Category Archives: Tip of the Week

Video Tip of the Week: GOLD, Genomes OnLine Database

Yes, I know some people suffer from YAGS-malaise (Yet Another Genome Syndrome), but I don’t. I continue to be psyched for every genome I hear about. I even liked the salmon lice one. And Yaks. The crowd-funded Puerto Rican parrot project was so very neat. These genomes may not matter much for your everyday life, and may not exactly be celebrities among species. But we’ll learn something new and interesting from every one of them. It’s also very cool that it’s bringing new researchers, trainees, and citizens into the field.

The good news is there is opportunity still for many, many more species. And decreasing costs will make it possible for more research teams to do locally-important species. But–it would be a shame if we wasted resources by doing 30 versions of something cute, rather than tackling new problems. A central registry for sequencing projects may help to manage this. Genomes OnLine Database has been cataloging projects for years, and it would be great if folks would register their research there.

I was reminded of this by a tweet I saw come through my #bioinformatics column. This is what I saw flying by:

As much as I enjoy Twitter and think that science nerds are pretty good at it, it’s hard to know if the right people will see a tweet. Anyway, I suggested that this researcher check out GOLD and BioProject to see if anyone had registered anything.

I realized that although we have talked about GOLD in the past, it hadn’t been highlighted in our Tips of the Week before. So here I will include a video from a talk about GOLD. Ioanna Pagani gives an overview of GOLD, the foundations and the purpose. And then she goes on to demonstrate how to enter project metadata into their registry (~12min). Watching this will help you to understand the usefulness of GOLD, and what you can expect to find there. She describes both single-species project entry, and another option for entering metagenome data projects (~25min).

In the News at GOLD, they mention that their update this summer resulted in some changes to the interface–so the specifics might be a bit different from the video. But the basic structural features are still going to be useful to understand the goals and strategies. It may also help to convey the importance of appropriate metadata for genome projects. If you are involved with these projects, checking out the team’s paper on the structure and use of metadata is certainly worthwhile.

In times of all this sequencing capacity, people are going to start looking for new organisms to cover. Of course, some people will want to look at another strain, isolate, geographical sample for good reasons–but keeping a lot of unnecessary duplication from happening would be nice too. And it would be great if submitters also conformed to the standards for genome metadata–the ‘Minimum Information about a Genome Sequence’ (MIGS, now in the broader collection of standard checklists in the MIxS project) standards being developed by the Genomic Standards Consortium. (You can see how GOLD conformed to this in their other paper below.) Let’s spread the resources around to get new knowledge when we can. I would like to see a more formal mechanism that connects people who have some genome of interest with researchers who might have the bandwidth to do it, as well. Social sequencing?

Quick links:

GOLD: http://www.genomesonline.org

Genomics Standards Consortium: http://gensc.org/

References:
Pagani I., J. Jansson, I.-M. A. Chen, T. Smirnova, B. Nosrat, V. M. Markowitz & N. C. Kyrpides (2011). The Genomes OnLine Database (GOLD) v.4: status of genomic and metagenomic projects and their associated metadata, Nucleic Acids Research, 40 (D1) D571-D579. DOI: http://dx.doi.org/10.1093/nar/gkr1100

Liolios K., Lynette Hirschman, Ioanna Pagani, Bahador Nosrat, Peter Sterk, Owen White, Philippe Rocca-Serra, Susanna-Assunta Sansone, Chris Taylor & Nikos C. Kyrpides & (2012). The Metadata Coverage Index (MCI): A standardized metric for quantifying database metadata richness, Standards in Genomic Sciences, 6 (3) 444-453. DOI: http://dx.doi.org/10.4056/sigs.2675953

Field D., Tanya Gray, Norman Morrison, Jeremy Selengut, Peter Sterk, Tatiana Tatusova, Nicholas Thomson, Michael J Allen, Samuel V Angiuoli & Michael Ashburner & (2008). The minimum information about a genome sequence (MIGS) specification, Nature Biotechnology, 26 (5) 541-547. DOI: http://dx.doi.org/10.1038/nbt1360

Video Tip of the Week: #Docker, shipping containers for software and data

Breaking into the zeitgeist recently, Docker popped into my sphere from several disparate sources. Seems to me that this is a potential problem-solver for some of the reproducibility and sharing dramas that we have been wrestling with in genomics. Sharing of data sets and versions of analysis software is being tackled in a number of ways. FigShare, Github, and some publishers have been making strides among the genoscenti. We’ve seen virtual machines offered as a way to get access to some data and tool collections*. But Docker offers a lighter-weight way to package and deliver these types of things in a quicker and straightforward manner.

