=Paper=
{{Paper
|id=None
|storemode=property
|title=Deploying the mutation impact mining pipeline with SADI: An exploratory case study
|pdfUrl=https://ceur-ws.org/Vol-645/Paper5.pdf
|volume=Vol-645
}}
==Deploying the mutation impact mining pipeline with SADI: An exploratory case study==
https://ceur-ws.org/Vol-645/Paper5.pdf
Deploying the Mutation Impact mining pipeline with SADI:
an exploratory case study
Alexandre Riazanov, Jonas Bergman Laurila and Christopher J O Baker∗
Department of Computer Science & Applied Statistics, University of New Brunswick, Saint John, New Brunswick, E2L 4L5, Canada.
Email: Alexandre Riazanov - alexr@unb.ca; Jonas Bergman Laurila - j02h9@unb.ca; Christopher J O Baker∗ - bakerc@unb.ca;
∗ Corresponding author
Abstract
Our previous work on text mining for mutation impacts resulted in (i) the development of a GATE-based
pipeline that mines texts for information about impacts of mutations on proteins, (ii) the population of this
information into our OWL DL mutation impact ontology, and (iii) establishing an experimental OWL
database for storing the results of text mining. The current focus of the project is to look for ways of
deploying our software and data to facilitate the integration of our mutation impact data in a broader
biological context.
This paper explores the possibility of using the SADI framework as a medium for publishing our mutation
impact software and data. SADI is a set of conventions for creating web services with semantic
descriptions that facilitate automatic discovery and orchestration. Here we describe a case study exploring
and demonstrating the utility of the SADI approach in our context. We describe several SADI services we
created based on our text mining API and data, and demonstrate how they can be used in a number of
biologicaly meaningful scenarios through a SPARQL interface (SHARE) to SADI services. In all cases we
pay special attention to the integration of mutation impact services with external SADI services providing
information about related biological entities, such as proteins, pathways, and drugs.
1
Introduction.
The annotation of mutants with their consequences is central task for researchers investigating the role of genetic
changes on biological systems and organisms. These annotations facilitate the reuse and re-interpretation of
mutations and are necessary for the establishment of a comprehensive understanding of genetic mechanisms,
biological processes and the resulting mutant phenotypes. As a result there are numerous mutation databases, albeit
perpetually out of date and often with a latency of many years. Automated mutation extraction systems based on text
mining techniques can identify and deliver mutation annotations for database curators to review. In this paper we
outline the publication of a mutation impact extraction system in the form of semantic web services, and their
integration with other semantically described bioinformatics services, based on the SADI framework.
In our previous work we developed the Mutation Impact pipeline [1] – a program, based on a GATE [2] pipeline, that
makes it possible to extract mutation impacts on protein properties from texts, categorising the directionality of
impacts as positive, negative or neutral. Moreover, the system grounds mentions of proteins and mutations to their
respective UniProt identifiers and protein properties described in the Gene Ontology.
For example, consider these two excerpts from [3]: “The haloalkane dehalogenase from the nitrogen-fixing hydrogen
bacterium Xanthobacter autotrophicus GJ10 (DhlA) prefers 1,2-dichloroethane (DCE) as substrate and converts it to
2-chloroethanol and chloride” and “DhlA shows only a small decrease in activity when Trp-125 is replaced with
phenylalanine”. Our pipeline (i) identified “haloalkane dehalogenase” as a protein, (ii) mapped it to the UniProt ID
P22643 by grounding it to the identified organism “Xanthobacter autotrophicus”, (iii) identified “Trp-125 is replaced
with phenylalanine” as the point mutation W125F, (iv) identified “activity” as a protein property (GO 00188786 in
the Gene Ontology, and (v) identified “decrease” as the direction of the impact of the mutation on the protein
property.
Until now the Mutation Impact pipeline has been deployed as a simple Java API and could only be used
programmatically. When the pipeline is executed on a document, it computes a sequence of Java objects representing
mutation specifications. Every such object contains information about a series of elementary mutations, the
corresponding wildtype and mutant proteins, and the discovered impacts of the mutations. The Java class
representing impacts contains the direction of an impact and the type of the protein property being affected.
