Towards linked open gene mutations data

The conversion of relational database contents into the Semantic Web

The Semantic Web [1] is gaining momentum as a framework for the development of next-generation bioinformatics data integration tools since its standards and technologies seem to have now reached enough maturity to be considered a viable solution for data integration challenges. Semantic Web based approaches to biomedical data integration have already been proposed a number of times in recent years [2–6]. Most recently an approach known as Linked Data, or Web of Data is being explored.

The vision of the Semantic Web is to evolve the Web into a distributed knowledge base: this vision relies on its evolution from the current Web of Documents, where each node of the network is represented by an unstructured document, into a Web of Data, where each node represents machine processable information. In this context, access to information is achieved through portals and search engines whose behavior is supported by semantic features. A good introduction to the Web of Data can be found in [7].

A relevant contribution to this evolution of the Web may come from the conversion of data stored in Relational Databases (RDB) into a viable representation such as the Resource Description Framework (RDF) [8], which is the basic technology to represent information in the Semantic Web. RDF is based on the composition of simple predicates ("triples") made by three elements identifying "Subject", "Predicate" (or "Property"), and "Object". Here, semantics can easily be associated to property definitions, while subjects usually are well identified entities and objects may either represent related entities or values.

Many research works have therefore been focused either on the static conversion or on the dynamic mapping of data from RDB to RDF. They have led to the implementation of both mapping tools and domain-specific applications. Some mappings are automatically generated via a simple association where the name of the relational table is mapped to an RDF class node and the names of its columns are used as RDF predicates. As a consequence, cell values are mapped to instances or data values. In this case, entities and relations, as well as their meaning, reflect the RDB schema and the knowledge of the schema is needed to understand the exported information.

In other mappings, relations and entities of the original databases are converted to a representation which is instead based on a shared conceptualization that can be, even significantly, different from the schema of the database. Differences may relate to properties, relationships, and even entity values (e.g., different coding applied, split/merged values). In this case, automatic mappings can serve as a starting point to quickly create customized, domain-specific mappings.

Relational to RDF mapping software exists both as independent tools (e.g.: D2RQ and Triplify), or as part of a larger suite (e.g. Allegrograph, Sesame, OWLim, Virtuoso). In general, they are components of a wider range of software solutions which can expose RDF entities and relations in structured information resources. A list of these tools is available on-line [9].

In the biomedical domain, an exemplar resource is represented by Bio2RDF [10], a system that allows an integrated access to a vast number of biomedical databases through Semantic Web technologies, i.e. RDF for data representation and SPARQL (SPARQL Protocol and RDF Query Language) [11] for queries. To this aim, many databases have been converted to RDF by special scripts, called RDFizers, while some information systems that were already offering a viable format and interface where directly linked to the system.

This conversion was based on a unified ontology, taking care of properties included in the information resources already available in RDF. Moreover, the system provided a unified URI schema, overcoming heterogeneity of URIs already provided by other systems. All major genomics, proteomics, networks and pathways, and nomenclatures databases were included in the system, as well as some clinical, e.g. Online Mendelian Inheritance in Man (OMIM), and bibliographic ones, e.g. PubMed, and the Gene Ontology.

The Linked Open Data (LOD) initiative, a Community Project at World Wide Web Consortium (W3C), aims at extending "the Web with a data commons by publishing various open data sets as RDF on the Web and by setting RDF links between data items from different data sources" [12]. In this context, many biomedical databases have already been made available (a Linked Open Data cloud diagram is available on-line [13]). Many of these datasets derive from Bio2RDF, but there are also some that were independently built, e.g. Diseasome, a dataset extracted from OMIM that includes information on disorders and disease-related genes linked by known associations.

Human variation data and the Semantic Web

In the last decade, with the advent of high-throughput technologies, sequencing has become faster and less expensive. As a consequence, a great wealth of data is being produced in order to identify variation data, i.e. specific, individual and sub-population related information. One of the best known projects of this kind is "1,000 Genomes", an international collaboration that recently ended its pilot phase [14, 15]. The goal of the pilot phase was the identification of at least the 95% of variations present in at least 1% of individuals in three distinct populations by means of Next-Generation Sequencing technologies. This led to the production of ca. 4.9 Tbases (about 3 Gbases/individual) and to the determination of 15 millions mutations, 1 million deletions/insertions, and 20,000 variants of greater size.

Such information constitutes the basis on which genomics may meet clinical information, correlation analyses between genotypes and phenotypes may be carried out, and the perspectives of genomic or personalized medicine may be realized [16, 17].

