=Paper=
{{Paper
|id=None
|storemode=property
|title=Ecotoxicology Data Federation with SADI Semantic Web Services
|pdfUrl=https://ceur-ws.org/Vol-952/paper_32.pdf
|volume=Vol-952
|dblpUrl=https://dblp.org/rec/conf/swat4ls/RiazanovHGMB12
}}
==Ecotoxicology Data Federation with SADI Semantic Web Services==
https://ceur-ws.org/Vol-952/paper_32.pdf
Ecotoxicology Data Federation with SADI
Semantic Web Services
Alexandre Riazanov1,6 and Matthew M. Hindle4,5 and E. Scott Goudreau2 and
Christopher J. Martyniuk2,3 and Christopher J. O. Baker1,6
1
Department of Computer Science & Applied Statistics, 2 Canadian Rivers Institute
and Department of Biology, University of New Brunswick, Saint John, NB, Canada,
3
Canada Research Chair in Molecular Ecology,
4
SynthSys, 5 School of Informatics, Edinburgh University, UK,
6
IPSNP Computing Inc, Canada,
1,2
{t969c,cmartyn,bakerc}@unb.ca, 4 matthew.hindle@ed.ac.uk,
6
alexandre.riazanov@ipsnp.com
Abstract. Biologists and biotechnologists need to draw information
from numerous distributed and heterogeneous resources, such as on-
line biomedical databases, nomenclatures and specialised bioinformatics
tools. These tasks can benefit significantly from semantic data federa-
tion with SADI Semantic Web services where multiple resources exposed
through SADI services are accessed as a single virtual SPARQL-queriable
database. We provide evidence in support of this premise by creating
and testing a kit of public SADI services for a number of bioinformatics
databases and programs, and by demonstrating how it can be used to
serve real information needs of ecotoxicology researchers, by using the
services to answer some model queries.
1 Introduction.
The future of semantic technologies depends on how quickly and
broadly they are adopted, which in turn depends on what value
these technologies deliver to end users. For semantic technology re-
searchers and engineers, this necessitates checking their ideas and
prototypes in real or realistic use scenarios driven by potential end
users. This paper presents the results of such “fieldwork” test-
ing the utility of a data federation approach based on Semantic
Web services for systems biology: we explore the use of SADI [27]
Web services for semantic querying of heterogeneous and distributed
biomedical data for the needs of ecotoxicology research.
The use cases we adopt to challenge the technology are provided by
a working biologist (the fourth author) and correspond to actual
research questions being investigated in the field of fish toxicology.
The goals of our study are twofold: first, we would like to demon-
strate that SADI can be practically useful for real-life biological
research, and secondly, our aim is that this paper will provide an
2
exemplar for SADI deployment in biological research settings that
can be followed by other SADI adopters, in bioinformatics and other
application areas where semantic data federation may be useful.
We also explore what can be done to further improve the utility of
SADI-based semantic querying.
We would like to emphasise that this paper is not an introduction
to SADI. In particular, we neither discuss the technical details of
how SADI services can be discovered and invoked, although a brief
overview will be given in Section 1.2, nor compare SADI to other
Semantic Web services and data federation frameworks. For this,
we refer the readers to [27, 25, 24].
1.1 Self-service Semantic Data Federation Vision
Many activities in biomedical research and biotech industry re-
quire finding and combining information from multiple heteroge-
neous and distributed resources. The scope of data requirements
for a biologist often includes many autonomous resources, such as
online biomedical databases, nomenclatures, ontologies, literature
and patent repositories, clinical databases and specialised analytical
Web services, such as biomolecular sequence search and alignment.
The state-of-the-art approaches to data integration – datawarehous-
ing and workflow scripting (see, e. g., [15] and [16])– are both limited
in scope and often unaffordable for academic research groups and
small biotech companies.
We are advocating the emerging data federation paradigm where
querying multiple heterogeneous distributed resources is as easy
as querying a single database. The dynamic nature of research and
biotech R&D activities implies that in many cases pre-programmed,
e. g., form-based, querying is not enough and ad hoc querying is nec-
essary. For ad hoc querying to be affordable in terms of labour it
has to be self-service, so that non-programmer users, such as biol-
ogists or clinical research professionals, can formulate and execute
queries without help from programmers. This may be possible if the
querying is semantic, i. e., based on the use of shared formalised
vocabularies and automatic application of knowledge written in the
form of ontological axioms or logical rules. If querying is semantic,
end users can formulate their queries in the terminology of their
domain, without knowing how the underlying data is structured or
specific mechanisms of access to the data. Our study contributes to
the research dedicated to the realisation of this vision.
