Bringing Genome Wide Associations to Life

A year in to the EBI's joint project with the NHGRI, the GWAS catalog and data visualization has become interactive with help from Semantic Web technologies.

In the fall of 2010, we started an informal collaboration with the NHGRI's Catalog of Genome Wide Association Studies (Hindorff et al, 2009) with our student, Paulo Silva. In that project, he rapidly created an ontology based on our ongoing EFO efforts to describe a lot of the traits in GWAS (see the NHGRI site for more on how the catalog is put together). Paulo deployed this using the existing ArrayExpress website infrastructure as a backbone to illustrate the benefits of using an ontology for curation and searching. Later that year, Paulo and Helen Parkinson presented this work to the NHGRI.

Figure 1. GWAS Catalog as of 2011. Much skilled work put this beautiful artefact
together. "My God, it's full of stars!"

Thankfully, they liked it and in October 2011 the EBI started a formal collaborative project with the NHGRI to improve the process of curation based on our expertise in using ontologies for annotation and in deploying them in applications. In addition, the famous and much-cited GWAS catalog diagram (see figure 1) was also to be given an overhaul in the way it was generated. The diagram illustrates GWAS traits associated with a SNP on a band on a human chromosome, and is generated manually by a skilled graphical artist every three months. If you take a look at the diagram you can quickly see that this is no easy task. New traits can appear (almost) anywhere each month and this can mean a lot of shuffling around of coloured dots to accommodate them. In addition, each trait has a unique colour which is described by a complex key (see figure 2)  - complex because there are so many traits and therefore a lot of colours required. In fact, the harder the team work to curate this data, the harder it becomes to generate the diagram. The lazy person in me thought the answer was simple - stop working so hard!

Figure 2. The key keeps growing as more work is included. Each of
these colours is unique. My favourite is #99FFFF

But they continue to work hard regardless and so a further issue presents itself; that searching the catalogue, either by the search interface or by viewing the GWAS image, is also hampered by the growing size and lack of structure to the trait names. A small list and this is not really a problem, but as the catalog has grown thanks to the curation efforts of the NHGRI, so have the amount of traits.

Motivation complete, let's look at the progress so far. You can click here to see the new diagram generated by the team at EBI* (please see below for the list of contributors). The first thing to note is that it is now an interactive, dynamic diagram. You can zoom in and out, you can mouse over traits and find out what they represent rather than have to look at the key. The default key has also been reduced down to higher level groupings, significantly reducing the number of colours used. These groupings correspond to ontology classes in the EFO which are superclasses of other traits and are generated based on maximum coverage (i.e. the 18 classes that covered the most amount of traits). One of the advantages of using an ontology emerge here; that you can begin to aggregate data together and get a better feel for global trends.

In addition, you can also drill down to more specific traits using the filter option. Because the diagram is dynamic, you can highlight only those traits of interest to you. Try entering 'cancer' into the 'Filter' box and press enter. The ontology is being used to show those SNPs annotated to cancer or subclasses of cancer. Now try 'breast cancer' and you can see a subset of these cancer trait dots remain highlighted. What about 'breast carcinoma'? Have a go. Clear the filters (click clear filter) and then enter 'breast carcinoma' and you'll see the same results as for breast cancer. Again, this is ontology at work; the browser is using synonyms stored in the ontology to perform the same query it does for 'breast cancer'. Simple, but very useful.

The benefit of generating the diagram programmatically is perhaps most evident in the time series view (click on the tab). A selection of diagrams from over the last seven years is shown here and all were generated with just a few clicks. This is in much contrast to the manual, hand-crafted artefacts that went before which took many weeks and much skill to produce. Seems almost unfair really.

The Techy Bit

So what's going on behind the scenes? The list of over 650 highly diverse traits, are mapped to EFO. These traits include phenotypes, e.g. hair colour, treatment responses, e.g. response to antineoplastic agents, diseases and more. Compound traits such as 'Type 2 diabetes and gout' are separated. Links between relevant traits are also made to facilitate querying (e.g. partonomy). It's also worth mentioning that EFO reuses (i.e. imports from) a lot of existing ontologies such as the Gene Ontology and Human Phenotype Ontology which also facilitates future integration.