Although several databases on gene mutation and variation for humans exist, their semantic annotation is very limited and their formats are heterogeneous. Overall, only a little information on human variation is included in the Web of Data and/or it is available on-line in implementations that are based on Semantic Web technologies. This is the case, e.g., for the data on impact of protein mutations on their function that was extracted from scientific literature by using a specialized text mining pipeline by Laurila et al [18] (in this case, data is available on-line through a SPARQL endpoint, but access is restricted to authorized users only).

Lists of Locus Specific Data Bases (LSDB) and other databases related to human variation, like those related to Disease Centered Mutations, SNPs (Single Nucleotide Polymorphisms), National and Ethnic Mutations, Mitochondrial Mutations, and Chromosomal Variation, are available on-line at the site of the Human Genome Variation Society (HGVS) [19, 20], although many of these lists are not up-to-date. Indeed, the best human variation information is available in curated databases, many of which are managed by means of the Leiden Open Variation Database (LOVD) [21] schema and system. Many other databases are managed by proprietary systems. The Human Variome Project (HVP) [22] has produced recommendations for nomenclatures of variations and for contents of mutation databases.

The issue of integrating variation data with molecular biology databases is however well known. Conditions for the integration of LSDBs with other biological databases have been outlined by den Dunnen et al in [23]. In this paper, a distinction is made between the information that should be shared and the one that could be shared. In the former set, only some reference data are defined, including contact information for the database, identifiers of the gene in various databases, a unique reference to the sequence, and the description of the mutation at DNA level. In the latter set, that includes data on original bibliography, changes at protein and RNA levels, and associated pathogeniticy, issues related to ownerships and quality of data are also present.

Shared property definitions for human variation data

Integrating data on the Semantic Web is mainly a matter of shared and reusable properties' definitions and unique data identifiers. Some mutation related ontologies exist. These include the Variation Ontology (VariO) [24] and the Mutation Impact Ontology (MIO) [25].

VariO is still in a development phase, not officially released for annotation or analysis purposes. It is aiming at providing standardized, systematic descriptions of effects and consequences of position specific variations. It can be used to describe effects and consequences of variations at different levels (DNA, RNA protein). VariO reuses some terms and definitions from Sequence Ontology (SO), Gene Ontology (GO), and other ontologies.

MIO was developed to support semantic extraction and grounding of mutation impact data from literature. The ontology has a strong use case in the publication of text mining results through semantic Web Services [18] in the framework of the Semantic Automated Discovery and Integration (SADI) [26, 27] infrastructure.

Other biological ontologies making reference to mutations also exist, such as the Sequence Ontology (for a recent assessment of the state and issues in incorporating mutation information in SO see [28]). However, a specific ontology able to represent or support representation of gene variation data is not available yet.

Even more relevant, a specific framework for identifying variations is missing. HGVS nomenclature defines mutations in relation to a specific version of RefSeq, which leaves the reconciliation of mutations described with reference to different RefSeq versions problematic. In a LOD framework, this is a key issue as having common URIs for the same mutations is a key for the integration of different datasets.

Furthermore, the definition of equivalent mutation relies on an abstraction which is based on sequence similarity. As such, it is not easily deducible by using common inference mechanisms which are based on Semantic Web technologies and tools (e.g.: a cluster of sequences may de facto inform a class which is characterized by the related consensus sequence).

Solutions which incorporate services in the LOD, e.g. SADI, may provide a unified framework where ontology languages and sequence alignment services could be used to compute the equivalence of mutations.

Aim of this work

In this paper, we cope with issues related to the integration of mutation data in the Linked Open Data infrastructure. We present the development of a mapping between a relational version of the IARC TP53 Mutation database (IARCDB) to RDF that takes into account HGVS recommendations as well as existing ontologies for the representation of this domain knowledge. A first implementation of servers publishing this data in RDF with the aim of studying issues related to the integration of mutation data in the Linked Open Data cloud is also presented.

Software infrastructure

Linked Data is an approach to publish information on the web which eases the integration of data from different sources by relying both on RDF as a lingua franca for a machine processable representation of information and on shared ontologies in order to allow the information from different resources to be semantically connected to each other. RDF itself however does not provide domain-specific terms. These may be identified by adopting shared taxonomies, vocabularies, and ontologies. Suitable terms in existing ontologies should of course be reused whenever possible: new terms should only be added when a viable term does not exist. RDF properties must then be mapped to external ontologies, while resources (objects and subjects in RDF triples) must be linked to LOD repositories by using shared identifiers, and comments and definitions must be added whenever possible.

By using dereferenceable URIs, i.e. URIs for which it is possible to get information about the referenced resource on the Web, as global identifiers for resources, linked data makes it possible to set hyperlinks between entities in different data sources. Such links are the glue connecting data islands of the Web of Data into a global, interconnected data space.

