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.

Have a fresh look at OMIM today, and you can also download slides to use and exercises to perform to help you perform more complex searches and exploration of the wealth of data they have.

The training materials are freely available because they are sponsored by the OMIM team, who worked with us to create them. They are free for a limited time, so check them out soon.

Quick links:

OMIM tutorial: http://openhelix.com/OMIM

OMIM main site: http://www.omim.org (but note there are mirror sites in the US and Europe that may be good options)

References:

McKusick, V. (2006). A 60-Year Tale of Spots, Maps, and Genes Annual Review of Genomics and Human Genetics, 7 (1), 1-27 DOI: 10.1146/annurev.genom.7.080505.115749

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

Related Tutorials:OMIM, UCSC Genome Browser: An Introduction

• RT @moorejh: #bioinformatics MT @brainpicker TreeVersity – interactive #visualization tool lets you compare tree diagrams http://t.co/09PMu6Oo [Mary]
• RT @tferriss: HUGE – “World’s largest release of human cancer genome data”: http://t.co/tnSbqE7d Partially thanks to all 4HB readers! http://t.co/dMxHiWoX [Mary]
• I’m gonna hafta watch & learn about “poo tea” RT @bykriscampbell Ooh, this @tedmed talk by @phylogenomics gets me so excited! An intro to “the microbes that live in and on us”. http://ow.ly/bhfQ0 [Jennifer]
• RT @emblebies: A new mirror for OMIM.org is now running from the EMBL-EBI for European users. Please visit europe.omim.org [Mary]
• Think I’d like to attend: RT @WorldSciFest: RT @TomLevenson: Four out of seven total Kavli laureates this year are women. Change comes; slowly, but it comes. #wsf12 [Jennifer]
• Heh–this got a bunch of re-tweets; I suspect it touched a nerve. RT @szintri: Common problem these days RT @brent_p people keep coming to me with data and without questions. #bioinformatics [Mary]
• Ha ha ha: RT @milklovesu: Now I understand why biologist are such bioinformatics haters, cause comp sci people write like this. What y… [pic] — http://t.co/aHrooVYy [Mary]

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:

Question: Is OMIM no longer available as structured data?

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:

OMIM API is now open, see omim.org/api and omim.org/help/api #OmimOrg

— omim.org (@OmimOrg) April 30, 2012

I’ve you’ve been waiting for the OMIM API access, check it out at the Help page.

The databases mentioned are OMIM, Human Gene Mutation Database (HGMD), MutaDATABASE and The Human Variome Project . There are video tips for the first three.

OMIM.

Last year OMIM moved to http://www.omim.org and had a entire new interface. Mary was on top of it and did a tip on the new OMIM interface with lots of information on the move and OMIM in the post:

Our full tutorial on the new OMIM is coming soon.

HGMD:
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:

Additionally, there is a good background page for more information.

MutaDATABASE:

Mary did a tip on MutaDatabase last summer:

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.

There are several other resources available for human disease variation including CGAP, dbGAP, GAD, PhenomicDB and several others. We have tutorials on all those if you wish to check those out.

Of course there’s dbSNP of which we have a tutorial and tip about searching human variation.

You can find an extensive list of other resources at Human Genome Variation Society (HGVS).

And an oft-asked question on Biostar is what kind of resources are there for this kind of data. You can find answers here, here and here.

” 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. 

Quick Links:

The Genetic Testing Registry (GTR) – http://www.ncbi.nlm.nih.gov/gtr/

GTR YouTube Tips from NCBI – http://www.youtube.com/playlist?list=PL1C4A2AFF811F6F0B

GeneTests – http://www.ncbi.nlm.nih.gov/sites/GeneTests/?db=GeneTests

GeneTests Introductory Tutorial by OpenHelix* – http://bit.ly/genetests

Genetic Home Reference (GHR) – http://ghr.nlm.nih.gov/

GHR Introductory Tutorial by OpenHelix* – http://bit.ly/geneticshomeref

Online Mendelian Inheritance in Man (OMIM) – http://www.omim.org/

OMIM Introductory Tutorial by OpenHelix – (coming soon, currently being updated)

Orphanet – http://www.orpha.net/

form.

*OpenHelix tutorials for these resources available for individual purchase or through a subscription

Available References:

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.

Quick links:

OMIA (Univ. of Sydney): http://omia.angis.org.au/

OMIA at NCBI: http://www.ncbi.nlm.nih.gov/omia

OMIM: http://www.omim.org/

References:

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

You can see past years’ tips here: 2008 I, 2008 II, 2009 I, 2009 II, 2010 I, 2010 II. The summary of the second half of 2011 will be available next week here.

