Structuring the life sciences resourceome for Semantic Systems Biology: lessons from the BioGateway pro ject

Abstract

The application of Semantic Web technologies in the life sciences for data integration is still nascent. We have recently built BioGateway, an RDF store that integrates all the candidate OBO Foundry ontologies with other resources such as SWISS-PROT. In the course of developing BioGateway, we faced challenges that are common to other projects that involve large datasets in diverse formats. We present a detailed analysis of the obstacles that had to be solved in creating BioGateway. In doing so, we demonstrate the potential of a comprehensive application of Semantic Web technologies to global biomedical data. The time is ripe for launching a community e ort aiming at a wider acceptance and application of Semantic Web technologies in the life sciences domain. We make a public call for the creation of a forum that strives to implement a truly semantic life science foundation of a type of Systems Biology that we named Semantic Systems Biology.

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We witness a growing acceptance of Semantic Web technologies by the life science community for the purpose of knowledge management. This is illustrated by the existence of a W3C special interest group7 (HCLS IG) and many other projects that exploit semantic technologies, such as the Resource Description Framework 7 http://www.w3.org/2001/sw/hcls/ (RDF)8 and the Web Ontology Language (OWL)9, to represent biological information [1{7]. We are, however, just at the beginning of this process [ 8 ], and there are still many issues to be solved in order to build a semantic infrastructure that is adequate for biological knowledge management. Such an infrastructure will not only allow more e cient knowledge management; it will make possible a much more integrated and contextualised approach towards biomedical research. Semantic Web technologies have the potential to add a new dimension of knowledge integration to Systems Biology (SB), which is expected to be among the early adopters of these technologies [ 1 ]. We call this combination Semantic Systems Biology (SSB), a form of systems biology where new hypotheses about a biological system are not generated through a mathematical model but through global queries and reasoning on integrated data.

As part of our work towards SSB, we constructed BioGateway10, a system built upon an RDF store that aggregates bio-ontologies and other bioinformatics resources. It provides protein information for all the species with annotated genomes. Data integration is an important component in an SB approach, and with BioGateway we add a semantic foundation. But more important for SB is mathematical modelling. By integrating a systems network with a mathematical model, one can simulate the behaviour of the network, and predict the outcome of new experiments. With semantic knowledge bases, a querying and reasoning component could be added to this, where a mathematical model is not exploited, but new hypotheses about the system and its components are obtained. In short, the paradigm of Semantic Systems Biology acts as a complement to \standard" Systems Biology.

BioGateway allows a bioinformatician or biologist to query across a semantically integrated collection of resources at a systems level. BioGateway illustrates both the challenges and the bene ts that the Semantic Web brings to the life sciences, and we therefore elaborate in this paper on its technical properties and demonstrate its utility. Some of the problems we faced while building BioGateway lead us to conclude that there is a need for a wider Semantic Systems Biology forum to promote standards. 2 2.1

BioGateway data model

BioGateway graphs

BioGateway is a system holding an RDF store that combines information from di erent resources11: the entire set of candidate Open Biomedical Ontologies (OBO) Foundry ontologies [ 9 ], the complete collection of annotations provided by the Gene Ontology Annotation (GOA) les [ 10 ], a simpli ed version of the NCBI taxonomy [ 11 ] (including the names, ranks, and taxonomical hierarchy), a 8 http://www.w3.org/RDF/ 9 http://www.w3.org/2004/OWL/ 10 http://www.semantic-systems-biology.org/biogateway 11 http://www.semantic-systems-biology.org/biogateway/resources subset of SWISS-PROT [ 12 ] (excluding the sequences themselves, for instance), and the Cell Cycle Ontology (CCO)12.