1.2 SADI Semantic Web services
The SADI (Semantic Automatic Discovery and Integration) frame-
work [27] is a set of conventions. Simple HTTP-based Web services
that follow these conventions can be fully automatically discovered,
3
composed and called by client programs. The two main principles of
SADI are as follows.
First, SADI services can only consume input in the RDF format and
can only produce output in the RDF format. This completely re-
moves the problem of syntactic interoperability – any SADI service
can directly consume data produced by any other SADI service.
Second, every SADI service provides a special semantic description
that unambiguously defines what the service does, thus facilitating
the findability of the service by client programs when they need the
corresponding functionality. The description specifies what kind of
RDF graphs the service expects in the input and can process, in
terms of the concepts (properties and classes) that can be used in
the input RDF and, more importantly, specifies the concepts the
service can use to form the RDF graphs in the output.
The concepts used in descriptions of a set of SADI services, to-
gether with related concepts from the underlying ontologies, con-
stitute a federated schema and can be navigated by users, including
non-technical users that understand the corresponding domain ter-
minology, to form meaningful queries over the network of available
services. Such a query can be expressed, for example, in SPARQL
and executed by a special query engine that will find SADI services
providing relations that may be useful to satisfy the query, identify
the data that can be sent as input to these services, and invoke the
services to retrieve more data, and so on, until it has enough data
to answer the query. Such engines can also apply ontological axioms
or rules to facilitate simpler and more intuitive queries. Currently,
there are two prototypes that implement this functionality: open-
source SHARE [25, 24] and commercial Hydra [2].
A typical scenario for publishing a resource as a set of SADI ser-
vices is as follows: suppose, a user would like to access the Web site
ClinicalTrials.gov – a registry of clinical trials – via SADI services,
and wants to retrieve trials by disease names and extract informa-
tion about trials, such as the names of the drugs studied. The pub-
lisher can do this with two services: getTrialsByDiseaseName and
getDrugNameByClinicalTrial.
The first step of the resource publishing process is data modelling:
it is necessary to decide how the data will be represented in RDF
and, in particular, what relations and entities can be used. For
illustration purposes, we assume that we use classes Name, Disease,
ClinicalTrial and Drug, and properties diseaseIsTargetedByTrial,
trialStudiesDrug, hasName. In our data model, a disease name can
be linked to a trial, and a trial can be linked to the corresponding
drug name as in the following RDF (in Notation3 syntax):
:disease_name a :Name; :hasValue "Typhoid fever" .
:disease a :Disease;
:hasName :disease_name;
4
:diseaseIsTargetedByTrial :trial .
:trial a :Trial; :trialStudiesDrug :drug .
:drug a :Drug; :hasName :drug_name .
:drug_name a :Name; :hasValue "Ceftriaxone" .
It is generally preferable to reuse some existing ontologies defining
the required concepts, but when no such ontology is available, one
can introduce the concepts in a small service-specific ontology and
ensure that they have mnemonic names and/or descriptive textual
labels, so that their meaning is apparent to users.
The next step is to define the input and output of the services in
terms of the chosen entities and relations. The service
getTrialsByDiseaseName will accept Disease objects with hasName
attached to them, and in the output it will link the diseases to
ClinicalTrial objects identified with their URLs in the
ClinicalTrials.gov database via the property
diseaseIsTargetedByTrial. The service getDrugNameByClinicalTrial
will accept these ClinicalTrial URLs as input and link them via
trialStudiesDrug to Drug objects linked to the corresponding names
via hasName. In the schematic representation of the service descrip-
tions below, the dashed and solid lines correspond to input and
output specifications respectively.
getTrialsByDiseaseName
Disease ClinicalTrial
a a
hasName diseaseIsTargetedByTrial
string
getDrugNameByClinicalTrial
ClinicalTrial Drug
a a
trialStudiesDrug hasName
string
The SADI framework uses OWL syntax to express the semantic
descriptions. For example, the description of
getTrialsByDiseaseName consists of the following two class defini-
tions (in Protégé syntax): (Disease and (hasName some string))
as the service input class and (diseaseIsTargetedByTrial some
ClinicalTrial) as the output class.
