The OpenHelix Blog

This next post in our continuing semi-regular Guest Post series is from Andrew Johnson, one of the developers and the concept designer of SNAP, SNP Annotation and Proxy Search which is hosted at the Broad Institute. If you are a provider of a free, publicly available genomics tool, database or resource and would like to convey something to users on our guest post feature, please feel free to contact us at wlathe AT openhelix DOT com or the contact form (write ‘guest post’ as subject heading). We welcome introductions to your resource, information on updates, highlights of little known gems or opinion pieces on the state of genomic research and databases.

SNAP (http://www.broadinstitute.org/mpg/snap/, Johnson et al. (2008) Bioinformatics 24(24): 2938), “SNP Annotation and Proxy search”, is a flexible, web-based tool that allows anyone in the world to quickly accomplish a range of SNP-related genetics and bioinformatics tasks. This post highlights some common questions andfeatures of SNAP, some more obscure uses, and recent and planned developments.

How did SNAP come about?

The idea for SNAP was originally sparked by GWAS analysts within a large collaborative group (the Framingham Heart Study SHARe project). This was in the pre-imputation era when GWAS investigators from different groups using different SNP arrays often wanted to find best proxy SNPs based on HapMap for comparison when they didn’t have common genotyped SNPs across groups. We initially implemented local programs to lookup upHapMap LD and also consider the presence of query and proxy SNPs on different commercial genotyping arrays. We quickly realized this was a community-wide problem as we received requests from outside collaborators so we decided it was worth developing a public tool and approached investigators at the Broad Institute. Through collaboration with Paul de Bakker, Bob Handsaker and others at the Broad Institute we were able to add more features like plotting and build a nice, quick and accessible interface. Many people have contributed ideas, testingand improvements to SNAP, and Bob Handsaker and Pei Lin in particular continue to maintain and update SNAP.

What do you use SNAP for the most?

The two major features of SNAP widely used 1) SNP LD queries, and 2) plotting of LD and association data. There are a number of flexible options for these functions. Beyond these, as a SNP bioinformatics specialist, I often use SNAP to rapidly retrieve information about a list of SNPs for other uses (see specialized queries below).

What are some commonly asked questions from users of SNAP?

HapMap has had a few minor updates to their browser, and importantly, new phase 3 data was released early last year (drafts of that data were released in 2008). Haploview, the downloaded software that allows the user to perform in depth LD and haplotype analysis, has been recently updated from version 4.1 to version 4.2. Haploview can be used with user data or data downloaded from the HapMap project. Though, version 4.1 did not work for phase III HapMap project data, so the user had to use phase I and II data if they wanted to use version 4.1. Haploview has now been updated to version 4.2, allowing the user to use HapMap phase III data.

That’s a lot of versions and phases . The short of it is, if you use Haploview 4.2, you can view and analyze data from any phase of the HapMap project.

Today’s tip briefly shows you how to download data from the HapMap project and view it in Haploview.

Interesting post from SNPedia blog (we mentioned being able to view SNPedia SNPS HapMap last year in a post) of the top 10 SNPs of the year.

Of course, as they mention, it’s very subjective.

Because they have chosen SNPs with serious health interest, I’ll semi-frivolously (because hey, no knowledge is necessarily “frivolous” nominate either:

The “ear wax” SNP which determines whether you have ‘wet or dry’ earwax, only because (yes, TMI) I have both, one in each ear so now I’m curious as to why.

and

The “Perfect Musical Pitch” SNP, only because my daughter and I seem to have that particular variation, and we know a few people who don’t .

This is from the HapMap team for the human data (not the green HapMap I referenced recently). Older data is going to be removed from the HapMap.org browser and from BioMart. It will still be available in the ftp archive–but I just thought a heads-up was in order for folks who might not be on the mailing list:

Beginning July 1st, HapMap data releases prior to October 2008 (or release #24) will no longer be available via the HapMap Genome Browser and HapMart utility.

The following three data releases will continue to be served via the HapMap Genome Browser:

* HapMap Release #24 — Latest HapMap (phase I+II) data release
http://www.hapmap.org/cgi-perl/gbrowse/hapmap24_B36/

* HapMap3 Draft #2 (hapmap3_r2) — Latest HapMap3 (phase III) data release
http://www.hapmap.org/cgi-perl/gbrowse/hapmap3r2_B36/

* HapMap Release #27 — Latest merged HapMap (phase I+II+III) data release
http://www.hapmap.org/cgi-perl/gbrowse/hapmap27_B36/

The following data release will continue to be served via HapMart:
* HapMap Release #27 — Latest merged HapMap (phase I+II+III) data release
http://hapmart.hapmap.org/BioMart/martview

All other data releases will still be available for FTP download:
http://www.hapmap.org/downloads/index.html.en#bulk

If you aren’t on the mailing list you can check it out and sign up here: http://osceola.cshl.edu/mailman/listinfo/announcements

There 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.

There is plenty of buzz out there for the big data biology projects–but usually the focus is the human data (with a few token model organisms thrown in).  But this week plant researchers renewed the call for big plant data.  I’m totally on board with that.

The 1000 Genomes project to obtain more human variation information is well underway, funded, and has companies supporting it.  And that’s great–I’m all for this too!  But as someone who survives largely on the kindness of plants I want more plant research going on.  I want to see this funded and supported.  And as we face increasing stresses on resources from limitations like oil and water supplies to wacky climate conditions and environmental consequences I think we could well afford to spend less time gazing at our human genomic navels and devote more attention to the plants.