The practical use of the system and its results in this form is limited, because it requires programming. Recently
in [1] we also explored the possibility of using semantic technologies for exporting the system outputs according to a
domain specific knowledge representation. Now our system, like [4], delivers its results in the form of an OWL
ABox, i.e., as a collection of logical statements characterising the extracted mutations, proteins and impacts. The
classes and property predicates in these statements are defined in our Mutation Impact ontology [5] in OWL, based
2
on the earlier mutation ontology from [6]. Figure 1 (borrowed from [1]) shows the top level concepts of the ontology
with some relations between them.
This semantic representation of text mining results already provides a great deal of flexibility – the results can be
used with any toolsets that work with OWL. The most straightforward way of using semantically described data is by
querying it directly, so we established an OWL database, using Sesame [7], that stores the results of mining different
documents. Our users can query the populated OWL database via a SPARQL [8] endpoint [9]. Figure 2 shows an
example of a query for proteins mutated with a specified impact.
As we anticipate a multitude of data reuse cases, the provision of a SPARQL endpoint as the sole data access form
may not be sufficient. Consequently, we are looking for additional ways to provide access to the data. Our primary
requirement is that the framework should support integration with other software and data for proteins, mutations,
impacts and related biological entities, such as pathways, and drugs. This criterion is critical because the mutation
impact mining results alone have limited reusability.
In this paper we review the SADI framework [10] as a candidate platform for providing interoperable access to our
semantically exposed Mutation Impact data. The choice is based on the robust integrative features displayed by SADI
services, discussed in the next section. This paper describes an exploratory case study using four biologically
meaningful queries that require (i) some data from our Mutation Impact DB, as well as (ii) some biological
knowledge from external sources. Furthermore, we test the queries using the SHARE client [11] which is designed to
automatically discover and combine the required SADI services.
What is SADI?
The SADI framework [10] is a set of conventions for creating Semantic Web Services that can be automatically
discovered and orchestrated. A SADI-compliant service consumes a whole RDF model as input and produces an
RDF model as output. An input RDF model has some URI node designated as the central input node, and the whole
input model is considered a description of the central node. Exactly the same node is always present in the output
model as the central output node. The sole function of a SADI service is to annotate this node with new properties
and assert these properties in the output model, in contrast with more conventional Web services that usually compute
output without an explicit connection to the input.
The most important feature of SADI is that the predicates for these property assertions are fixed for each service. A
declaration of these predicates, available online, constitutes a semantic description of the service. For example, if a
service is declared with the predicate myontology:isT argetOf Drug described in an ontology as a property linking
proteins to drugs, the user knows that he can use the service to search for drugs targeting a given protein.
3
The declaration of the service predicates is done by specifying an OWL class for the output nodes. If this output class
entails an existential restriction for some property, it means that the property is declared as produced by the service
and the corresponding output data may be available from the service.
Another part of a service declaration is the input (OWL) class that imposes restrictions on the kind of input nodes the
service can process. In particular, if this class subsumes an intersection of property restrictions, a well-behaved
service will look for the corresponding properties attached to an input node, and use the values as parts of the input.
As an example, consider the SADI service [12] computing the Body Mass Index. Its InputClass is defined as the
intersection of ∃ mged:has height.mged:M easurement (mged abbreviates [13]) and
∃ mged:has mass.mged:M easurement, so the service expects these two properties attached to an input node. The
service’s OutputClass is a subclass of ∃ bmi:BM I.xsd:int, so the service provides the predicate bmi:BM I (bmi
corresponds to the service’s own ontology that describes the input and output classes). Given the following RDF as
input
mged:has_height ;
mged:has_mass .
mged:has_value "1.7"ˆˆxsd:float ;
mged:has_units mged:m.
mged:has_value "85"ˆˆxsd:float ;
mged:has_units mged:kg .
the service generates this RDF as output:
bmi:BMI "29.4"ˆˆxsd:float .
The declaration of the input and output classes of a SADI service constitutes a semantic description of the service.
Importantly, such semantic descriptions allow completely automatic discovery and composition of SADI services
(see, e.g., [10, 11]). In practice, using SADI services to provide access to the Mutation Pipeline and DB will allow
automatic integration with hundreds of external resources dealing with mutations, proteins and related biomedical
entities, e.g., pathways and drugs, so long as they are registered with a SADI registry. These are desirable features of
SADI motivating us to deploy our mutation impact services with this framework.