One of the discussions I saw about Docker came from Melissa Gymrek, with this post about the potential to use it for managing these things: Using docker for reproducible computational publications. Other chatter led me to this piece as well: Continuous, reproducible genome assembler benchmarking. And at the same time as all this was bubbling up, a discussion on Reddit covered other details: Question: Does using docker hit performance?

Of course, balancing the hype and reality is important, and this discussion thrashed that about a bit (click the timestamp on the Nextflow tweet to see the chatter):

To get a better handle on the utility of Docker, I went looking for some videos, and these are now the video tip of the week. This is different from our usual topics, but because users might find themselves on the receiving end of these containers at some point, it seemed relevant for our readers.

The first one I’ll mention gave me a good overview of the concept. The CTO of Docker, Solomon Hykes, talks at Twitter University about the basis and benefits of their software (Introduction to Docker). He describes Docker of being like the innovation of shipping containers–which don’t really sound particularly remarkable to most of us, but in fact the case has been made that they changed the global economy completely. I read that book that Bill Gates recommended last year, The Box, and it was quite astonishing to see how metal boxes changed everything. This brought standardization and efficiencies that were previously unavailable. And those are two things we really need in genomics data and software.

Hykes explains that the problem of shipping stuff–coffee beans, or whatever, had to be solved, at each place the goods might end up. This is a good analogy–like explained in the shipping container book. How to handle an item, appropriate infrastructure, local expertise, etc, was a real barrier to sharing goods. And this happens with bioinformatics tools and data right now. But with containerization, everyone could agree on the size of the piece, the locks, the label position and contents, and everything standardized on that system. This brought efficiency, automation, and really changed the world economy. As Hykes concisely describes [~8min in]:

“So the goal really is to try and do the same thing for software, right? Because I think it’s embarrassing, personally, that on average, it’ll take more time and energy to get…a collection of software to move from one data center to the next, than it is to ship physical goods from one side of the planet to the other. I think we can do better than that….”

This high-level overview of the concept in less than 10min is really effective. He then takes a question about Docker vs a VM (virtual machine). I think this is the essential take-away: containerizing the necessary items  [~18min]:

“…Which means we can now define a new unit of software delivery, that’s more lightweight than a VM [virtual machine], but can ship more than just the application-specific piece…”

After this point there’s a live demo of Docker to cover some of the features. But if you really do want to get started with Docker, I’d recommend a second video from the Docker team. They have a Docker 101 explanation that covers things starting from installation, to poking around, destroying stuff in the container to show how that works, demoing some of the other nuts and bolts, and the ease of sharing a container.

So this is making waves among the genomics folks. This also drifted through my feed:

Check it out–there seem to be some really nice features of Docker that can impact this field. It doesn’t solve everything–and it shouldn’t be used as an escape mechanism to not put your data into standard formats. And Melissa addresses a number of unmet challenges too. But it does seem that it can be a contributor to reproducibility and access to data issues that are currently hurdles (or, plagues) in this field. Docker is also under active development and they appear to want to make it better. But sharing our stuff: it’s not trivial–there are real consequences to public health from inaccessible data and tools (1). But there are broader applications beyond bioinformatics, of course. And wide appeal and adoption seems to be a good thing for ongoing development and support. More chatter on the larger picture of Docker:

And this discussion was helpful: IDF 2014: Bare Metal, Docker Containers, and Virtualization.

And, er…

I laughed. And wrote this anyway.