January 2011

January 5: SKIPPY predicting variants w/ splicing effects

January 12: Twitter in Bioinformatics. This one was much more popular than I expected!

January 19: PolyPhen, for predicting the possible effects of mutations in genes

January 26: iRefWeb + protein interaction curation

February 2011

February 2: RCSB PDB Data Distribution Summaries

February 9: SIFT, Sorting (SNPs) Intolerant From Tolerant another tool for predicting the impact of mutations in genes.

February 16: Melina II for promoter analysis

February 23: SNPTips and viewing personal genome data This tip is one of the most-watched ones we’ve had. Thousands of views on SciVee!

March 2011

March 2: DAnCER for disease-annotated epigenetics data

March 9: World Tour of Genomics Resources

March 16: Encyclopedia of Life

March 23: ORegAnno for regulatory annotation

March 30: MetaPhoOrs, orthology and paralogy predictions

April 2011

April 6: The Taverna Project for workflows

April 13: VirusMINT , the branch of the Molecular Interaction database for viral interactions

April 20: LAMHDI for animal models

April 27: Dot Plots, Synteny at VISTA

May 2011

May 4: MycoCosm

May 11: InterMine for mining “big data”

May 18: Allen Institute’s Brain Explorer

May 25: SciVee, the YouTube of science

June 2011

June 1: New and Improved OMIM®

June 8: Converting Genome Coordinates

June 15: MutaDATABASE, a centralized and standardized DNA variation database

June 22: Update to NCBI’s Cn3D Viewer

June 29: Orphanet for Rare Disease information

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?

References:

• 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

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 week’s highlighted question is….

..which is the best database choice from where i can extract a data set of causative variants and a data set of benign variants (OMIM ,GWAS)…

A perennially favorite question. The accepted answer gives a good rundown of how to go about choosing a database. Another answer points to an earlier discussion with a wealth of databases.

In today’s tip I am going to feature a resource that I found recently. I’ve been updating our dbSNP tutorial, which Mary & Trey will be presenting at workshops in Morocco, and also our free PDB tutorial, which is sponsored by the RCSB PDB team. I have therefore been thinking about protein structures and small sequence variations a lot lately. As I explored the latest Database issue of NAR looking for resources to do a tip on, I found an article describing the VnD (genetic Variation and Drug) resource, which can also be accessed at the URL www.vandd.org, according to the NAR article. The article is “VnD: a structure-centric database of disease-related SNPs and drugs“, and figure one shows a veritable Who’s Who of protein, variation and disease resources, so I had to investigate.

What I found at VnD made me sure that this was a resource that I wanted to feature in a tip. VnD is from the Korean Bioinformation Center, or KOBIC, who has a list of databases and tools that they provide. I’ll save the rest of the KOBIC resources for another post & concentrate on VnD here. Compiling data from resources such as RefSeq, OMIM, UniProt, PDB, DrugBank, dbSNP, GAD and more might have been cool enough, depending on how it was done, but the VnD also does their own structure modeling analysis on how the variation affects the protein structure and drug/ligand binding.

This tip movie isn’t long enough to really show you the breadth of what is available from the VnD, but I hope it will be enough to encourage you to read the NAR article (listed below), and to check out VnD. One thing to note: don’t expect to find every dbSNP rs# over there – one that I’ve been using in our tutorial isn’t over there. They are specifically interested in variations within genes that might effect drug binding. But hey, you can’t query DrugBank with rs#s, and I’ve never seen the structure modeling done like VnD, so it is a worthy resource that you may want to investigate if you are interested in how genetic variations connect with disease and drug therapies.

Quick links:

VnD: Variations and Drugs resource -  http://vnd.kobic.re.kr:8080/VnD/index.jsp

Korean Bioinformation Center (KOBIC) – http://www.kobic.re.kr/

RCSB PDB – http://www.pdb.org

OpenHelix Tutorial on the RCSB PDB – http://www.openhelix.com/pdb

dbSNP: Short Genetic Variations, from NCBI -  http://www.ncbi.nlm.nih.gov/projects/SNP/

OpenHelix Tutorial on NCBI’s dbSNP – http://www.openhelix.com/cgi/tutorialInfo.cgi?id=39

For links to other resources and OpenHelix tutorials mentioned in this post, please see our catalog of resources – http://www.openhelix.com/cgi/tutorials.cgi

Reference:
Yang, J., Oh, S., Ko, G., Park, S., Kim, W., Lee, B., & Lee, S. (2010). VnD: a structure-centric database of disease-related SNPs and drugs Nucleic Acids Research, 39 (Database) DOI: 10.1093/nar/gkq957