5
1.3 Data integration requirements in Ecotoxicology
The discipline of ecotoxicology increasingly focuses on complex in-
teractions in biological systems which, on the technical side, of-
ten requires an integrated analysis – a systems view – of Omics
data of different types, such as proteomics and genomics data (see,
e. g., [18]). This requires multiple software tools and databases
supporting tasks such as microarray analysis (results of experimen-
tally measuring certain values, such as gene expression intensity) or
gene annotation (finding information about genes and correspond-
ing proteins). As a result, biologists are faced with a bewildering
array of disconnected bioinformatics resources. Drawing together
and mastering these tools and resources is frequently a frustrat-
ing technical exercise in identifying common links across database
records and connecting input and output formats of bioinformat-
ics tools. Interpreting experimental Omics data in the context of
the current available knowledge and methodologies from a single
query platform with explicit semantics would be a valuable asset to
ecotoxicologists. Emergence of such a framework would free toxi-
cologists from needlessly spending time on technical and semantic
idiosyncrasies, and enable the researchers to synthesize Omics infor-
mation to better predict risks associated with chemical exposures.
1.4 Study outline
There are several approaches to integrating biological data sets, and
some of them are based on Semantic Web. For example, projects
like Bio2RDF [8] and Linked Life Data [3] have used semantic tech-
nologies to build mash-ups of current biological information. How-
ever, many biological application cases also require the integration
of bioinformatics algorithms. Architectures where Semantic Web
services are used as components in complex bioinformatics analysis
pipelines, are an elegant solution to exposing knowledge, data and
algorithms in a semantically explicit framework.
In the study presented here, we consider a number of research ques-
tions from the field of toxicology that require data and algorithm
integration. To facilitate the integration of multiple resources which
are required for obtaining insights on these research questions, we
have created a kit of SADI services exposing these resources. In this
paper we describe how the relevant data is modelled, how the cor-
responding SADI services work and how they can be used via SADI
query engines to implement semantic federated querying of the re-
sources. We provide three example SPARQL queries that demon-
strate the potential utility of SADI-based semantic data federation
for biological research.
6
2 Target information needs in fish toxicology
and corresponding resources.
Fish toxicology is a sub-field of ecotoxicology that studies the re-
sponses of fish to exposure to various pollutants, such as fertilisers
and pesticides. Ultimately, such research is meant to facilitate the
preservation of fish populations, develop more efficient aquaculture
methods and also help to discover general biological mechanisms
that may transfer to other species, especially humans.
Typically, fish toxicologists want to know what sequences of molec-
ular events are triggered in fish after they are exposed to chemicals
of a certain type, and what organism functions and biological pro-
cesses are affected. More specific examples of research questions
might include “For all fish microarray studies, what complexes of
biochemical reactions are commonly affected?”, “Does the exposure
to chemical X affect the production of proteins with common struc-
tural properties?” or “What types of human diseases are known to be
related to the genes affected by pesticide Y in fish brain?”. Answer-
ing questions of this kind often depends on the ability of researchers
to analyse experiment results in the context of available biomedical
knowledge. Here we describe the main types of databases and algo-
rithmic resources that can be used in combination to get insights
on fish toxicology-related questions.
Microarray experiment repositories. The toxicity of many
chemicals is manifested by affecting the expression of genes: some
genes are up-regulated (produce more RNA than usual), others are
down-regulated (produce less RNA). Consequently, an important
technique for learning about the effects of a chemical on an organ-
ism is to measure the expression intensity for various genes by con-
ducting a DNA microarray experiment. Experiments of this kind
are also used for other tasks, such as measuring protein quanti-
ties in biological samples. Large numbers of results of microarray
experiments are deposited in public online databases, such as Ar-
rayExpress [12], in standartised flat-file or XML-based formats.
Sequence processing tools. Omics experiments often deal with
molecular sequences of different types, such as DNA and proteins,
so analysis of experimental data requires computation on sequences.
Two heavily used types of algorithms – BLAST [7] and HMMER3
[11] – allow searching for similar sequences in large sequence
databases, such as NCBI RefSeq [20] and Pfam [14], and aligning se-
quences. DNA sequences in microarrays, especially in fish microar-
rays, are often incomplete, contain missing gene fragments and start
at nucleotides that do not correspond to fragments that translate
into amino acid sequences. The OrfPredictor tool [19] helps to cope
with this problem by identifying Open Reading Frames (ORF) –
parts of DNA sequences that actually encode genes.
