As you may know, we’ve been doing these video tips-of-the-week for SiX years now. We have completed or collected around 300 little tidbit introductions to various resources through this past year, 2013. At first we had to do all of our own video intros, but as the movie technology became more accessible and more teams made their own, we were able to find a lot more that were done by the resource providers themselves. So we began to collect those as well. At the end of the year we’ve established a sort of holiday tradition: we are doing a summary post to collect them all. If you have missed any of them it’s a great way to have a quick look at what might be useful to your work.
For decade now we’ve been offering our video tutorial suites to help people learn how to use bioinformatics resources. We’ve used a couple of delivery platforms, and we’ve changed the website a few times. But we also know that people like consistency with software, and if there are going to be major changes to the behavior of something, there better be a good reason.
We have a good reason. With the rise of mobile devices and the increasing use of them by students, our subscribers wanted us to make watching the tutorials on iPads and Androids and Surfaces more friendly. So we’re doing it.
This week’s video tip demonstrates the change to our tutorial movies that we’re rolling out. The basics are the same–each video offers details about how to use the software features at some database or tool site. We explain the display features, and the search mechanisms. We offer the video as well as the slides and some exercises to use as well. The only thing we’ve changed is the menu and controller options. The YouTube video here illustrates that.
So soon when you launch a tutorial video, you will have to swipe over the edges to access the menus and the slider. You can still click individual chapters, or move ahead with the controller. But those items move out of the way when you aren’t using them.
Everything else is the same. The landing pages for each tutorial suite will still have the launch buttons for all the items you need to access everything.
For subscribers, all of the suites will have this new functionality. If your site doesn’t have a subscription, you can still try it out on our sponsored training suites, such as: GenoCAD, OMIM, UCSC Genome Browser, or anything else from the “free” tutorials page: http://openhelix.com/free .
To learn more about our philosophy of training materials, you can check out our paper (below). Regular readers may already understand what we do, but if you are accessing these for the first time it might help you to know more about what we offer and how we do it.
Let us know if you have any issue with the new interface and we’ll take a look right away.
Williams J.M., Mangan M.E., Perreault-Micale C., Lathe S., Sirohi N. & Lathe W.C. (2010). OpenHelix: bioinformatics education outside of a different box, Briefings in Bioinformatics, 11 (6) 598-609. DOI: 10.1093/bib/bbq026
For this week’s Tip of the Week we highlight our new tutorial on OMIM, Online Mendelian Inheritance in Man. If you haven’t looked at OMIM for a while, or if you usually only think about it as a link in some other database you use, look again. There’s more there than you realize.
OMIM is one of the first online tools I became aware of way back in my career. That shared Mac in the back of the lab, with it’s teeny little screen–and accessing the link to OMIM from that NCBI interface–remember that old interface? Even then OMIM was a venerable resource with an unmatched collection of human genes, traits, and phenotype data. There was a great paper about the history of OMIM that Victor McKusik wrote about his own career and his work, and he recounts the beginnings of his human gene information collection and many other aspects of the human genetic knowledge realm. It’s a fascinating look at one guy’s path and influences that lead us to where we are today. But here’s the short history of OMIM as a computational resource:
Mendelian Inheritance in Man has been maintained on the computer since 1964. With the first print edition in 1966, it was a pioneer in computer-based publication. In the 1980s, MIM was prepared for online presentation, with a search engine that enhanced its usefulness. Online access, as OMIM, was provided generally beginning in 1987, first from the Welch Medical Library at Johns Hopkins and since December 1995 from the National Center for Biotechnology Information (NCBI) of the National Library of Medicine (27).
Because of how long OMIM® has been around and its utility and depth, it’s been incorporated into probably almost every bioinformatics resource you use around the world. I love the UCSC Genome Browser track option that you can turn on to supplement your look at genomic regions and quickly find disease-causing genes, for example. But just seeing a link to OMIM doesn’t give you the full scope of understanding of the features it offers. With the move away from the NCBI site, the OMIM team changed their interface quite a bit to offer a lot more features than they were able to before. New links to appropriate resources have been added. New ways to integrate knowledge have been provided.
Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…
RT @moorejh: #bioinformatics MT @brainpicker TreeVersity – interactive #visualization tool lets you compare tree diagrams http://t.co/09PMu6Oo [Mary]
BioStar is a site for asking, answering and discussing bioinformatics questions. We are members of thecommunity and find it very useful. Often questions and answers arise at BioStar that are germane to our readers (end users of genomics resources). Every Thursday we will be highlighting one of those questions and answers here in this thread. You can ask questions in this thread, or you can always join in at BioStar.
