Tag Archives: gvs

Tip of the Week: Genome Variation Tour III

Today’s tip is the continuation of researching a single SNP in an individual genome. Trey will use a dbSNP RS ID to find linkage disequilibrium information between a SNP of interest and SNPs in the region easily and quickly. GVS, the Genome Variation Server at the University of Washington to analyze a dbSNP rs ID of your choice. This 3 minute screencast will show you how to use the GVS tool to quickly get this information for a wide range of populations.

Personal Genomics, clinical assessment and online resources

ResearchBlogging.orgThe Lancet paper, Clinical assessment incorporating a personal genome, has held my fascination this weekend (yes, I read it at the beach). Mary posted Friday and again Saturday on the paper and related NPR segment. It feels to me to be a seminal paper, though I do agree with Daniel at Genetic Future, there are a lot there we still don’t know. A large portion of the variation is in non-coding regions, and thus predictions and propensities are hard to come by with the available analysis. In fact, as he pointed out, many of the coding region variations have little information as to their effect on disease. I would add also that even if we get to that holy grail of $1,000 to sequence a personal genome, this kind of extensive analysis would still be time and cost-prohibitive for the vast majority of sequenced genomes.

Yet, as with all early steps in science and medicine, there’s missing pieces, large gaps and huge efforts (think “space travel,” “computers,” “microwave ovens,” “internet,”) that over time become inexpensive and commonplace (ok, so the former isn’t necessarily “inexpensive”). Sequencing genomes will become inexpensive before the analysis does, but both will come. And I think this paper is pointing to that future.

The other hurdle to large scale personal genomics I see (of course) is the understanding and use of the genomics and data resources. The authors use a large (and excellent, in my opinion) suite of genomics resources to do obtain data and do their analysis. I’ll list them here with links in alphabetical order:

dbSNP (T)
HapMap (T)
PubMed (T)
UniProt (T)

All of these resources have a wealth of data, but even then, that is a lot of analysis and familiarization that is needed with each tool. Each tool does have documentation and tutorials, and of course OpenHelix has tutorials on many of the ones mentioned (those with linked “T”s after the name). Still, this one analysis took a large number of tools and familiarization.

The paper does have a pretty good figure (figure 1) outlining the analysis process. For example, they SIFTed the genome to find gene-associated, non-synonymous, rare and novel and disease associated variations and then analyzed those using dbSNP, HGMD, OMIM and PubMed to analyze something like HFE2 which might have an association with Haemochromotosis. One of my quibbles with the paper, as often is with these papers, is that there isn’t a good methods ‘walk-through’ of the paper using something like Galaxy or Taverna in a history or workflow that would help reproduce the analysis.

We also have a tutorial I’d like to point you to, one that walks through a similar process and teaches users the basics of walking through that process. You can find this tutorial here, it’s free and publicly available. The tutorial walks the user through the analysis of a gene variation, in this case in the CYPC9 that effects an individual’s response to Warfarin. There is a similar variation (different gene, affects same drug response) in the paper. The tutorial uses the NIEHS SNPs site to get an overview of the variation including SIFT and PolyPhen predictions, then to the UCSC Genome Browser to find an overview of the region, walks through the dbSNP information and does a quick tag SNP analysis using GVS. That tutorial is only one very small step in what will have to be a immense education into genomics and genomics resources.

That is all to point out that the paper is an fascinating first step, and as a first step suggests the gaping holes we will have in bringing personal genomics to medicine.

Ashley, E., Butte, A., Wheeler, M., Chen, R., Klein, T., Dewey, F., Dudley, J., Ormond, K., Pavlovic, A., & Morgan, A. (2010). Clinical assessment incorporating a personal genome The Lancet, 375 (9725), 1525-1535 DOI: 10.1016/S0140-6736(10)60452-7

Clinical assessment with a personal genome. It has happened.

So this morning I was listening to NPR as I usually do while waking up.  And then I had to actually pay attention.  They were doing personal genomics.  But not just theoretical personal genomics.  They were talking with Steve Quake, who has his personal genome in hand. The story is here: Genomes May One Day Be Medical Crystal Balls

And Steve had his sequence analyzed for medically deleterious mutations.  He took this to an MD friend, and together they went over the data.  It’s a pretty interesting story.  And raises a lot of issues.

Some of the mutations are not actionable. That’s clear–and they say that you need to mentally prepared to look at this data, because some of it is scary and you are helpless.

“You know, the chance of dying is 100 percent, it’s just a question of how and when, right?” Quake says. “I think this sort of points to an interesting thing about personal genomes. You have to have a bit of a strong stomach for it.”

Are people really going to be able to take this not only for themselves, but for their 3 kids?  I can’t imagine that kind of weight multiple times.

Other information may be helpful–seems like Quake will benefit from statins.

But the part I found most intriguing was their assessment of the doctor-patient interaction around the personal genome.  They talk about how if you have 100-120 genes of concern that arise in your sequence, and you want to spend 2-3 minutes talking about each (which seems pretty minimal for non-scientists/non-MDs, I think), that would be 5 hours of genetic counselling.  And who will do that?  And who will pay for that?  And is the public ready for the bad news?