All the imported data sources, when converted to RDF graphs, share a basic URI:

http://www.semantic-systems-biology.org

This means that each resource (e.g. each protein from SWISS-PROT, each taxon from the NCBI taxonomy, each OBO term) has a URI of the form: http://www.semantic-systems-biology.org/SSB#resource

Each of the imported data sources is represented as an individual graph with a speci c URI, of the following form:

http://www.semantic-systems-biology.org/graph name

Additionally, the SSB graph combines all the constituent graphs of BioGateway, containing about 175 million triples. Intermediate graphs for the GOA les and the OBO Foundry candidate ontologies contain about 160 million triples and 8 million triples respectively.

Many of the RDF graphs in BioGateway contain orthogonal resources not connected to each other, like SWISS-PROT and the OBO Foundry ontologies. SWISS-PROT resources are, however, linked to GO resources via GOA resources. This also interlinks the three sub-ontologies of GO. To accommodate evidence codes from GOA, a rei ed or n-ary node is created. For example, the following excerpt from a GOA le13 would be converted into the RDF structure shown in Figure 1: UniProtKB O03042 O03042

GO:0000287 GOA:spkw|GO_REF:0000004 IEA 2.2

BioGateway sca old: BioMetarel and MetaOnto

Two ontologies were created in order to provide a sca old to integrate all the graphs: Metaonto and BioMetarel.

BioMetarel14 holds the predicate types or relation types used to link subjects to objects. It also links the unique id's of the relation types with their user-friendly names. BioMetarel also contains all the meta-information, like transitivity and re exivity, about the biomedical relation types that are used. This relation ontology consists of a generic sca old, the Metarel ontology15 , to which all the relation types of RO [ 13 ], and all the relation types that are used in the OBO Foundry ontologies, are added. Unfortunately, these relation types were 12 http://www.cellcycleontology.org/ 13 ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/proteomes/3.A thaliana.goa 14 http://www.bioontology.org/ les/38667/biometarel.obo 15 http://www.semantic-systems-biology.org/metarel Fig. 1. RDF model of a GOA entry. The protein O03042 (Ribulose bisphosphate carboxylase large chain) is annotated with the GO term GO:0000287 (Magnesium Ion Binding), a term in the Molecular Function subtree from GO. Therefore, O03042 has the molecular function of binding magnesium ion. This fact is supported by IEA, that is, Inferred from Electronic Annotation. not named consistently throughout the candidate ontologies (e.g. the subsumption relation was called both is a and Is A, and the partonomic relation both part of and is part of ). A consistent list of relation types was manually created for BioMetarel. We chose as a rule to include a verb in every relation type name, conjugated as the third person singular in the present tense. The application of this rule predominantly involved the addition of the verb is. As a consequence, we can return triples in the form of a pseudo-grammatical sentence, like blood is located in vein. This rule also prompted us to transform names like anatomical relation to is anatomically related to and surrounding to surrounds. The meaning of several poorly named relation types in fact became clearer by adhering to this format. The RDF le of BioMetarel is uploaded as a separate graph in BioGateway.

The most straight-forward use of BioMetarel is to connect the unique id's of the relation types with their user-friendly names. We observed, however, that the inclusion of the full BioMetarel interfered with some speci c queries, like the listing of all the resources of a graph. Therefore, we created a lightweight subontology of BioMetarel, called Biorel. This subontology contains only relation types without the metaclasses and metarelations between relation types. This made Biorel more suited to be included in every single RDF graph in BioGateway.

Having such relations infrastructure implemented in BioGateway enabled us to build a consistent RDF sca old for other resources such as evidence codes and GOA associations. All we needed to create the integrated graph was to consistently use appropriate identi ers for the predicates in the RDF triples. The integration of OBO Foundry ontologies with respect to the classes did not pose problems, because these get di erent identi ers in di erent ontologies, and they should be orthogonal as a design principle.

A small ontology, Metaonto, was created in the OBO format for the mapping between the names of the OBO ontologies and the pre xes they use in their unique id's. The mapping is very useful for users who want to explore the OBO Foundry with queries in BioGateway. Meta-information like the names of the RDF graphs, the names of the OBO ontologies and characteristics of the relation types are accessible as results of the so called \ontological queries" (in opposition to \biological queries", see Section 5).