In our case, automatic RDB to RDF mappings were first created by using D2RQ, a platform for treating relational databases as virtual RDF graphs [29]. This tool allows on-the-fly generation of RDF triples from a database. It also allows browsing the generated RDF triples through a standard web interface and querying the relational database through a SPARQL endpoint.

The query performance of D2RQ is lower than that which can be achieved by using a devoted RDF triple store. In order to evaluate reliability and performances of an on-the-fly mapping system, such as D2RQ, compared to a native RDF framework, a dump of all triples generated by D2RQ was loaded into a dedicated Jena TDB triple store [30], a SPARQL Database for Jena [31] that provides for large scale storage and querying of RDF datasets. Many triple stores exist which varies in features and performance. We have opted for Jena as it was providing sufficient performance and, at the same time, a good set of integrated and interoperable tools which was ideal for our prototype lead development approach.

We have then enriched our dataset by adding triples connecting samples to related UMLS concepts. This mapping was implemented by means of a SPARQL Update federated query interconnecting our dataset with the Linked Life Data (LLD) endpoint.

Once the RDF dataset was created, it was made accessible as a SPARQL endpoint using Joseki [32], a Jena tool that provides support for SPARQL queries through an HTTP engine. Joseki was configured to connect to the TDB database and it was connected to an implementation of SPARQLer [31], a user friendly interface to a SPARQL server. Finally, we exposed the content of our TDB triple store as a Linked Data interface using Pubby, a well known Linked Data frontend for SPARQL endpoints [33].

Datasets

The IARC TP53 Database has been maintained at the International Agency for Research on Cancer (IARC) in Lyon, France, since 1994 [34]. The database compiles all TP53 mutations that have been reported in the published literature since 1989 [35]. It includes annotations on functional impact of mutations, either predicted or experimentally assessed, clinico-pathologic characteristics of tumors and demographic and life-style information on patients.

Various datasets, corresponding to different views of the available data, are made available to interested users as spreadsheets for download. The relational schema, however, is not public. On-line queries are meant to allow for data analysis only, answering to such questions as "Search for TP53 mutation prevalence in selected types of tumor and populations", and "Display a histogram showing the distribution of tumors associated with the selected germline or somatic mutation(s)".

For many years, in the sphere of a collaboration between IARC and the National Cancer Research Institute of Genova, datasets have been implemented in a relational databases management system at IST as a basis for an SRS implementation of the IARC TP53 Mutation Database [36, 37].

Development of mappings

The first mapping file was produced automatically by D2RQ. It was then manually refined to improve its commitment to a shared representation and, thus, to encode in the mapping some semantics that is not expressed in the RDB schema.

For instance, D2RQ generates predicate names which are based on the RDB column names: there is no way to know when a predicate refers to a property for which a shared representation exists. By customizing predicates we have been able to better represent the semantics of our data, according to shared ontologies.

Only a limited set of external ontologies and terminologies have been taken into account. These include the NCI Thesaurus (NCIT) [38, 39] for medical terminology (namely topography and morphology), the Bibliographic Ontology (BIBO) [40] and the BibTeX definition in Web Ontology Language (OWL) [41] for bibliographic references, the Diseasome ontology [42, 43] and MIO [25].

Moreover, external links were set to LOD implementations of DBpedia, a system including all structured information which is present in Wikipedia pages [44], PubMed, the Human Genome Nomenclature Committee (HGNC) database [45], the On-line Mendelian Inheritance in Man (OMIM) system, UniProt, and the Unified Medical Language System (UMLS). All links were defined to the Bio2RDF entry points of these databases, expressed by using the unified Bio2RDF URI style, with the exception of DBpedia, that was linked through its own namespace, and UMLS, that was connected through LinkedLifeData [46].

We also made reference to some other frequently used vocabularies such as rdf:, rdfs:, and owl:. Where shared relations were not available to express the content of our database, we have used ad-hoc defined properties. The re-use of ontologies is not limited to relations. The majority of "values" in the IARC database comes from controlled vocabularies and reference dictionaries and therefore they are de facto ontology terms. We have mapped these terms to classes from ontologies (or terminologies) such as UMLS.

The Additional file 1 includes the complete mapping.

A prototype implementation

We have implemented a D2RQ Server for TP53 mutation data as a prototype for studying issues related to the publication of mutation data on the LOD framework. It provides data on a significant subset of the IARC TP53 database, including gene variations, somatic mutations, and related bibliographic references. In order to minimize duplications, information on samples and individuals have been made available separately from the related mutations.

In table 2, we present a summary of published classes and the correspondence with the original datasets. In Figure 1, the schematic representation of classes that were created by the mapping, of their relationships, and of external links is presented.