SADI services for Mutation Impact pipeline and data.
As an initial implementation with SADI, we created a service that takes a reference to a text, and outputs the property
assertions derived from the input text, such as links from the text URI to the identified grounded mutations. Note that
those grounded mutations also have links to ungrounded mutations, proteins and impacts. This SADI service can be
4
mostly useful in combination with services that find documents, as well as for users just wishing to use our pipeline
remotely (with no installation effort). In fact, we currently use this service ourselves to populate the Mutation Impact
DB with OWL ABox assertions, because it has the capability of converting the raw results of the Mutation Impact
pipeline to OWL.
All other our SADI services essentially wrap some ad hoc queries to our Mutation Impact DB. For example, one of
the most intensively used services – getM utationByW ildtypeP rotein – finds all instances of the Mutation Impact
ontology class M utationSpecif ication, given the UniProt ID of a protein that acts as the wildtype protein in those
mutations. The class M utationSpecif ication is central to the ontology and the DB: its instances represent
grounded mentions of mutations and are linked to the corresponding wildtype and mutant proteins, the mutation
impacts, and also the texts from which the mutation mentions were extracted. So, two other services also find
M utationSpecif ication instances by their mutant proteins and mutation impacts.
Two other services retrieve instances of biological entities of specified types, present in our DB. The service
getM IDBBioEntityByT ype does this for the top level biological entity classes in our ontology, such as P rotein
or P ointM utation. The service getP roteinP ropertyByT ype specialises in protein property types, most of which
are currently inherited from the Gene Ontology. Given a subclass of P roteinP roperty, e.g., GO 0018786
(’haloalkane dehalogenase activity’) from the Gene Ontology, it finds all known properties of this type, grounded to
specific proteins.
There are currently two auxilliary services: getM utationImpactByP roteinP roperty finds mutation impact
instances linked to a specified protein property grounded to a specific protein, and getM utationSubseries finds
series of elementary mutations identified in a text, that are subsets of a specified set of elementary mutations.
The list of all SADI services based on the Mutation Impact pipeline and DB, can be found in [14] and is also
summarised in the following table:
service operation
mineT extF orM utationImpacts extracts mutation specifications from a document
getM utationByW ildtypeP rotein finds specifications of mutations grounded to a given protein
getM utationByM utantP rotein finds specifications of mutations resulting in a specified protein
getM utationImpactByP roteinP roperty finds mutation impact instances affecting a specified grounded
protein property
getM utationByImpact finds mutation specifications corresponding to an impact on
a specified grounded protein property
getM utationSubseries finds mutation series instances that are subseries of a given
mutation series
getM IDBBioEntityByT ype finds biological entities by their type URIs
getP roteinP ropertyByT ype finds protein properties grounded to specific proteins by their
type URIs
All the services are also registered with the central SADI registry [15].
5
Use cases.
Here we introduce the use cases we have adopted to test the suitability of SADI as a medium for providing access to
out Mutation Impact data. All our use cases are in the form of queries, i.e., the user is seeking some information from
our Mutation Impact DB in combination with external resources.
Use case 1: Find all mutations and the structure images of wild type proteins that were mutated, where the
impact of the mutation is an enhanced haloalkane dehalogenase activity. In this use case we aim to address the
needs of a protein engineer who is seeking to understand what mutational changes can enhance the catalytic activity
of an industrial enzyme, which is haloalkane dehalogenase in this scenario. The medium for reviewing the causal
relationship of mutations on protein activity is a protein structure image which can be annotated with mutations and
their impacts retrieved from a database/triplestore [16] or extracted automatically from documents using text mining
techniques [4, 17]. In our use case, we perform retrieval of the specific protein structures where there are published
reports of mutations having a positive impact on catalytic activity. The user would wish to retrieve and review these
strucutres along with mutation locations and impact annotations. The expected output of the integrated SADI
services is the selected protein structure files and the corresponding mutations.
Use case 2: Find all pathways, together with the corresponding pathway images, that might have been altered
by a mutation of the protein Fibroblast growth factor receptor 3. In this scenario we address the needs of a
systems biologist who is seeking to understand the likely impact of reported mutations on signalling or metabolic
pathways [18] in which the mutated protein participates. This entails the retrieval of pathway information for the
mutated proteins, which can be provided as a pathway diagram also. In the current use case we deal with mutations to
the protein Fibroblast growth factor receptor 3 reported in scientific papers which impact the protein either positively
or negatively.