Quick links:

Docker main site: http://www.docker.com/

Docker Github: http://github.com/docker/

Reference:
(1) Baggerly K. (2010). Disclose all data in publications, Nature, 467 (7314) 401-401. DOI: http://dx.doi.org/10.1038/467401b

*Ironically, this ENCODE VM is gone, illustrating the problem:

encodevm_gone

Video Tip of the Week: NIH 3D Print Exchange

The other day I was joking about how I was 3D-printing a baby sweater–the old way, with yarn and knitting needles. And I also mentioned that I assumed my niece-in-law was 3D-printing the baby separately. I’ve been musing (and reading) about 3D printing a lot lately–sometimes the plastic model part, sometimes the bioprinting of tissues part. So when I came across this new NIH 3D Print Exchange information, it seemed worthy of highlighting.

Although I haven’t had access to a 3D printer setup yet (although I’m planning to take a course soon at the local Artisan Asylum), I’ve been seeing quite a bit of chatter about it. Some folks are designing gel combs (rather than paying ridiculous catalog prices). Some folks print skulls and other bones. There is so much opportunity for a wide range of helpful scientific applications across many fields that it seems an introduction to this topic would be wise for a lot of folks.

So when someone pointed me to the 3D printing initiative at NIH, I was hooked. The public announcement and site launch was in mid-June, according to their blog and press release. I was catching up by reading other items on their site, including some press coverage that provides context for this and other government initiatives on 3D printing. Make Magazine’s piece “The Scramble To Build Thingiverse.gov is On!” notes that the Smithsonian and NASA also have projects underway. But for me, molecules in 3D are what I’m most interested in, so I’ll focus on this NIH version below.

An intro video provides an overview of the kinds of things that will be available on their site. But there’s also a YouTube channel with more.

At the site now you will find a number of ways to get started. At the “Share” navigation area you will find already there is a section for custom lab gear, anatomical stuff, and biological structures and even some organisms. So if you have models to share, you can load ‘em up. With the “Create” space you can quickly generate some items with a handy quick start feature. Because I’m fascinated with the beautiful structures of hemolysins (have you seen these things?) I picked one out, entered a PDB ID, and within a half hour I was notified that the printable model was available to me–and you can see it here. But you can build your own from scratch as well, of course. There are other tutorials that will help you get some foundations in place.

Hemolysin 3D printable modelOr you can look around–from the “Discover” page you can browse or search for examples of models people have done. At this time, there are 347 (including the one I just did yesterday). But there will be more. I want to get mine printed up, and then see some other proteins too.

Ok, so it’s not like I made a kidney or something (although we know that day is coming). Being able to think about the 3D printing process, file types, and various options are probably worth noodling on. Getting your feet wet with a little protein structure or organelle might be a good way to get started. Check it out, and start thinking in other dimensions.

Quick links:

NIH 3D Print Exchange: http://3dprint.nih.gov/

Hemolysin for image: http://www.pdb.org/pdb/explore/explore.do?structureId=3B07

Model Generated for hemolysin from PDB record: http://3dprint.nih.gov/discover/3dpx-000507

Reference:
Murphy S.V. (2014). 3D bioprinting of tissues and organs, Nature Biotechnology, 32 (8) 773-785. DOI: http://dx.doi.org/10.1038/nbt.2958

Video Tip of the Week: Phenoscape, captures phenotype data across taxa

Development of the skeleton is a good example of a process that is highly regulated, requires a lot of precision, is conserved and important relationships across species, and is fairly easy to detect when it’s gone awry. I mean–it’s hard to know at a glance if all the neurons in an organism got to the right place at the right time or if all the liver cells are in the right place still. But skeletal morphology–length, shape, location, abnormalities can be apparent and are amenable to straightforward observations and measurements. Some of these have been collected for decades by fish researchers. This makes them a good model for creating a searchable, stored, phenotype collection.

The team at Phenoscape is trying to wrangle this sort of phenotype information. I completely agree with this statement of the need:

Although the emphasis has been on genomic data (Pennisi, 2011), there is growing recognition that a corresponding sea of phenomic data must also be organized and made computable in relation to genomic data.