This question was raised in the last month, and there was some discussion–but there’s been a big change in the options since, and I wanted to highlight that so people know:
One of our project used to query OMIM data as XML through NCBI’s efetch utility, as described here for example:
[?]What is the best way to interact programmatically with OMIM?[?]
However, it seems the service has stopped functioning a few months ago. It now simply returns the following error:
Database: omim – is not supported
I can find no mention of an update to the API on NCBI’s website or anywhere else. At the same time, the pages accessible directly on OMIM’s website offer no link to structured data (XML or otherwise) and the downloadable file, while using some specific format to delimit fields, is still far from the flexibility of the former XML files (for example, it is impossible to retrieve metadata for each reference).
Is there currently any way to regain access to OMIM data in a structured, parsable format (XML…)?
There was a lot of discussion about the changes since OMIM at NCBI moved away from to its new spot at OMIM.org, but just this week I spotted a tweet from OMIM which directly answers the access issue now:
After again reading Daniel MacArthur’s good rundown about the state of databases of human disease-causing variation from last year (One database to hold them all), I thought it might be nice to do a tip comparing several of them. I couldn’t get it under our self-imposed 5 minute limit for our tips (and technical limit of software I’m using, but that’s about to change). But as I perused our tips and other sites, I found we and others have quite a list of how-to tips to use these databases. So in today’s tip I’ve gathered video tips for 3 of the databases listed in the linked post. Below those tips I’ll link to other how-to videos for additional human variation and disease.
HGMD has a public site and a by-subscription site. The latter includes access to the most current data and some added features. The publicly accessible site is out-of-date by three years. Because of HGMD restrictions, we aren’t able to do a tutorial or a tip on HGMD, but they do have an introduction video to their database:
Another excellent resource is Gen2Phen. The Gen2Phen project “aims to unify human and model organism genetic variation databases towards increasingly holistic views into Genotype-To-Phenotype (G2P) data, and to link this system into other biomedical knowledge sources via genome browser functionality.” In that vein, they have quite an extensive list of Locus-specific databases and additional resources.
” provides a central location for voluntary submission of genetic test information by providers. The scope includes the test’s purpose, methodology, validity, evidence of the test’s usefulness, and laboratory contacts and credentials. The overarching goal of the GTR is to advance the public health and research into the genetic basis of health and disease.”
I’m always interested in checking out new resources from NCBI, especially when it is my turn to do a weekly tip. Initially I figured that I would check out the GTR and post a video on how to use it – but the NCBI beat me to that. You can see their YouTube tips (there are two) by clicking the link on their homepage & learn some search tips, etc. [Note, the two videos continued to loop for me & I needed to stop them after viewing them once].
But the question that I came up with is, “What will the GTR provide me with that I am not already getting from other clinical resources that I use, and that OpenHelix trains on?” I try to address that question in my video by doing the same search, for “Cystic fibrosis”, at five different clinically-related resources, and discussing what each offers and specializes in doing. Of course, in a five minute video I can’t be comprehensive – either for resources or what they cover – but I think it will give you enough of a taste for you to appreciate what the GTR offers you, or to continue the comparison on your own.
The resources that I visit in the tip movie are: the GTR, GeneTests, the Genetic Home Reference (GHR), OMIM, and Orphanet. At each resource I do a basic search for the the disease “Cystic fibrosis” and show the initial results display that resulted. I don’t have time to compare the detailed reports available at each, but lower on the post I link to a reference on the resource (if available), as well as the landing page for OpenHelix training materials on the resource – since we have a tutorial on many of these resources. I also include direct links to each resource.
I’d suggest that you read the NIH News article on the GTR release for some background on the GTR. I won’t cover everything here, but there are a couple of paragraphs that I want to point your attention to. The first explains the relationship between GeneTests and GTR, and says:
“GTR is built upon data pulled from the laboratory directory of GeneTests, a pioneering NIH-funded resource that will be phased out over the coming year. GTR is designed to contain more detailed information than its predecessor, as well as to encompass a much broader range of testing approaches, such as complex tests for genetic variations associated with common diseases and with differing responses to drugs. GeneReviews, which is the section of GeneTests that contains peer-reviewed, clinical descriptions of more than 500 conditions, is also now available through GTR.”
It seems to be another case where it was deemed easier to start a new resource (GTR) than to try and revamp an old resource (GeneTests) to handle the amazing influx of new data. Often resources aren’t retired as soon as expected, due to user feedback, but it is important to note that GTR seems to be in place to eventually replace GeneTests. I assume the GeneReviews will still be edited by & copyright to the University of Washington, Seattle, but I don’t have a reference for that. The similar transition occurred for OMIM, which was hosted at NCBI for years but now has a new URL at Johns Hopkins (watch for our new tutorial on OMIM, which is currently in the works).