Use case 3: Find all drugs related to mutated proteins, together with their interaction partners, where the
mutation impact is a decreased carbonic anhydrase activity. In this use case we address a query that a researcher
in drug discovery would make when looking for existing drugs targeting a new disease condition. In the case of
Carbonic anhydrase, an enzyme involved in the acid-base balance of blood (via the interconverion of carbon dioxide
and bicarbonate), enzyme inhibitors such as acetazolamide cause mild metabolic acidosis. This can be beneficial to
patients with severe chronic obstructive pulmonary disease (COPD) with chronic hypercapnic ventilatory failure who
need a reduction in arterial carbon dioxide and a rise in arterial oxygen and the transport carbon dioxide out of
tissues. The query will help us to identify the names of known drugs targeting the enzyme and what experimental
modifications on the protein have resulted in lowering its activity in situ. Moreover, the query will also retrieve the
names of proteins that interact with the enzyme directly through protein-protein interactions.
6
Use case 4: From the literature, find all reported mutations of the protein with the nsSNP rs2305178. In this
use case, a researcher in genomics asks for all known mutations reported in the literature for a protein containing a
non-synonymous SNP. Here the researcher is primarily looking for any literature describing impacts of a nsSNP on a
protein. By retrieving all known mutations for the protein in which the nsSNP is reported, the researcher can find out
if any of these reported mutations corresponds to the location of the SNP in question. Minimally the researcher can
retrieve the full set of mutations to the protein based on reported experimental analysis and their impacts, together
with references to the supporting literature.
Experiments with SHARE.
SHARE [11] is an experimental client featuring automatic discovery and orchestration of SADI services. From the
user point of view, SHARE is a SPARQL engine that computes queries by picking and calling suitable SADI services
from some registry. In a typical scenario, the user first looks up property predicates he needs for his query, in the list
of predicates declared as provided by SADI services in a registry, and also related classes and properties in the
referenced ontology. Then he uses the available concepts to form a regular SPARQL query, and sends it to a SHARE
endpoint. Importantly, the SHARE engine decides itself which services have to be invoked and in what order, to
execute the query. Note that the user deals only with an almost declarative query, i.e., he only needs to understand the
semantics of the URIs being used in the query, although knowing the services providing the predicates can be
beneficial. This situation suits our purposes well, so, for our experiments with SADI services for Mutation Impact
data we are using the Web interface for SHARE [19].
Note that in the query examples below we omit prefix declarations – the information can be found in the
corresponding URL references [20–27]. We also omit FROM clauses instructing SHARE to use our ontology for
processing the queries, as well as FROM clauses importing RDF file [28] qualifying some individuals, e.g.,
go:GO 0018786, as instances of the corresponding classes, e.g., mioe:P roteinP ropertyT ype. Full versions of the
queries are available from [29].
Our use case 1 (“Find all mutations and the structure images of wild type proteins that were mutated, where the
impact of the mutation is an enhanced haloalkane dehalogenase activity”) can be realised with the following
SPARQL query:
7
1 SELECT DISTINCT ?StructImage ?NormalizedMutation
2 WHERE {
3 ?Property mioe:proteinPropertyHasType go:GO_0018786 .
4 ?Impact mio:affectProperty ?Property .
5 ?Impact mio:hasDirection mio:Positive .
6 ?MutationSpec mio:specifiesImpact ?Impact .
7 ?MutationSpec mio:groundMutationsTo ?Protein .
8 ?MutationSeries mio:mutationSeriesIsSpecifiedBy ?MutationSpec .
9 ?MutationSeries mio:containsElementaryMutation ?Mutation .
10 ?Mutation mio:hasNormalizedForm ?NormalizedMutation .
11 ?Protein pred:has3DStructure ?Struct .
12 ?Struct obj:hasJmol3DStructureVisualization ?StructImage . }
Let us analyse how we construct this query. The predicate mioe:proteinP ropertyHasT ype in our ontology links
grounded protein properties with their types, so we can use it to enumerate known instances of GO 0018786. In lines
7-8, mio:af f ectP roperty links the grounded protein properties to the corresponding instances of mutation impacts
and mio:hasDirection selects only positive impacts. Using mio:specif iesImpact, we can select instances of
mutation specifications (line 9), which in turn link to the corresponding wildtype proteins (line 10) and series of
elementary mutations (line 11). We would like to see readable IDs of elementary mutations in the output, like D124N
or V226A, so we use mio:containsElementaryM utation to retrieve the corresponding elementary mutations and
mio:hasN ormalizedF orm to map them to the corresponding IDs.