Class	Description	Linked to	IARC datasets
database	Database information	somatic_mutation	Implicit
gene	Gene information	gene_variation	Implicit
gene_variation	Detailed description of the mutation	gene, somatic_mutation	Gene variations
somatic_mutation	Summary mutation data, linked to bibliography, sample, and variation data	database, sample, gene_variation, somatic_ref	Somatic mutations
sample	Tumor topography, morphology, origin, and classification	somatic_mutation, individual	Somatic mutations
individual	Demographic details, life-style data and genetics of the donor	sample	Somatic mutations
somatic_ref	Bibliographic references where mutations are described	somatic_mutation	Somatic references

A summary of published classes and the correspondence with the original datasets.

In Figure 2, the architecture of the overall system is shown. In brief, the original relational database is translated in RDF and exposed using the D2RQ framework. A RDF dump is stored into a TDB triple store that can be queried through a Joseki SPARQL endpoint either directly (through some SPARQL client) or by means of the Pubby interface that in turn may be queried by both HTML and RDF browsers.

Providing access to the information

As previously reported, the Pubby server gives access to the information through various interfaces. It allows browsing the RDF graph starting from any page: further navigation of the graph is achieved by internal links. E.g., one can select a defined somatic mutation (somatic_mutations/10000) and see links to related gene variation (variations/1579), sample (samples/9557), and bibliographic reference (somatic_refs/1065), along with some proper attributes, like the mutation (c.426T > A) or structural motif (NDBL/beta-sheets). See Figure 3.

Of course, this way of browsing the RDF graph allows to reach further pages. E.g., in the previous case, from a single mutation one can reach the triples associated to the related bibliographic reference and thus have a list of all mutations that were described in the same paper. In Figure 4 all properties and objects associated to the bibliographic reference denoted by somatic_refs/1065 are shown. In this figure, links to all somatic mutations described in the paper are presented. Moreover, two links to Pubmed are shown: the first one refers to its implementation in Bio2RDF and allows to extend the navigation of the virtual RDF graph externally from our implementation, while the second one relates to the interface that is usually accessed by researchers at NCBI.

More precisely, Pubby is used to add a Linked Data interface to our SPARQL endpoint. It handles external requests by connecting to the SPARQL endpoint, issuing a SPARQL "DESCRIBE" query about the requested URI, and showing the result in a HTML or RDF page, supporting Linked Data compliant content resolution and negotiation procedures.

An additional interface to the SPARQL endpoint is provided via SPARQLer, a simple interface to perform SPARQL queries. In this case, all prefixes that are needed to properly identify RDF nodes are added by the system, so that the compilation of the query is simplified.

Moreover, the RDF data set that we implemented can be explored by using any Semantic Web browser or application, like Marbles [48]. The SPARQL endpoint can also be queried by using some more sophisticated tool, such as RelFinder [49].

Some example queries

The first query is a simple example involving only the TP53 mutation data. It selects neoplasm-gene variation associations along with the number of their occurrences in the dataset. This query is essentially equivalent to a standard SQL query in the database and shows how similar queries may be performed in order to achieve the same functionalities of a relational database (see Table 3).

The second and third queries show how to perform federated SPARQL queries across distinct datasets. Query federation is expressed by means of the SERVICE keyword in a SPARQL query. This keyword supports the execution of the query on distributed SPARQL endpoints: it causes a sub-pattern of the query to be sent to a named endpoint, instead of being matched on the local dataset.

The second query demonstrates how to retrieve complementary data from DBpedia. Capital towns of countries included in our dataset are retrieved for individuals whose samples were used for the detection of somatic mutations which are present in the mutation dataset (see Table 4).

The last example query shows how to select information from the Linked Clinical Trials (LinkedCT) dataset which is available via the LinkedLifeData endpoint. Given a defined sub topography (precise anatomical location of the origin of the sample) that is put in relation with clinical trials of interest through the shared adoption of the corresponding ULMS code for inclusion criteria in the trial, information on gene variation, neoplasm and clinical trial ID is shown (see Table 5).

Some statistics on contents of the triple store

External URIs that specify either Linked Life Data, Bio2RDF or DBpedia entities are included in about 5% of triples. The number of triples including a shared property, i.e. a property that is defined within a re-used ontology, are about one third of the total. It may be noteworthy that there are 9,399 triples including one property from MIO and 146,553 triples with one property from NCIT.

Availability

Presently, we offer access to our dataset via two distinct modalities. The first interface is a SPARQL endpoint available at: http://bioinformatics.istge.it/logvdsparql/sparql. It is implemented via Joseki and TDB. Interfaces to validators for SPARQL queries and for RDF data are also available. The second interface is a Linked Data representation available at [51]. It is based on the Pubby frontend.