The second paragraph that I found particularly interesting was the one on what the GTR contains, and will contain. It states:
“In addition to basic facts, GTR will offer detailed information on analytic validity, which assesses how accurately and reliably the test measures the genetic target; clinical validity, which assesses how consistently and accurately the test detects or predicts the outcome of interest; and information relating to the test’s clinical utility, or how likely the test is to improve patient outcomes.”
I didn’t immediately find mention of who will provide the validity or utility information in the GTR documentation, which is currently under construction. It is clear that much of the content of the database will be “voluntarily submitted by test providers”, and it is stated that “NIH does not independently verify information submitted to the GTR; it relies on submitters to provide information that is accurate and not misleading.”, but I also saw that experts will input on GTR’s content regularly, as can be read here. The GTR team is also very interested in receiving input on the resource, which can be submitted through the GTR feedback form.
*OpenHelix tutorials for these resources available for individual purchase or through a subscription
For GeneTests (free from PMC) – Pagon RA (2006). GeneTests: an online genetic information resource for health care providers. Journal of the Medical Library Association : JMLA, 94 (3), 343-8 PMID: 16888670
For GHR (free from PMC) – Mitchell JA, Fomous C, & Fun J (2006). Challenges and strategies of the Genetics Home Reference. Journal of the Medical Library Association : JMLA, 94 (3), 336-42 PMID: 16888669
For OMIM (open access article) – Amberger, J., Bocchini, C., & Hamosh, A. (2011). A new face and new challenges for Online Mendelian Inheritance in Man (OMIM®) Human Mutation, 32 (5), 564-567 DOI: 10.1002/humu.21466
For Orphanet (full access requires subscription) - Aymé, S., & Schmidtke, J. (2007). Networking for rare diseases: a necessity for Europe Bundesgesundheitsblatt – Gesundheitsforschung – Gesundheitsschutz, 50 (12), 1477-1483 DOI: 10.1007/s00103-007-0381-9
Many people are probably familiar with OMIM, Online Mendelian Inheritance in Man. It’s one of the oldest online collections of human genetic traits around. But many people may not be aware that OMIM inspired OMIA–Online Mendelian Inheritance in Animals.
The team from the University of Sydney that develops and curates OMIA collects information on animal traits with a specific emphasis on non-laboratory animals and comparative biology. The species range is huge. You can see traits from water buffalo and from rainbow trout, and more. The amount of detail may vary–sometimes you will find just links to papers that describe a phenotype. But there are other cases where there are not only links to the papers, but the gene features may be available in that species with information about the molecular details. Further, if there is a human trait that may be related, they will link to the OMIM pages for those. The sample page that I used to illustrate this in the short video is Aranochomelia, and you can see these types of links and details.
You can access OMIA at the Australian site, but there is also a mirror available via NCBI. It contains the same information, but since it is integrated with other NCBI tools you can use your mad NCBI skillz to do custom queries of all sorts with limits and structured syntax, or save queries with your MyNCBI account, and more. Visit OMIA at NCBI for that access.
This week’s tip is a 5 minute look at the ways to access OMIA and explores sample records. Check them out. I think they are going to become increasingly important as “big data” projects like 10K Genomes, and numerous other next-gen sequencing projects, bring us access to hoards of new genomes. Many of those genomes are going to have limited information that we can use to annotate the features. OMIA could really help with that.
Nicholas, F. (2003). Online Mendelian Inheritance in Animals (OMIA): a comparative knowledgebase of genetic disorders and other familial traits in non-laboratory animals Nucleic Acids Research, 31 (1), 275-277 DOI: 10.1093/nar/gkg074
Lenffer, J., Nicholas FW., Castle K., Rao A., Gregory S., Poidinger M., Mailman MD., & Ranganathan S. (2006). OMIA (Online Mendelian Inheritance in Animals): an enhanced platform and integration into the Entrez search interface at NCBI Nucleic Acids Research, 34 (90001) DOI: 10.1093/nar/gkj152
Sayers, E., Barrett, T., Benson, D., Bolton, E., Bryant, S., Canese, K., Chetvernin, V., Church, D., DiCuccio, M., Federhen, S., Feolo, M., Fingerman, I., Geer, L., Helmberg, W., Kapustin, Y., Krasnov, S., Landsman, D., Lipman, D., Lu, Z., Madden, T., Madej, T., Maglott, D., Marchler-Bauer, A., Miller, V., Karsch-Mizrachi, I., Ostell, J., Panchenko, A., Phan, L., Pruitt, K., Schuler, G., Sequeira, E., Sherry, S., Shumway, M., Sirotkin, K., Slotta, D., Souvorov, A., Starchenko, G., Tatusova, T., Wagner, L., Wang, Y., Wilbur, W., Yaschenko, E., & Ye, J. (2011). Database resources of the National Center for Biotechnology Information Nucleic Acids Research, 40 (D1) DOI: 10.1093/nar/gkr1184
As you may know, we’ve been doing these video tips-of-the-week for FOUR years now. We have completed around 200 little tidbit introductions to various resources. At the end of the year we’ve established a sort of holiday tradition: we are doing a summary post to collect them all. If you have missed any of them it’s a great way to have a quick look at what might be useful to your work.