In summary, the integration of data in BioGateway has been achieved on the basis of the use of BioMetarel, the use of the same URIs for equivalent resources in the data sources (SwissProt, GOA, NCBI taxonomy) and the orthogonality of OBO ontologies with respect to the classes. 3

Design of BioGateway

While de ning the speci cation of the RDF-translations for each of the integrated resources, we also developed a library of queries (see Section 5). This resulted in an RDF model that is adequately suited for querying, in particular in terms of performance. During this process we have paid attention to several quality constraints: 1. Quick results: A relatively quick query answer is always a desirable feature for any system. Therefore, we have systematically tested the response time with a suite of queries. This quality constraint turned out to be the biggest challenge during the development of the system. One extra line (triple) in a SPARQL16 query could mean a huge di erence for the computational performance. This is one of the reasons why we did not pick existing ontologies or Systems Biology resources represented in OWL's (RDF/XML syntax), such as BioPAX [ 6 ] or SBML17. The verbosity of OWL's RDF/XML might work satisfactorily in other query systems having small OWL models, but it is a heavy burden for e cient SPARQL querying using current solutions. We could, however, substantially reduce the length of queries by RDF optimisation. As BioGateway was increasing in size during its development, the computational performance was decreasing dramatically when new resources were integrated. Therefore, next to the single graph integrating all the resources (SSB graph), we created RDF graphs corresponding to each of the constituent resources of BioGateway, which can still be combined in queries. By these optimisations, many queries answer within a second, while others can require about 10 seconds. 2. Human readable output: As RDF works with URIs, many outputs from SPARQL queries might be hard to comprehend. We tried to avoid such outputs as much as possible by creating labels for all the terms and all the relation types. These can be used to present the results to the user. 3. Good practice: RDF is a Semantic Web standard that implies good design practices18 when it comes to integration with other e orts within the framework of the Semantic Web. Orthogonality was achieved for all the terms, 16 http://www.w3.org/TR/rdf-sparql-query/ 17 http://sbml.org/Documents/Speci cations 18 http://www.w3.org/TR/2008/WD-swbp-vocab-pub-20080123/ meaning that the proteins in SWISS-PROT received the same unique id's as the proteins in GOA. Combining these graphs in a single query would not otherwise be possible. 3.1

Simulating transitive closure

Transitive closure is an important feature in biomedical knowledge representation, especially where it concerns partonomy [ 14 ]. In addition, transitive closure along the is a relation type is also desirable. Transitivity, however, cannot be expressed in RDF, and therefore it had to be created explicitly by adding all the necessary triples programmatically when loading the resources into the RDF triple store dedicated to BioGateway (see Section 3.2). That is, if resources A, B and C are related via part of (A part of B part of C), a third triple A part of C is created. This operation was done for the candidate OBO ontologies, the Cell Cycle Ontology and BioMetarel allowing transitivity in queries to be exploited with little impact on the performance of BioGateway. The ONTO-PERL [ 15 ] utility, used for adding the transitive closure over is a and part of, can be customized to consider other types of relations (e.g. located in). 3.2

BioGateway architecture

BioGateway serves as a gateway to distributed resources on the Web. An automated pipeline downloads the latest released resources on a local server every two months. The majority of the downloaded resources are converted to RDF using the ONTO-PERL suite [ 15 ], which contains RDF converters for the following formats: the OBO les (OBO format19), the tab delimited GOA les20, the NCBI taxonomy dump les21 and the SWISS-PROT entry les [ 12 ]. In addition, ONTO-PERL generates the necessary transitive closure graphs (see Section 3.1).

