Tag Archives: OMIM

(one) Video Tip of the Week (to hold them all): Variation and Disease Databases

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.

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 The publicly accessible site is out-of-date by three years.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 :D 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.

Video Tip of the Week: New Genetic Testing Registry (GTR) Resource


Late last month the National Center for Biotechnology Information, or NCBI, released a new resource containing information on genetic tests. The resource’s name is the Genetic Testing Registry (GTR), and according to its homepage, the GTR:

” 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

Video Tip of the Week: OMIA, Online Mendelian Inheritance in Animals

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

Video Tips of the Week: Annual Review IV (first half of 2011)

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.

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

On a Mission for Protein Information

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? :)

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

What’s the Answer? disease causing SNPs

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 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.

Video Tip of the Week: VnD Resource for Genetic Variation and Drug Information


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

New OMIM displays at UCSC Genome Browser

For those of you who may have missed the @GenomeBrowser tweets and don’t subscribe to the genome-announce mailing list, here’s my PSA about the new features of the OMIM displays in the UCSC Genome Browser, snipped from the full announcement. You can see the whole email here. The re-engineered data subsets are great. And also note that they are linking to both the NCBI OMIM (which is currently not being updated) but also to the new official OMIM site from the UCSC pages.  For more info on the new OMIM site, check out our recent Tip of the Week on it.

Hello, Browser friends,

We announce today the release of our newly re-engineered OMIM (Online Mendelian Inheritance in Man) tracks for both hg18 and hg19. With the kind assistance of Ada Hamosh (director), Joanna Amberger and Francois Schiettecatte of the OMIM project, we have divided the OMIM records into three separate tracks:

OMIM Allelic Variant SNPs

  • Variants in the OMIM database that have associated dbSNP identifiers.

OMIM Genes

  • The genomic positions of gene entries in the OMIM database.
  • The coloring indicates the associated OMIM phenotype class.

OMIM Phenotypes – Gene Unknown

  • Regions known to be associated with a phenotype, but for which no specific gene is known to be causative. This track also includes known multi-gene syndromes.

Anyway, check it out. The tweet has a handy link to a setting page:

@GenomeBrowser: See the the newly re-engineered OMIM (Online Mendelian Inheritance in Man) tracks for both hg18 and hg19: http://t.co/Szh2h4L

Quick links:

UCSC Genome Browser

New OMIM

NCBI OMIM

Tip of the Week: New and Improved OMIM®

In the realm of bioinformatics resources, few are more venerable than OMIM®, Online Mendelian Inheritance in Man [well, originally not online, on index cards...]. For those who might be new to OMIM, it is a catalog of genes and their variations, and resulting phenotypes in human, with a more clinical perspective than some resources offer. As I was reviewing the history of OMIM for this post, I began to wonder if there even is any repository in genomics that’s been maintained on a computer framework longer. I know of an older protein analysis program that I wrote about once here–from Margaret Dayhoff and Robert Ledley. But as an ongoing repository or catalog that was stored, Victor McKusick wrote:

Mendelian Inheritance in Man has been maintained on the computer since 1964.

It was stored on a mainframe at Johns Hopkins at that time. The other one that I thought was probably close was RCSB PDB, which is described on their “about” page in this way:

The PDB was established in 1971 at Brookhaven National Laboratory and originally contained 7 structures.

It’s likely that in some form it existed on a computer system earlier than that–and may give MIM a run for the record. Bruno Strasser described 4 resources developed around the same time–1965–as the Cambridge Structural Database, MIM, Index Medicus, and Atlas of Protein Sequence and Structure.

It’s not easy to maintain and develop a resource for this long. Just this past month we learned about the risk of KEGG going away. But in bioinformatics–like biology–a resource needs to evolve or die. (Actually, I can remember in grad school that phrase was used by the chair of our department to describe what biology faculty needs to do as well.) In this week’s tip of the week, I report that OMIM is evolving, and I introduce you to the new interface.

Most people have encountered OMIM at the NCBI. But if you go over to there today, you’ll see this notice on the homepage:

This is because OMIM has a new home. It’s not clear at this time if the NCBI incarnation will be updated going forward. The OMIM team at JHU is requesting that software providers who serve links to OMIM now migrate those links to the new OMIM.org site, which the OMIM team considers to be the official site and will be the up-to-date one.

Let’s talk specifically about the evolved OMIM now: it’s entered a new century! Yay! The incredible deep collection of curated data over the decades still remains, but the new interface is very nice to use and to look at–it no longer looks like 1995 over there. There are also new handy links, and new search options, and new features still to come.

Compare this same record for the APC gene (a contributor to hereditary colon cancer) in both places:

Old OMIM at NCBI: http://www.ncbi.nlm.nih.gov/omim/611731

New OMIM at JHU: http://www.omim.org/entry/611731

I find the new page to be significantly tidier, don’t you? The links you need to other resources are still there, but you can toggle open the menus to find them now. And some of the links are to resources that weren’t available on the old page (for example, BioGPS and PharmGKB which we like very much!).  I’m also told that on appropriate pages there will be links to the DECIPHER resource.

You can still browse around the MIM map by clicking on the Advanced search for Gene Map link: http://www.omim.org/search/advanced/geneMap . I have done this on many days when I have found something intriguing in a chromosomal region and I want to see what was reported in that area and stored at OMIM.

Another feature that I think is very cool is the option to change the language with the Google Translate menu. I know it’s not perfect, but I’m finding increasingly that I want to read blog posts in other languages and I am finding it works pretty well. Making the OMIM data so easily accessible to non-English speakers is a really nice touch.

Although sometimes it is tough to transition to new software, I think this is a good sign. In addition to maintaining the excellent knowledge collection that began so long ago, a new interface means that OMIM is continuing to grow and change to meet the needs of today. And as we move forward to identify more and more genomic variations and alterations that impact human health, well-curated and deep knowledge bases like this are crucial.

Congrats to the OMIM team on the new look and new home.

Quick link to the new OMIM: http://www.omim.org/

(Bonus: did you know there’s also an Online Mendelian Inheritance in Animals OMIA? http://www.ncbi.nlm.nih.gov/omia )

Follow them on Twitter: http://twitter.com/#!/OmimOrg

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

Strasser, B. (2009). Collecting, Comparing, and Computing Sequences: The Making of Margaret O. Dayhoff’s Atlas of Protein Sequence and Structure, 1954–1965 Journal of the History of Biology, 43 (4), 623-660 DOI: 10.1007/s10739-009-9221-0 (PDF available on his faculty web site here.)

Strasser, BJ (2006) “Collecting and Experimenting: The moral economies of biological research, 1960s-1980s.”, Preprints of the Max-Planck Institute for the History of Science, 310, 105-23.

Tip of the Week: Genome Variation Tour II

The last tip of the week I did was Genome Variation Tour I where we started our journey following one SNP in an individual’s genome through various databases to see what we can find out about that variation. In that tip we started out by looking at a SNP in the CYP4F2 gene in the UCSC Genome Browser and followed it to dbSNP. Today’s tip will continue our journey to OMIM to see what information we can find there. We’ll find this variation is clinically associated with Warfarin dosage effects and specifically this individual’s C/T heterozygosity indicates an intermediate dosage for effectiveness if indeed he ever needed this drug.  In some ways, your guess is as good as mine as to what we will find and what avenues we will be taking in the next few tips I’ll be doing. I’m am discovering information as I go along too. I can tell you though that the next installment of the genome variation tour will take us to PubMed, and a few not particularly well known but gem databases perhaps and probably back to the UCSC Genome Browser to expand our look at the interactions of several variations in this individuals genome.