It’s probably just the human brain’s ability to connect dots & find patterns, but it can be interesting how many “unrelated” events and information bits accumulate in my head & eventually get mulled into an idea or theory. Take, for example, a recent biotech mixer, bits from an education leadership series & a past Nature article – each “event” has been meandering in my mind and now they are finding their way out as this blog post.
OK, now the explanation: At a recent local biotech event I heard about a company (KeraNetics) purifying keratin proteins & using them to develop therapeutic and research applications. The company & their research sounded very interesting & because a lot of it is aimed at aiding wounded soldiers, it also sounded directly beneficial. The talk was short, only about 20 minutes, so there wasn’t a lot of time for details or questions. I decided I’d venture forth through many of the bioscience databases and resources that I know and love, in order to learn more about keratin.
My quest was both fun and frustrating because of the nature of the beast – keratin is “well known” (i.e. it comes up in high school academic challenge competitions ‘a lot’, according to someone in the know), but is hard to work with (i.e. tough, insoluble, fibrous structural proteins) that is hard to find much general information on in your average protein database (because it is made of many different gene products, all referred to as “keratin”). I decided to begin my adventure at two of my favorite protein resources, PDB & SBKB, but I found no solved structures for keratin. Because of the way model organism databases are curated and organized, I often begin a protein search there, just to get some basic background, gene names, sequence information, etc. I (of course) found nothing other than a couple of GO terms in the Saccharomyces Genome Database (SGD), but I found hundreds of results in both Mouse Genome Informatics (MGI) (660 genomic features) and Rat Genome Database (RGD) (162 rat genes, 342 human genes). I also found gene names (Krt*), sequences and many summary annotations with references to diseases with links to OMIM. When I queried for “keratin”, in OMIM I got 180 hits, including 61 “clinical synopsises”, in UniProt returned 505 reviewed entries and 2,435 unreviewed entiries, in Entrez Protein 10,611 results and in PubMed 26,430 articles with 1,707 reviews. I got my curiosity about KeraNetics’ research sated by using a PubMed advanced search for Keratin in the abstract or title & the PI’s name as author (search = “(keratin[Title/Abstract]) AND Van Dyke[Author]“).
I ended up with a lot of information leads that I could have hunted through, but it was a fun process in which I learned a lot about keratin. This is where the education stuff comes in. I’ve been seeing a lot of studies go by talking about reforming education to be more investigation driven, and I can totally see how that can work. “Learning” through memorization & regurgitation is dry for everyone & rough for the “memory challenged”, like me. Having a reason or curiosity to explore, with a new nugget of data or understanding lurking around each corner, the information just seems to get in better & stay longer. (OT, but thought I’d mention a related site that I found today w/ some neat stuff: Mind/Shift-How we will learn.)
And I could have done the advanced PubMed search in the beginning, but what fun would that have been? Plus there is a lot that I learned about keratin from what I didn’t find, like that there wasn’t a plethora of PDB structures for keratin proteins. That brings me to the final dot in my mullings – an article that I came across today as I worked on my reading backlog: “Too many roads not taken“. If you have a subscription to Nature you can read it, but the main point is that researchers are still largely focusing on the same set of proteins that they have been for a long time, because these are the proteins for which there are research tools (antibodies, chemical inhibitors, etc). This same sort of philosophy is fueling the Protein Structure Initiative (PSI) efforts, as described here. Anyway, I found the article interesting & agree with the authors general suggestions. I would however extend it beyond these physical research tools & say that going forward researchers need more data analysis tools, and training on how to use them – but I would, wouldn’t I?
Sierpinski P, Garrett J, Ma J, Apel P, Klorig D, Smith T, Koman LA, Atala A, & Van Dyke M (2008). The use of keratin biomaterials derived from human hair for the promotion of rapid regeneration of peripheral nerves. Biomaterials, 29 (1), 118-28 PMID: 17919720
Edwards, A., Isserlin, R., Bader, G., Frye, S., Willson, T., & Yu, F. (2011). Too many roads not taken Nature, 470 (7333), 163-165 DOI: 10.1038/470163a