So far we have used only predicates from our Mutation Impact ontology. Since the essense of the use case 1 is
visualisation, we look for predicates in SADI-related ontologies, that could link proteins to their images. There is no
direct link, but we can use the composition of pred:has3DStructure and
obj:hasJmol3DStructureV isualization to first retrieve the PDB ID of a protein, and then find the corresponding
graphics file.
SHARE was able to compute our query using three of our SADI services and two third party SADI services from the
registry, providing pred:has3DStructure and obj:hasJmol3DStructureV isualization. However, this was
completely transparent to us as the end users. We only dealt with an almost completely declarative query composed
of predicates we were able to find in ontologies. The only thing we need to know beyond the semantics of a predicate
is the direction in which available services compute it: e.g., we cannot use pred:has3DStructure to get from a PDB
ID to the corresponding protein because there is currently no service that would annotate a PDB ID with the inverse
of pred:has3DStructure. Finding the services, their invocation and some deduction with the ontological definitions
of predicates, was done by SHARE completely automatically. Note especially the ease with which integrating our
mutation-related information with the external sources of data was achieved.
The work required by use case 2 (“Find all pathways, together with the corresponding pathway images, that might
have been altered by a mutation of the protein Fibroblast growth factor receptor 3“) can also be divided into two
8
parts: the first part can be done using the predicates from our ontology, and the second part has to be delegated to
external resources, dealing with genes, pathways and pathway visualisation. Since we know that the wildtype protein
is Fibroblast growth factor receptor 3 (UniProt ID P22607), we can easily retrieve the mutation specifications linked
to this protein with the property mio:groundM utationsT o. These instances will have impacts attached to them
with mio:specif iesImpact, and we can specify the interesting impact directions with mio:hasDirection.
Using pred:isEncodedBy we also map the protein to the corresponding gene, and ont:isP articipantIn allows to
retrieve the pathways in which the protein participates, pred:visualizedByP athwayDiagram will fetch the
corresponding graphics file URL. The resulting query is as follows:
SELECT DISTINCT ?Pathway ?PathwayDiagram
WHERE {
?MutationSpecification mio:groundMutationsTo uniprot:P22607 .
?MutationSpecification mio:specifiesImpact ?Impact .
{?Impact mio:hasDirection mio:Positive}
UNION {?Impact mio:hasDirection mio:Negative} .
uniprot:P22607 pred:isEncodedBy ?Gene .
?Gene dmt:isParticipantIn ?Pathway .
?Pathway pred:visualizedByPathwayDiagram ?PathwayDiagram }
SHARE evaluated the query using our service that links proteins to mutations specifications, and two external SADI
services providing ont:isP articipantIn and pred:visualizedByP athwayDiagram, and found five pathways
with diagrams.
Use case 3 (“Find all drugs related to mutated proteins, together with their interaction partners, where the mutation
impact is a decreased carbonic anhydrase activity”) is somewhat similar to use case 1: given the protein property
type, we retrieve the grounded properties, positive impacts and the wildtype proteins with the help of some predicates
from our ontology. The conection from the proteins to drug names is realised with the predicates
obj:isT argetOf Drug and obj:hasDrugGenericN ame. Separately, we find the interacting proteins with
pred:hasM olecularInteractionW ith. The resulting query is
SELECT ?DrugName ?InteractingProtein
WHERE {
?Property mioe:proteinPropertyHasType go:GO_0008270 .
?Impact mio:affectProperty ?Property .
?Impact mio:hasDirection mio:Negative .
?MutationSpecification mio:specifiesImpact ?Impact .
?MutationSpecification mio:groundMutationsTo ?Protein .
?Protein obj:isTargetOfDrug ?Drug .
?Drug obj:hasDrugGenericName ?DrugName .