The GWAS ontology is a small ontology used within the triple store to describe a GWAS experiment structure (not including traits) and describes: links between a trait and a SNP and the p-value of that association; where that SNP appears on a chromosomal band; and on which chromosome. Each chromosome is an SVG in which, the XML source has been tagged with ontology information, e.g. these coordinates of the SVG are band x. The dots representing traits are similarly assigned the trait type (from the ontology) within the class attribute of the SVG. These bits of information are used to render each trait on the images, and allows for the filtering on trait - and (in the very near future) on other properties such as the association p-value.

DL queries are also employed as part of this visualisation, making use of the OWL describing the two ontologies (EFO and GWAS ontology). The initial creation of the diagram uses a DL query which asks, amongst other things, which bands are on which chromosomes, which SNPs are on each band and which traits should be rendered where. Traits are selected using another DL query which, as well as location information, asks if they satisfy a certain p-value (which is a datatype property) - those below this threshold are not rendered. In the future this will become more dynamic so a user can specify their own p-value threshold.

These queries all take a while so they are all pre-computed when the diagram is built each release and then each SVG is cached on disk for quick retrieval. The actual filtering queries (which will also become auto-complete in the near future) also use a simple DL query; they ask for all the subclasses of a given trait class from EFO and these traits are passed back in a JSON object which is used to refresh the diagram, showing SVGs of this class. In the longer term, the aim is to use disk caching of the reasoner using something like Ehcache. Currently this is not possible due to some serialization issues with the version of the OWL-API that is being used but this is set to change. This will enable much more complex queries to be performed, utilising EFO, dynamically such as traits that may be the subject of a protocol of measurement or diseases that affect a particular system. There are many possibilities.

A side-effect of this project is that the technology is reusable for other projects and indeed we intend to utilise some of this for some of our current work in rendering the Gene Expression Atlas in RDF (more on that very soon). The model is relatively simple; describe your data in some schema (OWL ontology), work out what each ontology looks in SVG (the relationship between an ontology class and the image) and then render it.

This is all done using Semantic Web technologies; OWL, RDF, triple stores, ontologies, description logic reasoners. And you'd never know looking at the website. I've always thought this is the perfect example of when Sem Web comes good - it just works and the user never knows about the guts. This is the right way round I think; the biology should come first in this application, the technology is secondary. 99% of users won't really care that your website uses CSS or JavaScript the same should be true here. When Semantic Web technologies hit this level of brilliant but quiet, accepted ubiquity, we have reached our goal.

*List of contributors
The tooling for this project was developed by Danielle Welter and Tony Burdett, with input from Helen Parkinson, Jackie MacArthur and Joannella Morales at the EBI and the catalogue curation team at the NHGRI.

Semantics of the Semantic Web Journal

I've been looking at the Semantic Web Journal as we've a few bits of Sem Web work we're doing (which I'll discuss in a future blog) which I think is interesting and probably worth publishing (eventually) and I've seen a few interesting articles published there. My friend and colleague Phil Lord has oft talked /battered me into submission about the Semantics of Publishing - a slightly different take than a journal about the Semantic Web - but, nevertheless, relevant.

Phil's work has concentrated on adding value to publications using things like Greycite which gives a nice way of searching webpages for metadata and presenting a way of citing, for example, blog articles. This is extremely useful in a world where a journal publication is really only part of the modern online literature we read and might like to cite.

This is what I was hoping for deep below the surface of each
webpage. Beautiful, icy RDF and maybe a penguin or two.

I had hoped the Semantic Web Journal would take things a step further by providing Semantic Web style metadata behind the pages so I had a bit of trawl using Greycite and cURL to see what I could get back by analysing the HTML or perhaps any RDF through content negotiation. Sadly, I got nothing back other than HTML. In fact, not even any meta keywords were in the head of the HTML, and nothing back from cURL requests for RDF.

