• METAREP: JCVI Metagenomics Reports – an open source tool for high-performance comparative metagenomics. Hat tip to @kozo2 [Mary]
• PLoS now has a blogging network. Science blogging continues to diversify [Trey]
• USGS has a genomics site? I thought they just did earthquakes. From their page they refer to the sequencing of the fungus that’s affecting bat populations, and their team is involved with that. Neat. Plus lots of other interesting genetics of wildlife issues. And a microbiology component. Wowsa. H/T to @lakeganharris [Mary]
• Crowd-sourcing a genome project: to raise funds for sequencing the whale shark genome, you can buy hair bands. I’m not sure I get the linkage–but if I still had long hair I’d probably do it… Whale shark genome schwag has been unbridled! [Mary]
• Speaking of crowds (crowds of variations?.. something ), HapMap 3 paper just came out and Razib Khan has an excellent blog post about it. [Trey]
• Organizations that you might consider joining, or at least following: Scientists without Borders, UNESCO, and WAY: World Association of Young Scientists – Science, Remixed. [Jennifer]
• I might have to read the full article, based on this summary: “Sweet fruit from a poisonous kiss” by Delong Liu Full paper by Zhang et al at Science. 2010 Apr 9;328(5975):240-3. [Jennifer]
• ensembl wants your feedback. Check out their survey: http://bit.ly/cTSXyu [Mary]
• Check it out – the series of ScienceOnline interviews, or my specific interview – you decide which to read… [Jennifer]
• more than 2 weeks of science fun throughout North Carolina and including the MythBusters guys: NC Science Festival 2010 – Life is Your Lab! [Jennifer]

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?

Many of the common questions are covered in detail in the FAQ and/or Documentation available on the website. Here are some questions I commonly receive.

How do I return all LD proxy relationships within 500kb of my query SNPs? Change the r^2 threshold to “No Limit” and leave the distance limit at “500”.

Why doesn’t my favorite SNP appear when I make a query? Occasionally query and/or expected proxy SNPs will not be found. This could be a result of an error in your representation of the query SNPid. The most likely explanation for proxy SNP is that the filters you’ve selected (r^2 and distance limits) have caused a SNP not to be included. Another likely explanation is that the expected SNP is >500kb apart from query SNPs (see below). Rarely SNAP may return an alias SNPid for a proxy rather than the one you expect since SNAP takes aliases into account in queries (see below).  Finally, a SNP(s) may not be included in the HapMap release you are querying.HapMap releases 21 and 22 differ among a small number of SNPs. HapMap release 3 differs from prior releases at a greater number of SNPs. If it is important you should try querying different HapMap releases to find a release(s) with your SNP(s) of interest. Alternatively, you can also try to find genotype data separately and load into a program such as Haploview to calculate LD metrics.

Can I generate plots based on my own data? Yes. You can upload both your own association data (-log P) and your own LD data, as you may have generated LD estimates within your own population and/or a larger sample than available in the HapMap. If you don’t specify your own LD data SNAP uses HapMap by default. If you don’t provide chromosome and position SNAP fills these in based on HapMap. If you have de novo markers that are not in HapMap you can also include these as long as you specify the chromosome and positions and LD to the target SNP.

Why do I observe different LD estimates for the same pair of SNPs in different HapMap releases? Identical SNP pairs generally have identical, or very similar, LD estimates in different releases of HapMap. If LD estimates differ slightly it is attributable to differences in genotypes in the releases.

What if I just want to query LD among a select group of SNPs? Click the “Pairwise LD” tab. Copy and paste yourSNPid list, or upload a file. Your LD queries will be limited to only your SNPs of interest rather than all HapMapSNPs that meet the filtering criteria.

What if I want to find SNPs genotyped only on a specific array or group of arrays? There is a rapid way to limit queries to specific arrays. Click the ‘+’ on the Filter By Array. Select those arrays you want.

What do if I want to calculate long range LD or trans-chromosomal LD? SNAP returns results for pairwise LD between SNPs with distance up to 500kb. This is greater than the default of HapMap pre-calculated data of 250kb. In some cases users may want to assess SNPs that are further apart. A few options exist including 1) downloading the HapMap genotypes and loading into Haploview while removing the pairwise distance limitation, 2) calculating using PLINK, or 3) querying with the GLIDERS website (http://mather.well.ox.ac.uk/GLIDERS/). GLIDERS returns extreme long range queries on chromosomes or trans-chromosomal queries.

What are some specialized queries I can conduct with SNAP?

Find annotation for SNPs regardless of LD proxies. SNAP doesn’t have to be used as an LD querying tool. You can simply retrieve information about a list of SNPs. To do so load your SNPids. Under “Search Options” select Distance Limit as 0 instead of the default 500kb. With the default settings SNAP will now only return information for your query SNPs themselves. You can select additional options like GeneCruiser annotations and MAFs. This is an excellent way to rapidly answer questions like: would a SNP(s) be genotyped on my array(s) of interest?Which of my SNPs are nonsynonymous SNPs? What are the genomic coordinates for my list of SNPs in a specific genome build (just select the corresponding HapMap build – Release 21=hg17, Release 22/HapMap 3=hg18)?What are the HapMap MAFs for my list of SNPs? Of course, you can also turn on proxy querying and ask these same questions in relation to both query and/or proxy SNPs. For instance, of my significant GWAS SNPs are any of them in LD with r^2 > 0.5 with a known nonsynonymous SNP?

Find alias or alternate SNPids for my SNPs of importance. Some people do not realize that SNPs can suffer from a historical aliasing problem just like gene names. If you are using SNPids to query bioinformatics tools or databases, or to conduct cross dataset queries, to be extra cautious you should rely on genome positions and allelesor account for potential alias IDs. SNAP allows querying to return alias SNPids. Click the tab “Map SNP IDs“. You can retrieve IDs for a SNP across all previous dbSNP builds or specify a specific build to target.