They have over half a million phenotype observations cataloged. These include observations in thousands of fish taxa. They created and used an annotation suite of tools called Phenex to facilitate this. They describe Phenex as:

Annotation of phenotypic data using ontologies and globally unique taxonomic identifiers will allow biologists to integrate phenotypic data from different organisms and studies, leveraging decades of work in systematics and comparative morphology.

That’s great data to capture to provide important context for all the sequencing data we are now able to obtain. I think this is a nice example of combining important physical observations, mutant studies, and more, with genomics to begin to get at questions about evolutionary relationships among genes and regulatory regions that aren’t obvious only from the sequence data. You may not be personally interested in fish skeletons–but as an informative way to think about structuring these data types across species to make them useful for hypothesis generation–this is a useful example.

Here’s a intro video provided by the Phenoscape team that walks you through a search starting with a gene of interest, and taking you through the kinds of things you can find.

So have a look around Phenoscape to see a way to go from the physical observations of phenotype to gene details, or vice versa.

Quick links:

Phenoscape: http://phenoscape.org/

Phenex: http://phenoscape.org/wiki/Phenex

References:
Mabee B.P., Balhoff J.P., Dahdul W.M., Lapp H., Midford P.E., Vision T.J. & Westerfield M. (2012). 500,000 fish phenotypes: The new informatics landscape for evolutionary and developmental biology of the vertebrate skeleton., Zeitschrift fur angewandte Ichthyologie = Journal of applied ichthyology, PMID: http://www.ncbi.nlm.nih.gov/pubmed/22736877

Balhoff J.P., Cartik R. Kothari, Hilmar Lapp, John G. Lundberg, Paula Mabee, Peter E. Midford, Monte Westerfield & Todd J. Vision (2010). Phenex: Ontological Annotation of Phenotypic Diversity, PLoS ONE, 5 (5) e10500. DOI: http://dx.doi.org/10.1371/journal.pone.0010500

Video Tip of the Week: Immune Epitope DB (IEDB)

This week’s tip was inspired by the recent NHGRI workshop of the future directions for funding and resourcing of genomics-related projects. Titled “Future Opportunities for Genome Sequencing and Beyond: A Planning Workshop for the National Human Genome Research Institute” brought together a lot of influential folks on this topic, and had them noodle on the priorities and major gaps in this arena that should get more attention going forward.

Much of the meeting was live-streamed, which was really great. You can see the video segments and sometimes the slides are available on the workshop page. One of the great things about this meeting was that there’s so much excitement about what scientists want to do, and all the terrific ideas that are out there. One of my personal favorites was the Human Cell Atlas presented by Aviv Regev. I’d love to work on that. I loved working on the Adult Mouse Anatomical Dictionary and Gene Expression Database at Jax.

But for today’s focus, I’ll turn to a totally different aspect of genomics research that intrigues me–the immune system. As an undergraduate in microbiology and immunology, the fact that microbes and their teeny genomes could wreak havoc on large mammals fascinated me (Ebola–I mean, seriously, it’s not that big). And that the hosts have developed the mix-and-match adaptable response and antibody system to do battle–clever stuff, as long as it doesn’t turn into an autoimmune situation…. But this could also be turned to good use if you want to battle cancer cells with immunotherapies. So when David Haussler’s talk brought that back around–the idea of the complexity of the immune response genomics which is not well characterized yet–I connected with that idea as well. And it struck me that I had not ever featured the Immune Epitope Database before, which Haussler had mentioned in his talk. It was also noted that this is an interesting system because it is also a hybrid of proteomics and genomics information that’s required to be wrangled. And if this is a direction that NHGRI will emphasize, it’s important to know what’s out there, and think about the ways to go forward.

So here’s Haussler’s talk to set the foundation, but there’s another video about the database I’ll point to below.

In this talk he mentioned NetMHC for peptide binding prediction as well, and ImmPort at NIAID. There was a quick mention of an unfunded prototype UCSC immunobrowser to keep an eye out for. And for the most part these resources aren’t new–you can find a number of publications that go back and describe the foundations and development over the years. And it seems to be a good solid foundation, and with appropriate support can continue to keep this important information coming.