So I'm left a little disheartened; not annoyed, but disappointed. I think if you're going to lead the line by publishing articles on the Semantic Web, it would be good practise to add semantics to your Semantic Web journal, probably. I suppose, in return the onus is then us so-called practitioners to also follow the good example set. We've been doing this for a while for EFO but I confess my own website also lacks RDF behind it but I could be convinced to do so if there was a Linked Data framework that would add value to it. Ironically, this journal has some great articles on exactly these areas. The OntoBee paper talks about a service which does a lot of this stuff for ontology class and individuals in RDF and HTML at the same time and it all works rather well, but there are multiple other ways of doing this, and, as Greycite demonstrates, they need not be complex. Just useful.

There is one other thing I should add, which I really like about this journal; an open and transparent review process, posting all peer reviews on the journal website. This is one of my biggest gripes about science as it stands. I think the review process is flawed. I think reviews should be open and transparent for journals, conferences and grants.

Why choosing ontologies should not be like choosing Pepsi or Coke

I've just returned from the International Conference on Biomedical Ontology (ICBO) which is the biggest conference in the field of bio-ontology. I was invited to sit on a panel which was somewhat provocatively titled 'How to deal with sectarianism in biomedical ontology' during which we discussed how we might better get along and defuse some of the issues that have plagued the community over the last decade or so.

Fish-Tree-Pepsi-Coke. I know what you're thinking but
just go with me on this and read the post.

The range of views of the panelists was interesting, in part because they were not as extreme as one might have expected, or at least as they might have been five years ago. I'll attempt to summarise the panelists thoughts based on their initial two minute 'pitch' slide, I've included a link to the participants slide:

Alan Rector - Alan mentioned the need for humility and to understand what a given ontology is designed to do before we criticise it as they are can be made for different purposes. He also mentioned the need for proper evaluation.

Chris Stoeckert - Chris stated that sectarianism is inevitable and that he had chosen his sect which was BFO/realism. Ultimately, he said the biggest sect wins and that this is the OBO Foundry, which, as a community effort, we should join.

Barry Smith - Barry suggested that any ontology of any worth should be developed by an ontologist that has signed up to a 'code of ethics' which includes principles of reuse, aggressive testing in multiple real world applications and of  'thinking first' before adding a term or definition.

My own stance was that in general, I don't think a sectarian approach is very useful, not only because it causes political divides within our community, but because it also alienates us from other communities who, from the outside looking in, may be less likely to engage with us. And that hurts us because above all else we need users, more than they need us. I also think competition is fine. This is in general how science has worked for quite some time, moreover, if it didn't then we would never have made leaps forward by listening to the minority voices on issues such as evolution and Copernican heliocentrism. 

But underlying everything I said is my desire to see ontology engineering become a first class citizen and mature as a discipline. My job, in part, entails building ontologies for millions of data points with much diversity; 1,000 species, 30,000 experiments, 250,000 unique annotations. If people are willing to call out that I should be using ontology a instead of ontology b, then I need to know why, and this can not be based on subjective or political opinions. I want to see the development of formal, objective metrics to determine whether or not one ontology is better than another, so that we can really measure these artifacts and have something scientific to base our judgements on.

Alan Rector also rightly points out ontologies are built for different purposes so we need to factor that in. As Einstein said "if you judge a fish by its ability to climb a tree, it will live its whole life believing that it is stupid." If Amazon used an ontology to power their website, it would be hard to argue that particular fish is not a good artifact as the Amazon application seems to work pretty well.

I've also heard many comments from certain quarters about an 'ontology crisis' wherein ontologies of poor quality are now everywhere to be seen, polluting the pool. This sort of comment is similar to comments made during the software crisis of the 1960s, and, given that funding for ontologies can be hard to come by, we can ill afford to overrun. They reacted to this by developing software engineering processes and methods which, over time, helped enormously, though they did not resolve all the issues, cf. no silver bullet. Whatever your stance, it is hard to argue against wanting proper processes and methods for building in quality; nobody wants a blue screen of death on a plane's fly-by-wire system during a transatlantic flight. Similarly, nobody wants a medical system using an ontology to give incorrect results. An ontology for your photo collection, we care less so.