Anyway–this is tied to a paper in The Lancet.  Figure 1 is very cool–it describes the workflow/steps they went through for looking at the mutations.  It included GVS, HGMD, OMIM, PubMed, PolyPhen, SIFT, and more. It’s interesting–I had talked recently about the steps I would take if I had my genome, and I think this is a very nice model for that. (EDIT: by the way, we have tutorials on a number of the tools in Figure 1…)

They talk about PharmGKB. They show Steve’s pedigree, and his clinical assessment. You can look at his mutations. And Figure 3 is his clinical risks of things like Obesity, diabetes, Depression, various cancers, and more.

It’s an important paper in this arena. If you are looking at a personal genome in your future, check it out.  And think about the implications.  I think the conclusions way undersell the look at the future that this provides–but it’s a research paper, I get it.  But this is fascinating to think about.

Ashley, E., Butte, A., Wheeler, M., Chen, R., Klein, T., Dewey, F., Dudley, J., Ormond, K., Pavlovic, A., & Morgan, A. (2010). Clinical assessment incorporating a personal genome The Lancet, 375 (9725), 1525-1535 DOI: 10.1016/S0140-6736(10)60452-7

Tip of the Week: F-SNP

fsnp_thumbThere are a lot of databases to search for to find SNP data, HapMap, dbSNP, SeattleSNPs, Genome Variation Server and many more. I’m going to add one more to your data mining arsenal, F-SNP. F-SNP (described more fully here in the 2008 NAR Database issue),

provides integrated information about the functional effects of SNPs obtained from 16 bioinformatics tools and databases. The functional effects are predicted and indicated at the splicing, transcriptional, translational, and post-translational level. As such, the F-SNP database helps identify and focus on SNPs with potential pathological effect to human health.

…as they say in the introduction. It looks to be a good first stop to find SNPs of functional relevance. The databases they pull from to get their information include several I’ve mentioned above and also the UCSC Genome database, Ensembl, SIFT and PolyPhen predictions and more. I’ve given a quick intro in the tip this week on how to get functional SNP information from F-SNP.

Tip of the Week: GVS

gvs intro tip This is another tutorial at SciVee (click on image to go to SciVee and watch movie), but this time it’s one of mine I did earlier. I’m on vacation (in Germany, were we used to live in Heidelberg while I worked at EMBL) so I’m pointing you to this short intro I did earlier so I can get back to my weiss wurst). Of course, I’ve also done a tip on one specific aspect of GVS before that you might want to check out. This is a general introduction to the Genome Variation Server at the University of Washington. Additionally, you can get more freely available training materials on GVS including a longer introductory tutorial (40 minutes), slides, handouts and exercises at OpenHelix GVS Tutorial or visit the resource at GVS.

Tip of the Week: Free bioinformatics training materials

We wanted to take this “tip of the week” to introduce you to some of the materials that we have which are freely available for you to download and use in classes, seminars, or just for your own learning. OpenHelix creates training materials that include tutorial movies (animated + audio), slides with script, and exercises to reinforce concepts developed in the tutorials. Some of them are sponsored by the software provider, so we can make them freely available. We can even send you these great Quick Reference Cards that you can give out to students, or tape next to your computer, which will remind you of many of the features of the site. You can access them from our blog, or from our regular homepage. This tip of the week movie introduces you to how to access these materials.

A taste of OpenHelix

The bloggers here at OpenHelix and some of our family and friends decided to do the taste tests. You know the ones. You probably did them in your genetics class. I used them in my introductory biology class at CCSF years ago and had hundreds of the test strips left. So, we thought we’d distribute them to the bloggers and families here and see what the results were. The test strips are for sodium benzoate, PTC and thiourea. There is also a control strip of no taste (but paper). I numbered the strips and sent them to the bloggers and families (so they wouldn’t know what they were tasting, control or otherwise). And here are the results (and some database links to more about the genetics of taste):

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Sponsored Genomics Resource Tutorials, available again

Our server went down last week and the host provider had to move our server. The settings weren’t completely corrected, so if you have attempted to view the free tutorials and training materials this week, you might have had problems doing so.

We have fixed that problem and those links now work. Please visit our tutorials!

As a reminder, here are the tutorials that are sponsored by providers and free to the user:

sponsored by the National Institute of Environmental Health Sciences (NIEHS)

Genome Variation Server (GVS)
sponsored by the National Heart, Lung, and Blood Institute (NHLBI) and the University of Washington

Integrated Microbial Genomes (IMG)
sponsored by the Joint Genome Institute

sponsored by the National Heart, Lung, and Blood Institute (NHLBI) and the University of Washington

UCSC Genome Browser (Intro and Advanced Topics)
sponsored by the University of California Santa Cruz Bioinformatics Group

VISTA Comparative Genomics Tools
sponsored by Lawrence Berkeley National Laboratory

Tip of the Week: Visualizing Genotypes

SeattleSNPs genotypeAnd maybe it will help you visualize peace too :). There are several sites and software programs (Haploview and GVS are two) that will help you do visual genotype, we are going to show you one here that is simple to use at SeattleSNPs. This tip will show you how to access SeattleSNPs VG2 software and quickly visualize SNP allele genotype data for a gene, either your own data or that from the SeattleSNPs project.