To learn more about IEDB, you can access their documentation, which includes a whole list of video tutorials. Here I’ll highlight the intro/overview one–but there are others that offer specific guidance on other tasks. I can’t embed this one, so the link will take you over to the video at their site.

Click the image to visit the video page.

Click the image to visit the video page.

So have a look at the IEDB resources, and think about the future directions of this important aspect of genomics.

Quick links:

NHGRI workshop: http://www.genome.gov/27558042

IEDB: http://www.iedb.org/

Intro IEDB video: http://www2.immuneepitope.org/videos/site_overview.cfm

NetMHC: http://www.cbs.dtu.dk/services/NetMHC/

ImmPort: http://immport.niaid.nih.gov/

References:

Vita R., J. A. Greenbaum, H. Emami, I. Hoof, N. Salimi, R. Damle, A. Sette & B. Peters (2010). The Immune Epitope Database 2.0, Nucleic Acids Research, 38 (Database) D854-D862. DOI: http://dx.doi.org/10.1093/nar/gkp1004

Kim Y., Z. Zhu, D. Tamang, P. Wang, J. Greenbaum, C. Lundegaard, A. Sette, O. Lund, P. E. Bourne & M. Nielsen & (2012). Immune epitope database analysis resource, Nucleic Acids Research, 40 (W1) W525-W530. DOI: http://dx.doi.org/10.1093/nar/gks438

Lundegaard C. & M. Nielsen (2008). Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers, Bioinformatics, 24 (11) 1397-1398. DOI: http://dx.doi.org/10.1093/bioinformatics/btn128

Bhattacharya S., Linda Gomes, Patrick Dunn, Henry Schaefer, Joan Pontius, Patty Berger, Vince Desborough, Tom Smith, John Campbell & Elizabeth Thomson & (2014). ImmPort: disseminating data to the public for the future of immunology, Immunologic Research, 58 (2-3) 234-239. DOI: http://dx.doi.org/10.1007/s12026-014-8516-1

Video Tip of the Week: EpiViz Genome Browsing (and more)

This is the browser I’ve been waiting for. Stop what you are doing right now and look at EpiViz. I’ll wait.

I spend a lot of time looking at visualizations of various types of -omics data, from a number of different sources. I’ve never believed in the “one browser to rule them all” sort of thing–I think it’s important for groups to focus on special areas of data collection, curation, and visualizion. Although some parts can be reused and shared, of course, some stuff just should be viewed win certain species or strategies that don’t always end up nicely in a “track” of data that you can slap on some browser.

My dreams of this began in earnest with the Caleydo tools I’ve been talking about for a long time. Years ago I began imagining genome browser data in one panel, pathway maps in the nearby one, TF motifs, an OMIM page loaded up, and other stuff that was all part of my train-of-thought on some issue. They Caleydo team has continued on this path, and their EnRoute and Entourage tools get part of that way too. You can do some of that with the nifty BioGPS layouts. I also love the idea of looking at multiple genomic regions at the same time, in the manner that the Multi-Image Genome viewer (MIG) enables.

So we are getting closer and closer. And this EpiViz tool is an excellent demonstration of how to combine necessary genome track data visualizations and other analysis strategies into one viewer. It also allows other types of data to come in, with the Data-Driven Documents tools. You should read the paper, you should try out their software, and have a look at this overview video the EpiViz team has provided to get started.

Off we go. More like this please.

Quick links:

EpiViz Browser example: http://epiviz.cbcb.umd.edu/

EpiViz main site: http://epiviz.github.io/

References:

Chelaru F., Smith L., Goldstein N. & Bravo H.C. (2014). Epiviz: interactive visual analytics for functional genomics data., Nature methods, PMID: http://www.ncbi.nlm.nih.gov/pubmed/25086505

Video Tip of the Week: Biodalliance browser with HiSeq X-Ten data

Drama surrounding the $1000 genome erupts every so often, and earlier this year when the HiSeq X Ten setup was unveiled there was a lot of chatter–and questions: Is the $1,000 genome for real? And some push-back on the cost analysis: That “$1000 genome” is going to cost you $72M. A piece that offers nice framework for the field of play is here: Welcome to the $1,000 genome: Mick Watson on Illumina and next-gen sequencing. Aside from the media flurry, though, what matters is the data. And not many people have had access to the data yet.