After that, the RDF les are uploaded into RDF graphs in Open Virtuoso22, which contains an endpoint (http://crunch.fvms.ugent.be:8891/sparql), where SPARQL queries can be submitted. A user interface (http://www.semanticsystems-biology.org/biogateway/querying) with a library of queries and an editbox points to the SPARQL endpoint, through simple HTML-technology (Figure 2). 4

Visualisation of query results

The visualisation of triple-based resources poses a special challenge. It is necessary to develop and deploy new interfaces to manipulate, query and visualize this knowledge in an intuitive way. A SPARQL browser (still under development) 19 http://www.geneontology.org/GO.format.obo-1 2.shtml 20 http://www.ebi.ac.uk/GOA/goaHelp.html#4 21 ftp://ftp.ncbi.nih.gov/pub/taxonomy/ 22 http://virtuoso.openlinksw.com/ OBO ...

RDF documents

Stored

RDF graphs endpoint

User

Interface

BioGateway enables querying and visual exploration of the results obtained using the BioGateway. It can be accessed from the SSB website23. With this interface, users can de ne a SPARQL query over BioGateway resources, the SPARQL endpoint could also be customised (by default it points to the SSB endpoint24) (Figure 3). After executing a query, a network of results is displayed (Figure 4). A tabular representation of the result is also available. The SPARQL browser has been developed using Flex technologies25, which provide powerful ways of creating interfaces with dynamic features. The entire source code is freely available26. 5

Queries

SPARQL queries can be executed against the BioGateway triple store27. Many sample queries are available at the web site, for example, the query in Figure 5 returns all the human proteins that are located in the nucleus (note the use of transitivity). 5.1

One-click query access

BioGateway provides a library of optimized, easily customisable SPARQL queries that make the resources easily accessible to both layman users and experts. Even SPARQL experts will not easily nd their way through RDF resources with which they are not acquainted. Therefore, we tried to re ect the basic query requirements in the library. It makes BioGateway accessible with a single click and it is a building block for future applications.

The library was split into a section with biological queries and a section with ontological queries. The biological queries are designed for usage by biomedical 23 http://www.semantic-systems-biology.org/sparql-viewer 24 http://crunch.fvms.ugent.be:8891/sparql 25 http://www.adobe.com/products/ ex/ 26 http://www.netthreads.co.uk 27 http://www.semantic-systems-biology.org/biogateway/querying Fig. 3. SPARQL viewer. The queries are selected from the drop-down menu on the top right: in this case, the query \Get proteins in the nucleus" is selected. Queries can be customised, for example, by changing the parameters. # NAME : get_proteins_in_nucleus # PARAMETER: GO_0005634: the nucleus # PARAMETER: 25.H_sapiens: the GOA graph for human # FUNCTION : returns all the human proteins that have the # nucleus as annotated location BASE <http://www.semantic-systems-biology.org/> PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#> PREFIX ssb:<http://www.semantic-systems-biology.org/SSB#> SELECT ?protein ?sublocation ?protein_id WHERE {

GRAPH <25.H_sapiens> { ?protein_id ssb:located_in ?sublocation_id.

?protein_id rdfs:label ?protein. } GRAPH <gene_ontology_edit_tc> { ?sublocation_id rdfs:label ?sublocation. ?sublocation_id ssb:is_a ?sublocpart_id.

?sublocpart_id ssb:part_of ssb:GO_0005634. }

} scientists, and they draw on the most relevant part of the knowledge base. Some examples of biological queries read as follows: 1. Get the proteins with a speci c function/location/process for any of the annotated organisms. For example in Figure 5 a query that returns all the human proteins that are located in the nucleus can be seen. 2. Get the information on the function, location, process and associated disease for a given protein. 3. Get the proteins that are involved in the \psoriasis" disease.