What are recently added features of planned future updates to SNAP?

SNAP is in version 2.1. Recent updates have included the addition of 1) HapMap 3 release featuring 12 population groupings, 2) SNP information for 7 new commercial SNP arrays (25 arrays now listed), 3) and the ability to include HapMap major and minor alleles, frequencies and observed genotype counts in output. We welcome suggestions for additional features. Most of the added features in the past have come from suggestions by active SNAP users and testers. In the future we plan to include query options based on 1000 Genomes Project based LDdata. We also plan to keep up with additional SNP array releases as they come to our attention.

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.

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 .

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

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.

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

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.

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!

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.

Whose DNA was sequenced for the Human Genome Project?

This is intentionally not known to protect the volunteers who provided DNA samples for sequencing. The sequence is derived from the DNA of several volunteers….

Of course, projects to investigate small variations (simple or single nucleotide polymorphisms, or SNPs) such as the large HapMap effort and many other projects have revealed a lot of important single A, T, C, and Gs that vary among us. If you think of those as typos, you’ll have a sense of the scale of that variation. However, it is clear that there are other genomic variations that may be important for our understanding of any one individual’s genome. Whole pages or even whole chapters missing or added, not just typos, that might be an issue. Recently I got intrigued by that discovery of a possible deletion or duplication that might be associated with autism. So this wider look of these types of structural changes–deletions, insertions, inversions–got me really thinking about how to interpret data differently than I usually do when I consider the reference genome. It isn’t just the SNPs I need to keep in mind.

On to the paper itself:

Mapping and sequencing of structural variation from eight human genomes, by Kidd et al.

The paper in Nature is remarkable for many reasons, including the sheer volume of data it provides. This team examined the individual genomes of 8 different people. These same 8 people were examined in the HapMap project–so we can consider their genomes in the context of that variation project as well. Further, 5 of them are also part of the ENCODE project–so we will also be able to look at those genomes with that set of flashlights as well. I swear, those 5 people are going to be the most closely examined humans ever.

The researchers used several techniques to examine and confirm these structural variations in the genomes. They created whole genomic libraries of clones for each person–with about a million clones each. They sequenced each end to be able to map them to the reference genome. Think about how much that means in both physical and electronic reagents….And how much you can put in a figure on a regular page in a journal….

They mapped these clones and found over 75,000+ that differed in length or orientation. Some larger, some smaller, some flipped, some missing ends, etc. They whittled these down to around 3000 copy number variant candidates to pursue in more detail, and ended up with over 1,000 “non-redundant sites of copy-number variation” identified by restriction enzyme digestions. They validated these with microarrays that they had designed, and rescued another ~200 sites of variation with that strategy. Many of these could be additionally confirmed using SNP analyses as well. Inversions are more challenging to confirm, but they were able to find some with sequencing strategies and in situ hybridizations. End result: 1,695 sites of structural variation unearthed: 747 deletions, 724 insertions, 224 inversions. They discuss the occurrence of these in the context of the reference genome sequence, with some intriguing conclusions. You should go read the whole thing.

Of course, there is much more in the paper. Novel human sequences inserted in the genomes. Specific genes often affected by these changes. Examinations around human diversity. Very compelling stuff.

But there is just so much information in this paper–all those clones, all those individual implications….it simply can’t be contained in just a few pages. There is an extensive section of supplements for this paper. But even that isn’t enough. You have to check out the web site where you can see and interact with all of this clone data: http://hgsv.washington.edu/

The Human Genome Structural Variation Project site is a mirror of the UCSC Genome Browser that we know and love (and provide training for). But they overlay all of that clone data on top of the reference genome sequence information, and present it with the other genomic context that you can find the UCSC Genome Browser. So you can have it all in your view–and in your head–at once. It is a great way to go deeper with this data, and explore any genomic regions you might be interested in.

A traditional paper publication simply cannot contain all the data in a situation like this. This is an excellent example of one way to use the UCSC Genome Browser framework to present custom data for a project.

But it is such a huge advance in our understanding of genome-wide variations in humans, beyond the individual SNPs that have been such a major focus lately. We need to be thinking about the possible differences in each one of the pair of chromosomes that we carry. We need to be thinking about entire chunks of DNA that can be there, not there, twice there, or completely turned around. And if you think a personal genome scan for SNPs is going to provide all the information you may need to understand your biology, you may be missing some really crucial bits. Just keep that in mind as we move forward in the personal genome era. SNPs ain’t all, folks.

Image from: Jane Ades, NHGRI

Kidd, J.M., Cooper, G.M., Donahue, W.F., Hayden, H.S., Sampas, N., Graves, T., Hansen, N., Teague, B., Alkan, C., Antonacci, F., Haugen, E., Zerr, T., Yamada, N.A., Tsang, P., Newman, T.L., Tüzün, E., Cheng, Z., Ebling, H.M., Tusneem, N., David, R., Gillett, W., Phelps, K.A., Weaver, M., Saranga, D., Brand, A., Tao, W., Gustafson, E., McKernan, K., Chen, L., Malig, M., Smith, J.D., Korn, J.M., McCarroll, S.A., Altshuler, D.A., Peiffer, D.A., Dorschner, M., Stamatoyannopoulos, J., Schwartz, D., Nickerson, D.A., Mullikin, J.C., Wilson, R.K., Bruhn, L., Olson, M.V., Kaul, R., Smith, D.R., Eichler, E.E. (2008). Mapping and sequencing of structural variation from eight human genomes. Nature, 453(7191), 56-64. DOI: 10.1038/nature06862