Via Gholson Lyon, I heard about access to some:

A set of collaborators (The Garvan Institute of Medical Research, DNAnexus and AllSeq) have provided a test data set from the X Ten. I’ll let them describe this effort:

Take advantage of this unique opportunity to explore X Ten data.

The Garvan Institute of Medical Research, DNAnexus and AllSeq have teamed up to offer the genomics community open access to the first publicly available test data sets generated using Illumina’s HiSeq X Ten, an extremely powerful sequencing platform.  Our goal is to provide sample data that will allow you to gain a deeper understanding of what this technological advancement means for your work today and in the future.

My focus won’t be this data itself–but if you are interested in many of the technical aspects of this system and their process, have a listen to this informative presentation by Warren Kaplan from Garvan:

The sample data is derived from a cell line, the GM12878 cells. These cells are from the Coriell Repository here: Catalog ID: GM12878. Conveniently, this is one of the Tier 1 cell lines from the ENCODE project too, so there is other public data out there on this cell line–which I have explored in the past and knew some things about.

There are 2 different data sets of the sequence in the download files, and one of them is available in the browser to view. I’m sure the Genoscenti will be all over the downloadable files. But because I’m always interested new visualizations, I wanted to explore the genome browser they made available. Although I had heard of Biodalliance before, we hadn’t highlighted it as a tip, so I thought that would be interesting to explore. Biodalliance is a flexible, embeddable, extensible system that’s worth a look on it’s own, besides delivering this test data. And if you come by at a later date and the X Ten data is no longer available, go over to their site for nice sample data sets. Their “getting started” page has a nice intro to the features.

In the video, I’ll just take a quick test drive around some of the visualization features with the X-Ten GM12878 data. I’ll look at a couple of sample regions, one with the SOD1 gene just to illustrate the search and the tracks. And I’ll look at a region that I knew from the previous ENCODE CNV data had a homozygous deletion to see how that looked in this data set. (If you want to look for deletions later, search for the genes OR2T10 or UGT2B17).

Note: the data is time-sensitive–apparently it’s only available until September 30 2014. So get it while it’s hot, or browse around now.

Quick Links:

Test data site: http://allseq.com/x-ten-test-data

Biodalliance browser software details: http://www.biodalliance.org/

References:

Down T.A. & T. J. P. Hubbard (2011). Dalliance: interactive genome viewing on the web, Bioinformatics, 27 (6) 889-890. DOI: http://dx.doi.org/10.1093/bioinformatics/btr020

Check Hayden E. (2014). Is the $1,000 genome for real?, Nature, DOI: http://dx.doi.org/10.1038/nature.2014.14530

Dunham I., Shelley F. Aldred, Patrick J. Collins, Carrie A. Davis, Francis Doyle, Charles B. Epstein, Seth Frietze, Jennifer Harrow, Rajinder Kaul & Jainab Khatun & (2012). An integrated encyclopedia of DNA elements in the human genome, Nature, 489 (7414) 57-74. DOI: http://dx.doi.org/10.1038/nature11247

Garvan NA12878 HiSeqX datasets by The Garvan Institute of Medical Research, DNAnexus and AllSeq is licensed under a Creative Commons Attribution 4.0 International License

Video Tip of the Week: PhenDisco, “phenotype discoverer” for dbGap data

The dbGaP, database of Genotypes and Phenotypes, repository at NCBI collects information from research projects that link genotype and phenotype information and human variation, across many different types of studies, providing leads on variation that may be important to understand clinical issues. Some of the data is publicly available de-identified patient information, and some of the data requires authorization to access. This is valuable information, certainly, but I know I’ve heard folks grouse about how challenging it can be to locate specific things you might be interested it, because of a lack of standardization of some of the aspects of the project details.