On the other hand, the set of ontological queries shows how SPARQL can be used to explore BioGateway and speci cally the OBO ontologies. This set of queries is intended for users interested in ontology engineering. Any future applications that build on the results of SPARQL queries will certainly bene t from the availability of basic navigation-type queries like get neighborhood, get the root of an ontology, get the hierarchy to the root, get graphs, etc. These queries explore the typical network structure of RDF models. On the other hand, the ontological queries show the RDF semantics that are available in BioGateway, like subsumption, transitivity and composition of relations. Some examples of ontological queries read as follows: 1. Query the OBO Foundry: search on names and get their unique id's. 2. Get all the neighbor terms of a given term. 3. Get all the properties, like de nition, synonyms, etc., of a given OBO term.

Both sections of the library make BioGateway a workbench for creating SPARQL queries. Often, the results of a query can be used to copy-paste as a parameter in other queries. We elaborate this idea further in Section 5.2.

All the queries in the library were provided with a name, their function and a list of parameters that can be customised in a query. By using pre xes properly, a SPARQL query can be written in such a way that a parameter needs replacement only in one xed place. All the queries in the library were written in that way. 5.2

Combining regular RDF graphs with transitive closure graphs.

One of the ontological queries in the library is designed to nd the closest common ancestor in the hierarchy of an ontology for two given terms (Figure 6).

For this query we need both the regular RDF ontology and its transitive closure (SSB tc, which is generated by the pipeline, see Section 3.2) . In fact, the query might be reduced to: nd all the ancestors of both terms that do not have any descendants that are ancestral to both terms. To nd all the terms that are ancestors of both terms, we need the transitive closure graph, as in that form all the ancestors are directly linked to their descendants. Two triples in the query are enough to retrieve their id: GRAPH <SSB_tc> { term1_id: ssb:is_a ?common_ancestor_id. term2_id: ssb:is_a ?common_ancestor_id. } # NAME : get_common_ancestor # PARAMETER: GO_0002617: the first query-term # PARAMETER: GO_0034125: the second query-term # FUNCTION : returns the closest common ancestor-term in the # hierarchy for two given terms BASE <http://www.semantic-systems-biology.org/> PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#> PREFIX ssb:<http://www.semantic-systems-biology.org/SSB#> PREFIX term1_id: <SSB#GO_0002617> PREFIX term2_id: <SSB#GO_0034125> SELECT distinct ?common_ancestor ?common_ancestor_id WHERE {

GRAPH <SSB_tc> { term1_id: ssb:is_a ?common_ancestor_id. term2_id: ssb:is_a ?common_ancestor_id.

OPTIONAL { term1_id: ssb:is_a ?direct_child. term2_id: ssb:is_a ?direct_child.

GRAPH <SSB> {

?direct_child ssb:is_a ?common_ancestor_id.

} } ?common_ancestor_id rdfs:label ?common_ancestor. } } FILTER(!bound(?direct_child))

We get all common ancestors with this query, while we only want the closest ones. Therefore, we check for the children of this set of ancestors. This can be best accomplished in the ontology without transitive closure: GRAPH <SSB> { ?direct_child ssb:is_a ?common_ancestor_id. }

Additionally, we check whether these children belong to the same set of common ancestors as de ned before: term1_id: ssb:is_a ?direct_child. term2_id: ssb:is_a ?direct_child.

The last two checks go in an optional clause, because we only want the common ancestors for which these checks fail. In this way, we can lter the common ancestors for which this kind of ?direct child does not exist: FILTER(!bound(?direct_child)) 6

Discussion

Currently, the life sciences community is becoming aware of the need for standards and standardised ways to archive data and metadata [ 16, 17, 9 ]. The W3C provides formal standards to represent knowledge (e.g. RDF, OWL). Although there are still limitations with respect to the representation of some type of information (e.g. spatio-temporal information) or it may prove di cult to model complex scenarios (such as expression data from microarray experiments), these standards have been shown to accommodate information that can be queried to gain further biological insights [18{20].

Here we have presented an RDF triple store, named BioGateway, that integrates di erent life science knowledge resources. Other projects [21{24, 4] have attempted similar integration. They, however, either used smaller sets of data or o ered limited query possibilities due to performance issues.