So what do we need? Here's my list;

1. A formal set of engineering principles for systematic, disciplined, quantifiable approach to the design, development, operation, and maintenance of ontologies
2. The use of test driven development, in particular using sets of (if appropriate, user collected) competency questions which an ontology guarantees to answer, with examples of those answers - think of this as similar to unit testing
3. Cost benefit analysis for adopting frameworks such as upper ontologies, this includes aspects such as cost of training for use in development, cost to end users in understanding ontologies built using such frameworks, cost benefits measured as per metrics such as those above (e.g. answering competency questions) and risk of adoption (such as significant changes or longer term support).

In a sentence; making public judgements on ontologies should be a formal, objective and quantifiable process and less like deciding whether you'd prefer a Pespi or a Coke.

Incidentally, I prefer Coke.

A million gene expression annotations with Zooma

ArrayExpress, based at the EBI, is one of the world's largest public repositories of transcriptomic data. One of the much valued features of the repository is that data submitted undergoes curation, not only by computational assessment but ultimately by manual experts - our curators. Their job is to ensure this data meets certain minimum quality requirements and is described in a way that is accurate (ontologies help with this) and therefore searchable in the archive.

Zooma - Like an Ooma but even faster.

This is a much valued service by a lot of the community, but it is not without issues. One of the primary issues is that data submitted to ArrayExpress continues to increase. In fact, even though it has been postulated that microarrays are dead there is no sign submission of these experiments is slowing, in fact our figures show they are on the whole stable. On top of this, new sequencing technologies are emerging almost monthly it would seem and our figures also show a slow but steady increase in this sort of submission. Overall then, this leads to a net increase in the amount of submissions coming into ArrayExpress.

So business is good, but this is not without its drawbacks. The primary one is cost and I mean this in the broadest sense; high quality annotations are time-consuming and there is a limit to how many experiments a curator can curate. Simply put, more experiments means more resources are required. We call this an annotation gap, i.e. the gap between high-quality data annotation (especially using ontology classes) and the amount of resources available to do such annotation.

Search the Zooma KB for an annotation. The pop out
here is showing info for the first hit for Caucaisan used as
as a value for a category ethnicity. This pattern has been
used 608 times and each annotation has its own URL. 

One of the ways of reducing this annotation gap is by enabling submitters to annotate their own data more easily and more aligned to a common standard, in this case the ontologies we use. This reduces the effort required by curators to make sure everything is aligned within the repository. Another way, is to maximise the amount of automated annotation against such ontologies that can be done. The is the job of Zooma.

Zooma is an RDF knowledge base of annotation knowledge, extracted from the expert curation performed on a subset of ArrayExpress data. This subset of data has the added advantage of being curated twice because it has also been loaded into the Gene Expression Atlas, where it has been aligned to ontology classes in EFO. This is very powerful for several reasons.

Firstly, it enables access to the curation process. This is useful because it allows a person to easily look-up how a property (some textual item) has been annotated to an ontology class and therefore repeat the process - the users here are both external submitted and our own curation team (see image). This makes curation consistent and rich. An additional benefit of this is that it also enables computational exploitation of the curation process. Not only does Zooma capture how a textual property has been mapped to an ontology class, but it also captures corrections between annotations, for example an update to a more accurate class. What this really gives us is a big set of rules, manually created over several years, which can be applied to data automatically.

A second  feature is that provenance is stored and used for ranking and filtering. Using the Open Annotation Model, where an annotation 'rule' has come from, for instance curator asserted or inferred from the knowledge base, is recorded.

A third feature is that everything has a URI. Every annotation, sample, assay, study and they link to experiments in ArrayExpress and the BioSample Database. So this is truly linked (to) data.

Finally, this model is additive. Not only can our own, new additions be added by our curators and submitters, but any annotation based on our simple abstract model can be incorporated - including a whole database dump.