The developers of PhenDisco were aware of the challenges of extracting the information out of dbGaP, and they chose to investigate ways make searches for key data more effective. They looked at requests that had come in to dbGaP. They surveyed researchers who would represent typical users, and found that the way to make the mining of dbGaP easier would be to standardize a lot of the aspects of the project descriptions and data. They thought through use-case scenarios. And once the standardization was completed, a new query interface relying on these new descriptors was made available as well.

For the foundations of the project and how they went about it, you should read their paper (linked below). But for this week’s video tip, I’ll include a couple of things that this group has delivered to help people understand their project and use their site. If you want the short version about how to approach the site, this YouTube video will cover that (erm, and I’m sorry about the actual disco music….):

But if you have time for the longer form, there’s a webinar they delivered that I’ll include here as well. Part of this webinar is the video from YouTube, but the details are easier to see in the YouTube version so I’d encourage watching that and skipping that piece of the webinar.

So have a look at the PhenDisco if you’ve been finding searchers of dbGaP have been less satisfying than you’d hoped. I think one of the best ways to grasp the standardization is to have a look at their advanced search page to see what types of things are selectable there. Try some searches and see if it’s helpful for your research.

Just wanted to add a link to a slide set from a journal club presentation on PhenDisco as well, in case the videos aren’t ideal for your situation. There is also a separate video of that journal club.

 

If this is a type of resource you find useful, you might also want to explore the PheGenI (Phenotype-Genotype Integrator) that I covered in a previous Tip of the Week too.

Quick links:

Project overview page: http://pfindr.net/

Search engine main page: http://phendisco.ucsd.edu/

Advanced search page to understand the structure: http://phendisco.ucsd.edu/AdvanceSearchPage.html

References:

Doan S., Lin K.W., Conway M., Ohno-Machado L., Hsieh A., Feupe S.F., Garland A., Ross M.K., Jiang X. & Farzaneh S. & (2013). PhenDisco: phenotype discovery system for the database of genotypes and phenotypes., Journal of the American Medical Informatics Association : JAMIA, PMID: http://www.ncbi.nlm.nih.gov/pubmed/23989082

Tryka K.A., A. Sturcke, Y. Jin, Z. Y. Wang, L. Ziyabari, M. Lee, N. Popova, N. Sharopova, M. Kimura & M. Feolo & (2013). NCBI’s Database of Genotypes and Phenotypes: dbGaP, Nucleic Acids Research, 42 (D1) D975-D979. DOI: http://dx.doi.org/10.1093/nar/gkt1211

Video Tip of the Week: VectorBase, for invertebrate vectors of human pathogens

I wish I had been clever enough to coordinate this week’s Video Tip of the Week with “Mosquito Week” a couple of months back. There was a bunch of chatter at that time about this infographic that was released by Bill Gates, which illustrated the contribution of various human-killing species. The mosquito was deemed: The Deadliest Animal in the World. Jonathan Eisen took issue with the numbers, however, noting that if you are consistent about the way you count disease vectors, humans come out on top (or, bottom, I guess, in this category). Still, Eisen noted, mosquitoes are important and demand attention. But there are lots of other vectors to keep in mind as well.

Luckily, the team at VectorBase is on it. VectorBase has been providing information on invertebrate vectors of human pathogens for a long time. They collect a variety of species data, including mosquitoes, but also a lot more–ticks, lice, flies, etc. Check out their list of organisms here: https://www.vectorbase.org/organisms . They have information not only on basic biology, but also information about the very key problems of resistance to insecticides as well.

We’ve been fans of VectorBase for years, and have highlighted them in the past, after a site redesign a couple of years ago, and a few other times with various other news tidbits. But I was delighted to discover recently that they have a new overview video which is my favorite kind to highlight in these tips. If you are new to a resource, a brief overview is the most helpful way to understand the kinds of data and tools you’ll see at their site. They have a lot of other slide/PDF tutorials as well, which focus on specific tools and features that will supplement an overview. But in our experience, a video overview is a bit more tempting when you are first becoming acquainted with a resource.

So here I’ve embedded the VectorBase overview, which you can also find here: https://www.vectorbase.org/tutorials/tour. The slides to accompany it are also available there.