?Protein pred:hasMolecularInteractionWith ?InteractingProtein }
Finally, the most difficult use case 4 (“From the literature find all reported mutations of the protein with the nsSNP
rs2305178”) was implemented with the following query:
9
1 SELECT DISTINCT ?NormalizedMutation ?DocumentURL
2 WHERE {
3 dbsnp:rs2305178 obj:correspondsToEntrezGene ?EzGene .
4 ?Protein mioe:biologicalEntityHasType mio:Protein .
5 ?Protein pred:isEncodedBy ?KeggGene .
6 ?KeggGene obj:hasRefSeqTranscript ?RefSeq .
7 ?RefSeq obj:correspondsToEntrezGene ?EzGene .
8 ?MutationSpecification mio:groundMutationsTo ?Protein .
9 ?MutationSeries mio:mutationSeriesIsSpecifiedBy ?MutationSpecification .
10 ?MutationSeries mio:containsElementaryMutation ?Mutation .
11 ?Mutation mio:hasNormalizedForm ?NormalizedMutation .
12 ?Document foaf:topic ?MutationSpecification .
13 ?Document rss:link ?DocumentURL }
The property obj:correspondsT oEntrezGene (line 3) maps the specified dbSNP ID to an Entrez gene ID. If we
were dealing with completely declarative queries, it would be enough to use a composition of the predicates
obj:correspondsT oEntrezGene, obj:hasRef SeqT ranscript and pred:isEncodedBy, as in lines 5-7, to map the
Entrez gene ID to a protein. However, no SADI service currently provides the inverses to the first two predicates, so
the composition can only work in the direction from proteins to Entrez gene IDs. To do so, we had to implement a
service that enumerates all proteins known in our DB. In fact, it is more general – it enumerates instances of several
main biological entity classes from our ontology, such as M utationImpact or P ointM utation. The service
provides the inverse of mioe:biologicalEntityHasT ype whose use is demonstrated in line 4. Linking the protein to
elementary mutations is done exactly the same way as in use case 1. Finally, the last two lines in the query serve to
retrieve the URLs of the documents from which the corresponding mutation specifications were extracted.
Conclusions and future work.
The primary goal of our case study was to explore the suitability of the SADI framework as a medium to faciliate
data sharing and integration across biological data types. We have identified that SADI provides an effective way of
exposing our mutation impact data such that it can be leveraged by a variety of stakeholders in multiple use cases.
Our experience in deploying and regisrering mutation services in accordance with SADI specifications was positive,
albeit with some challenges. Specifically, we identified that advanced skills in knowledge engineering were required
to build semantic representations of the services. In addition, we note that formulating the queries based on the SADI
services still requires extensive search for predicates in the SADI-related ontologies. Clearly, the necessary
infrastructure for such search is yet to be built. We also did not yet fully explore the current capabilites of other SADI
clients, such as the plugins for Taverna [30] and Sentient Knowledge Explorer (see, e.g., [11]) for our use cases.
In future work we aim to extend the Mutation Impact DB with more data types related to mutation annotations
extracted from the literature, and create the corresponding SADI services facilitating integration with other
10
Bioinformatics data.
Acknowledgements.
This research was funded in part by the New Brunswick Innovation Foundation, New Brunswick, Canada; NSERC,
Discovery Grant Program, Canada; and the CANARIE NEP-2 Program (C-BRASS project). We also thank Luke
McCarthy for helping us with various SADI-related technical issues.
References
1. Laurilla J, Naderi N, Witte R, Riazanov A, Kouznetsov A, Baker CJO: Algorithms and semantic infrastructure for
mutation impact extraction and grounding. In ICOB2010 2010. [To appear.].
2. Cunningham H, Maynard D, Bontcheva K, Tablan V: GATE: A Framework And Graphical Development Environment
For Robust NLP Tools And Applications. In Proceedings of the 40th Anniversary Meeting of the Association for
Computational Linguistics (ACL’02) 2002.
3. Lau EY, Kahn K, Bash P, Bruice T: The importance of reactant positioning in enzyme catalysis: a hybrid quantum
mechanics/molecular mechanics study of a haloalkane dehalogenase. Proc. Natl. Acad. Sci. USA 2000, 97(18):9937–42.
4. Rajaraman K, Choo KH, Ranganathan S, Baker CJO: A Workflow for Mutation Extraction and Structure Annotation. J.
Bioinformatics and Computational Biology 2007, 5(6):1319–1337.
