have one. (If you are new to Docker, we’ve done tips on that too: intro, and a bioinformatics application example). And they note in their paper that they intend to have other folks use their libraries to create more apps that have similar features. So if people like this, and they seem to so far, you may see more of these tools coming along. Check ‘em out.

Quick links:

iobio main site:



Miller, C., Qiao, Y., DiSera, T., D’Astous, B., & Marth, G. (2014). bam.iobio: a web-based, real-time, sequence alignment file inspector Nature Methods, 11 (12), 1189-1189 DOI: 10.1038/nmeth.3174


Video Tip of the Week: World Tour of Genomics Resources, part II

This week’s tip is not our usual short video. We’ll connect you to our newest tutorial suite, our World Tour of Genomics Resources, part II. Our previous tour was really popular–because as much as bench researchers know about the tools they currently use–everyone realizes there are more tools out there. And many of them don’t realize that there could be some very handy ones for tasks that they have.

This time the tour discusses not only tools for which we have full tutorial suites (video, slides, handouts, exercises), but also a lot of the handy problem-solving tools that we cover in our weekly tips. Things like UpSet for exploring data relationships among sets–which scales way better than Venn diagrams for genomics data sets. Or like Slidify to make slides from RStudio directly. We won’t have full training suites on these, but people will find them really useful in their daily work.

Sometimes we will also add tips about tools for which we have suites, but that have new features. For example, although thousands of people watch our UCSC Genome Browser full trainings, we also have tips that highlight new features or tools that aren’t part of the basic intro–such as new wiggle track features, or the Genome Browser in a Box. So we help people keep current in the field this way, even with existing tools they use.

But still we adhere to our philosophy that we explained in our paper (below). Raising awareness of tools that are out there, and help with how to find and use them effectively. This World Tour illustrates that.


Quick links:

New tutorial suite:

Williams, J., Mangan, M., Perreault-Micale, C., Lathe, S., Sirohi, N., & Lathe, W. (2010). OpenHelix: bioinformatics education outside of a different box Briefings in Bioinformatics, 11 (6), 598-609 DOI: 10.1093/bib/bbq026


Video Tip of the Week: Araport, Arabidopsis Portal

The recent Plant Biology 2015 conference tweets were full of delightful morsels (). Some of them edible. I am very psyched to learn of the Legume Federation. Legumes are *way* at the top of my list of favorite organisms. I think it was their tweet of the Araport data that led to this week’s video tip of the week.

Araport was new to me. I had been familiar with TAIR, and I knew of some of their changes after the funding went away. But this got me looking into how the community was re-organizing to support the plant model organism data, and also providing supporting tools and new directions.

The paper (below, 2014) describes the foundations and some of the transition issues from TAIR. And it also describes some of the tools that they are making available going forward. They have a customized InterMine called ThaleMine that can help you make customized queries to mine the data. There’s a JBrowse for visual browsing of the genome data. They are also maintaining a GBrowse with the Arabidopsis data, and they have a page of browser comparisons. There’s BLAST for sequence-based searching.

But what’s also very cool is that they are also making this a framework for people to build their own apps around. This “community contributed modules” is a great idea. So tools that folks may need for their particular research directions can be built right on top of the Araport setup.

This week’s tip is the ThaleMine intro video that they have provided.

I would keep an eye out for more videos from them in the future as well. There’s also a   Presentations page, and if you want more of an overall look at their foundations and plants there’s a nice overview slide deck from the ICAR 2015 conference.

Check out the Araport resources for arabidopsis and plant genomics tools.

Quick links:



Hanlon, M., Vaughn, M., Mock, S., Dooley, R., Moreira, W., Stubbs, J., Town, C., Miller, J., Krishnakumar, V., Ferlanti, E., & Pence, E. (2015). Araport: an application platform for data discovery Concurrency and Computation: Practice and Experience DOI: 10.1002/cpe.3542

Krishnakumar, V., Hanlon, M., Contrino, S., Ferlanti, E., Karamycheva, S., Kim, M., Rosen, B., Cheng, C., Moreira, W., Mock, S., Stubbs, J., Sullivan, J., Krampis, K., Miller, J., Micklem, G., Vaughn, M., & Town, C. (2014). Araport: the Arabidopsis Information Portal Nucleic Acids Research, 43 (D1) DOI: 10.1093/nar/gku1200