So have a look at VectorBase’s important collection of species data and tools. You can also read more about their foundations and directions in their publications, including the one below. I keep up with news about their new features from their newsletter, but you can also see other types of community outreach strategies over at their site.

Quick link:

VectorBase: www.vectorbase.org

Reference:

Megy K., D. Lawson, D. Campbell, E. Dialynas, D. S. T. Hughes, G. Koscielny, C. Louis, R. M. MacCallum, S. N. Redmond & A. Sheehan & (2012). VectorBase: improvements to a bioinformatics resource for invertebrate vector genomics, Nucleic Acids Research, 40 (D1) D729-D734. DOI: http://dx.doi.org/10.1093/nar/gkr1089

Bonus video: The Gates blog hosted this highly-produced video about mosquito bites and their impact.

Video Tip of the Week: Google Genomics, API and GAbrowse

This week’s video tip comes to us from Google–it’s about their participation in the “Global Alliance for Genomics and Health” coalition. Global Alliance is aimed at developing genomic data standards for interoperability, and they’ve been working on creating the framework (some background links below in the references will provide further details). It has over 170 members, and one of these members is Google. Although Google talked about this earlier this year when they joined this group, more recently pieces have begun to emerge about the directions and specific tools. Google’s efforts made the mainstream news recently in their announcement about working on a project to examine genomic data associated with autism.

Although this video doesn’t talk about a single specific tool like we usually cover, it provides more detail about this framework for building tools which is important to understand. And in this video I learned about a new browser developed under this project that I did have a quick look at, and I’ll add below.

They browser that they reference is called GAbrowse–I assume that means Global Alliance browse–but there’s not a lot of detail. Their “about” dialog box says this:

GABrowse is a sample application designed to demonstrate the capabilities of the GA4GH API v0.1.

Currently, you can view data from Google, NCBI and EBI.

  • Use the button on the left to select a Readset or Callset.
  • Once loaded, choose a chromosome and zoom or drag the main graph to explore Read data.
  • Individual bases will appear once you zoom in far enough.

The code for this application is in GitHub and is a work in progress. Patches welcome!

I kicked the tires a bit, but it’s clearly not fully fleshed out at this point. When I tried to zoom up from the nucleotide level it went up a bit, but eventually you hit a point that says “This zoom level is coming soon!” So certainly there’s more to come, and a lot more functionality that would be necessary. But it’s early. And it’s just a demo. I have no idea if it’s intended to become a stand-alone public browser.

So if you are interested in issue of cross-compatibility of human genomic data (and as far as I can tell this is all human-centric, I’d like to see a wider conversation on this), it’s probably worth knowing what Google is offering here. You should also be aware of what the Global Alliance is working on. Below I’ve added some of the publications and media I’ve seen about their efforts.

Hat tip to Can Holyavkin on Google+ for the link to the video.  https://plus.google.com/u/0/114690993717100405711/posts/gwNy5E7E6Vb?cfem=1

Quick links:

Global Alliance for Genomics and Health: http://genomicsandhealth.org/

Google genomics: https://developers.google.com/genomics/

GAbrowse: http://gabrowse.appspot.com

Reference:
(2013). Global Alliance to Create Standards For Sharing Genomic Data, American Journal of Medical Genetics Part A, 161 (9) xi-xi. DOI: http://dx.doi.org/10.1002/ajmg.a.36168

Callaway E. (2014). Global genomic data-sharing effort kicks off, Nature, DOI: http://dx.doi.org/10.1038/nature.2014.14826

White paper 2013: http://www.broadinstitute.org/files/news/pdfs/GAWhitePaperJune3.pdf

Framework for Responsible Sharing of Genomic and Health-Related Data – DRAFT # 7 http://genomicsandhealth.org/our-work/work-products/framework-responsible-sharing-genomic-and-health-related-data-draft-7

Terry S.F. (2014). The Global Alliance for Genomics , Genetic Testing and Molecular Biomarkers, 18 (6) 375-376. DOI: http://dx.doi.org/10.1089/gtmb.2014.1555 [available here from GA: http://genomicsandhealth.org/files/public/gtmb%252E2014%252E1555%5B2%5D.pdf]