Have a play with the live demo at http://wwwdev.ebi.ac.uk/fgpt/zooma
Tony Burdett will be presenting this work at ISMB Technology Track on Sunday July 15th at 15:30.
This work is supported in part by EMBL and by the DBP project with NCBO via NIH.

Has the War of the Words alienated Google?

A great wordsmith once wrote;

What's in a name? that which we call a rose
By any other name would smell as sweet

This is taken from Shakespeare's play Romeo and Juliet and it captures two themes I'd like to briefly explore in this post. Firstly, that the essence of what a thing is does not rely on its name alone. Secondly, that feuding about a thing can unintentionally damage a thing.

Your search did not match any documents.
Did you mean Brazillian Shakespeare Horse Jupiter Mountain?

'And what does that have to do with Google?', you might well ask. In May 2012 Google announced the Knowledge Graph search enhancement which they headlined as 'things not strings'.  This led to much discussion in various press outlets in the last month or so about how Google were going to use this new way of indexing web pages to give you more intelligent searching, such as dealing with homonyms - words that have multiple, different meanings (e.g. tire - car wheel, tire - sleepy). This sounds great and, in fact, I think it is. But the idea is nothing new to anyone who has been working in ontology, semantic web or more specialist cases like biomedical data curation. It's one of the driving use cases of ontologies - I refer you to my blog on what an ontology does. So how does Google's new Knowledge Graph differ? I noted with particular dismay that Google's blog did not contain the words 'ontology' and 'semantic'. Various press stories which talk about this hint at it without saying it and many proclaim this to be fundamental new technology, with a tip of the hat to Yahoo's 2009 paper.

Credit to Google though - they have actually implemented something and they are using it, that is more than a lot of practitioners do. But the question remains - why are the words 'ontology' and 'semantic web' missing from these articles, including Google's own? An ontology by any other name is still an ontology - concepts, relationships, graph nodes, edges, types, instances, whatever you call it.

I think the answer may lie in my second theme; the War of the Words. In biomedical ontologies, a field in which I am closely involved, there is an undercurrent of strong opinions and cutting debate with the aim of building consensus. Undercurrent is probably inaccurate because it's actually highly visible - it's more like a tsunami. See the 2010 Merrill and Smith papers for a peek at some of this. Ontologies, even from within the community, divide opinion, engender indignation and entrench viewpoints and to those on the outside this must sometimes seem, well, problematic. It's not always this way of course - there are many great things happening in these communities and some times they unite opinion, reduce division and bridge viewpoints. Collaborative work from many different communities continues and I have been party to several such efforts, with mixed success, but then getting everyone to agree is intrinsically hard. The worry is that, perhaps, the success stories are overshadowed by the war of words. The punch is mightier than the handshake, sadly and perhaps this is the root of my disappointment. I've heard the words 'if ontologies/sem web were really that good Google would be using them' often from those outside these communities.

There is also a feeling from certain quarters that the Semantic Web, as it was originally cast, has also failed to live up to the hype and that what I consider to be a simplified version (avoiding grand gestures) - Linked Data - is similarly floundering. I should add this is not a one-sided argument though and many believe it has and is succeeding though perhaps it needs to do more. Indeed I personally believe that we are now in a better position than ever to exploit these technologies and I am already involved in a project here at EBI which is doing just that. I'll report that in the future.

It would seem apparent that Google are using something akin to ontologies, and possibly Semantic Web technologies, but are unwilling to beat the drum about these overloaded and much travelled buzz words. These words come with baggage, high expectations and a strongly opinionated community. It may just be an omission by Google of course, accidental in nature, and in the coming months they will begin to champion the cause and recognise the work that goes on in the ontology and semantic web communities. If they want a success story then I refer them to the Knowledge Graph that is the Gene Ontology, circa 1999.

Perhaps Google's Knowledge Graph is the killer app that everyone has been 'searching' for; the ontology is dead long live the Knowledge Graph. Let us not, then, kill their efforts with semantics, they're just words after all and in the end, what's in a name?