In the future, BioGateway will include more resources, for example OBO cross products28. Other BioGateway extensions will include an improved SPARQL interface and an enhanced integration of graphs.

In an ideal world where the Semantic Web is completely operational, BioGateway would probably be obsolete. That ideal world is, however, still far away, and many issues will have to be solved if the Semantic Web is ever to become fully operational. 28 http://obofoundry.org/index.cgi?show=mappings Biological identi ers A universal resolvable mechanism for identifying biological entities is vital for a life sciences Semantic Web [ 8 ]. There have been di erent attempts in that direction. For example, the OBO Foundry insists that the ontologies have unique identi ers that are orthogonal to identi ers in other OBO Foundry ontologies. Such identi ers, however, are not resolvable and therefore not scalable [ 8 ]. Currently, there are mechanisms proposed for resolvable identi ers, such as URIs29, LSIDs30, OKKAM IDs31 and MIRIAM URIs [ 25 ]. URIs are used for identi ers in OWL or RDF ontologies, and therefore they o er an efcient foundation. In the case of BioGateway, ad hoc URL-based identi ers were created. They are expected to be standardised once the SSB forum is established. Lack of semantic content Most biological information is either not adequately semantically codi ed or it has been codi ed with a poor axiomatisation [ 26 ]. This information should be richly codi ed using Semantic Web languages like RDF or OWL, which is not a trivial task given the disparity of the assumptions behind languages like OBOF or OWL [ 27, 28 ].

Most biologists are still unaware of the importance of semantically codifying knowledge, and perceive semantic languages as a nuisance. Best practices are needed to help biologists create semantic ontologies [ 29 ], so that in the future a global and distributed group of high-quality RDF/OWL ontologies will be a reality.

The content of these ontologies should be non redundant and have common foundations, e.g. RO, for facilitating alignments and cross products. Semantic languages, tools and interfaces Even though ontology editors, reasoners, APIs and Knowledge Base (KB) software for the Semantic Web have advanced a lot over the last few years, they still fall short of constituting established and robust technology, especially when it comes to their utility and reliability. On the language side, OWL is evolving fast and many new features are expected to appear in OWL 232.

The problems that we observed can only be addressed at the community level. Therefore, we make a public call for the creation and development of a Semantic System Biology community with the following aims: 1. Encourage and facilitate the creation of semantic bio-content. 2. Develop agreed upon best practices for such content creation. 3. Collect and index such content. 4. Agree upon and encourage a mechanism for identifying biological entities. 5. Facilitate the communication between the semantic technology developers and the life scientist, the users of such technology. 29 http://bio2rdf.wiki.sourceforge.net/Ban +Manifesto 30 http://lsrn.org/ 31 http://okkam.org/ 32 http://www.w3.org/TR/2008/WD-owl2-syntax-20080411/

This community should have objectives beyond those of the OBO Foundry: it should build upon the best of OBO (the community, the content creation guidelines, and the content) and exploit it in a standardized platform with emerging Semantic Web qualities. As a rst step towards such a community, we are building the Semantic Systems Biology wiki33.

We venture to consider the following topics to organize and structure the life sciences Semantic Web resourceome, and to de ne a set of principles:

We strongly feel this is the appropriate moment to establish such a community to bolster and extend the current e orts (e.g. HCLS IG, NeuroCommons34) and to begin building a universal, interoperable knowledge architecture [ 30 ]. Such a structured resource will further ensure that semantic technologies will become one of the most crucial means for knowledge integration in the life sciences [ 31 ].

Acknowledgements

This work was funded by the EU FP6 (LSHG-CT-2004-512143). EA was funded by the European Science Foundation (ESF) for the activity entitled Frontiers of Functional Genomics, ME by the University of Manchester and the EPSRC. 33 http://www.bio.ntnu.no/systemsbiology/ssbwiki 34 http://neurocommons.org

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