There is already some work on this Arabidopsis project.  The first paper with data on this effort came out last fall.  But the researchers are still having to go out and lobby for this project.  A new opinion piece in Genome Biology calls out for awareness and support for this effort.

They have already done a first generation green HapMap.  The paper last fall illustrated the feasibility of the project by looking at the reference Col-O (Columbia) and Bur-O and Tsu-1 strains.  The paper presents the process, compares their pipeline software with another package (SHORE that they developed and MAQ), They have a GBrowse installation that presents the data  (and you can get free training on GBrowse here to effectively use the site).  They also provide data to TAIR.

I think this is important and I hope it gets the same level of support and respect that 1000 humans will get.

1001 Genomes main site: http://1001genomes.org/

1001 Genomes GBrowse: http://gbrowse.weigelworld.org/cgi-bin/gbrowse/ath_reseq_1001/

References:
Clark, R., Schweikert, G., Toomajian, C., Ossowski, S., Zeller, G., Shinn, P., Warthmann, N., Hu, T., Fu, G., Hinds, D., Chen, H., Frazer, K., Huson, D., Scholkopf, B., Nordborg, M., Ratsch, G., Ecker, J., & Weigel, D. (2007). Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana Science, 317 (5836), 338-342 DOI: 10.1126/science.1138632

Ossowski, S., Schneeberger, K., Clark, R., Lanz, C., Warthmann, N., & Weigel, D. (2008). Sequencing of natural strains of Arabidopsis thaliana with short reads Genome Research, 18 (12), 2024-2033 DOI: 10.1101/gr.080200.108

Weigel, D., & Mott, R. (2009). The 1001 Genomes Project for Arabidopsis thaliana Genome Biology, 10 (5) DOI: 10.1186/gb-2009-10-5-107

We are seeing a lot of interest in visualizing GWAS data lately.  We cover this a bit in our UCSC Genome Browser tutorial.  And we recently did a pretty popular post on a quick look at the NHGRI GWAS catalog data using the UCSC Genome Graphs tool.

But as I was looking at the HapMap tools again recently, I noticed that they have a tool for this as well.  So today’s tip examines that tool for visualizing the NHGRI GWAS catalog data, and having a look at the GBrowse view of this data in genomic regions with the HapMap context.  In this movie I load up one of the sample data sets and move from that GWAS karyogram visualization to the HapMap GBrowse view.  Click the image to view the movie.

I was reading a newsletter I get from Biotechniques, and their WebWatch often has some fun items. (You may need to get a free login to see the WebWatch.) This week they referred to the MaizeGDB database in the post Amaizing Base. Although I had been aware of MaizeGDB before, it was a nice reminder to go over and have a look to see what’s new.

When I went over there I was intrigued by the new browser they are about to launch (in mid-October). The link says “coming soon” and I went to check out the information there.

Currently that link goes to a page that describes their move to a more sequence-centric representation of their data. It was a fascinating look at their decision process to move to a new browser platform and what they decided to do. For database geeks like me, seeing their ranking of the importance of various features was very compelling.

And what they decided? GBrowse!

We have a tutorial available on GBrowse. Usually we do tutorials on specific sites, but as we kept seeing GBrowse over and over at different sites we created a tutorial for that. It helps me to understand the underlying basic browser when I visit any site that employs it. Even though the wrappings and the data types will vary at different sites, understanding how it works makes it much easier to use at any new site that uses it. HapMap, MGI, WormBase, FlyBase, TAIR, Watson’s personal genome, and a whole bunch of other sites use the GBrowse software.

Looking forward to checking out the MaizeGDB GBrowse version when it launches!

Looking at the NHGRI News feed recently, I noticed this story (below) about a new genomic data collection that intrigued me. I found out about a new resource that I wanted to share as this week’s Tip of the Week. So this ~4 minute movie discusses my path to the Human Genome Structural Variation resource and a quick look at some of the data. But the paper was so influential on my thinking about the genome that I wanted to cover that in more detail in text form as well. So for a quick hit, watch the movie. For more detail, check out the text and links below.  Quick trip to the database: http://hgsv.washington.edu

Researchers Produce First Sequence Map of Large-Scale Structural Variation in Human Genome

….Other recently created maps, such as the HapMap, have catalogued the patterns of small-scale variations in the genome that involve single DNA letters, or bases. However, the scientific community has been eagerly awaiting the creation of additional types of maps in light of findings that larger scale differences account for a great deal of the common genetic variation among individuals and between populations, and may account for a significant fraction of disease. While previous work has identified structural variation in the human genome, a sequence-based map provides much finer resolution and location information….

I spend a lot of time thinking about the official or “reference” human genome sequence. This sequence–the one that was released to all that fanfare a few years back–is a composite of several people. Rather like a “generic” genome.

We are starting a little bit of genetic genealogy in our household. I’ve always have been an avid genealogist and with an adopted child we’ve been found it interesting and helpful to delve a bit deeper into our heritages in a way we couldn’t have 10 years ago. So, I’m a bit aware lately of studies of historical and genetic links to our backgrounds…

A study in this month’s American Journal of Human Genetics suggests that the history of the spread of Islam and the crusades can be found in the genomes of the male population in Lebanon. The study found that…

926 Lebanese men were typed with Y-chromosomal SNP and STR markers, and unusually, male genetic variation within Lebanon was found to be more strongly structured by religious affiliation than by geography.

Their hypothesis was that migrations within historical times contributed to difference. The data was from the Genographic Public Participation Project (National Geographic). Their conclusions?