5. Mutation Impact Ontology. [http://unbsj.biordf.net/ontologies/mutation-impact-ontology.owl].
6. Witte R, Kappler T, Baker CJO: Enhanced semantic access to the protein engineering literature using ontologies
populated by text mining. Int J Bioinform Res Appl 2007, 3(3).
7. Broekstra J, Kampman A, van Harmelen F: Sesame: A Generic Architecture for Storing and Querying RDF and RDF
Schema. In The Semantic Web ISWC 2002 2002:54–68.
8. SPARQL Query Language for RDF, W3C Recommendation 15 January 2008.
[http://www.w3.org/TR/rdf-sparql-query/].
9. Mutation Impact RDF triplestore SPARQL endpoint.
[http://unbsj.biordf.net/openrdf-workbench/repositories/mutation-impact-db/query].
10. Wilkinson MD, Vandervalk BP, McCarthy EL: SADI Semantic Web Services – ’cause you can’t always GET what you
want! In APSCC 2009:13–18.
11. Vandervalk BP, McCarthy EL, Wilkinson M: SHARE: A Semantic Web Query Engine for Bioinformatics. In The
Semantic Web (ISWC 2009) 2009:367–369.
12. SADI service computing the body mass index. http:// sadiframework.org/ examples/ calculateBMI.
13. MGED ontology prefix, abbreviated as mged. http:// mged.sourceforge.net/ ontologies/ MGEDOntology.owl.
14. SADI services based on the Mutation Impact pipeline and DB. http:// unbsj.biordf.net/ mutation-impact.
15. Central SADI registry. [http://sadiframework.org/registry/services/].
16. Gabdoulline RR, Ulbrich S, Richter S, Wade RC: ProSAT2Protein Structure Annotation Server 2006.
17. Baker CJO, Witte R: Mutation Mining-A Prospector’s Tale. Information Systems Frontiers 2006, 8:47–57.
18. Bauer-Mehren A, Furlong LI, Rautschka M, Sanz F: From SNPs to pathways: integration of functional effect of sequence
variations on models of cell signalling pathways. BMC Bioinformatics 2009, 10(S-8):6.
19. Web interface for SHARE. [http://biordf.net/cardioSHARE/].
20. Our Mutation Impact ontology core URL prefix, abbreviated as mio.
http:// www.freewebs.com/ riazanov/ mutationOntology2010.owl\#.
21. Our Mutation Impact ontology extension URL prefix, abbreviated as mioe.
http:// www.freewebs.com/ riazanov/ mutationOntology2010\ extras.owl\#.
11
22. Gene Ontology prefix, abbreviated as go. http:// purl.org/ obo/ owl/ GO\#.
23. SADI service object ontology prefix, abbreviated as obj. http:// sadiframework.org/ ontologies/ service\ objects.owl\#.
24. SADI predicates ontology prefix, abbreviated as pred. http:// sadiframework.org/ ontologies/ predicates.owl\#.
25. Dumontier Lab ontology prefix, abbreviated as dmt. http:// ontology.dumontierlab.com/ .
26. BioRDF UniProt nomenclature prefix, abbreviated as uniprot. http:// biordf.net/ moby/ UniProt/ .
27. dbSNP nomenclature prefix, abbreviated as dbsnp. http:// lsrn.org/ dbSNP:.
28. RDF file containing descriptions for seed values in our queries. http:// dl.dropbox.com/ u/ 2483134/ input.rdf .
29. Full versions of the SPARQL queries presented in this paper.
[http://unbsj.biordf.net/mutation-impact/aimm2010 queries.html].
30. Withers D, Kawas E, McCarthy L, Vandervalk B, Wilkinson M: Workflow Construction in Taverna: The SADI and
BioMoby Plug-ins. In ISoLA 2010 (to appear).
Figures
Figure 1 : Mutation impact ontology structure.
Visualization of top level concepts as Mutation Specification, Protein, Mutation Impact and Protein Property being
connected through object properties.
Figure 2 : Sample SPARQL query to our Mutation Impact DB
A SPARQL query expressing the natural language question ”Which proteins have been mutated so that there is a
negative impact on haloalkane dehalogenase activity and what is the sequences of the corresponding mutants?” is
shown to the left. The first four answers (result rows) are displayed to the right.
12