7
Gene Ontology (GO) and model organism databases. Mean-
ingful interpretation of gene expression experiments often requires
mapping genes to GO annotations specifying molecular functions of
the corresponding proteins or biological processes they are involved
in. Experimentally derived annotations of this kind are available
from a number of genomic databases for well-studied (“model”)
organisms, such as human protein annotations at EBI [1].
Miscellaneous resources. Various other biomedical resources may
be necessary for fish toxicology data analyses. For example, if a bi-
ologist needs to analyse data for a genus or a whole class, rather
than a species, he will have to use a taxonomy, such as the NCBI
Taxonomy [4], to enumerate all species names. Another example
of a popular resource is the UniProt database [6] containing infor-
mation about proteins, such as references to biochemical reactions
they participate in.
3 Data modelling.
This section describes how we model the data discussed in the previ-
ous section. Choosing appropriate ontological primitives and RDF
modelling patterns is crucial for convenient and flexible querying as
well as for the semantic interoperability of services.
Ontologies. In order to improve the re-usability of our SADI ser-
vices, wherever possible we reference existing upper and domain
ontologies. Table 1 lists the ontologies used by the SADI Web ser-
vices written for our experiment: The Semanticscience Integrated
Prefix URL
sio http://semanticscience.org/resource/
lsrn http://purl.ovclc.org/SADI/LSRN/
efo http://www.ebi.ac.uk/efo/
ncbi http://purl.org/obo/owl/NCBITaxon#
blastso http://cbakerlab.unbsj.ca:8080/BLAST-sadi-service-ontology.owl#
hmmrso http://cbakerlab.unbsj.ca:8080/fishtox/HMMR-sadi-service-ontology.owl#
goaso http://unbsj.biordf.net/fishtox/GOA-sadi-service-ontology.owl#
maso http://cbakerlab.unbsj.ca:8080/fishtox/arrayexpress-sadi-service-ontology.owl#
tsso http://cbakerlab.unbsj.ca:8080/fishtox/record-translation-sadi-service-ontology.owl#
stso http://cbakerlab.unbsj.ca:8080/fishtox/seq-tools-sadi-service-ontology.owl#
Table 1. Main ontologies used in our study
Ontology (SIO) is an upper ontology providing a broad set of gen-
eral classes and properties, and is used extensively by many SADI
8
services. The Life Science Resource Name (LSRN) ontology pro-
vides classes for records and identifiers from standard databases and
nomenclatures, such as lsrn:UniProt Record and lsrn:GO Identifier.
The Experimental Factor Ontology (EFO) [13] provides classes and
properties for describing gene expression experiments and is in-
tended to support querying over experimental data and data in-
tegration. We use EFO, in particular, to leverage interoperability
with the Gene Expression Atlas [12] where it is used extensively. We
also use an OWL version of the NCBI taxonomy to identify species,
genera, etc. Our service-specific ontologies (with the prefixes end-
ing with “so”) mainly contain input and output class definitions for
our SADI services.
Modelling patterns. The following figure shows a fragment of
the federated schema for our SADI services, which can be used to
design SPARQL queries. The predicates in brackets are the inverse
of the properties represented by the connecting arrows.
sio:SIO_000028 sio:SIO_000231 SIO_000132
sio:SIO_010000 has part efo:EFO_0000635 has input in efo:EFO_0002694 has participant
organism organism part experimental process
efo:EFO_0002698
0 0 629 u t) IO _0 00629
0 o :S Array
sio:S
IO_ (is ab sio
bject
of
b je ct of is su
is su SIO_000028
lsrn:ArrayExpressExperiment_Record lsrn:ArrayExpressPlatform_Record has part
sio:SIO_000008 sio:SIO_000008 sio:SIO_010018
has attribute has attribute DNA Sequence
8
lsrn:ArrayExpressExperiment_Identifier lsrn:ArrayExpressPlatform_Identifier 00 sio:SIO_010080
00 e is transcribed into
_0 but (is transcribed
9 O i
_00062 t) lsrn:UniProt_Record SI ttr
sio:SIO (is abou o: from)
ect of si s a
is subj ha
sio:SIO_010015 sio:SIO_010017
Protein Sequence sio:SIO_010082 (sio:SIO_010083) RNA Sequence
is translated into (is translated from) 628
sio:SIO 0
_0006 _00
SIO
refers
to
28 sio: s to
sio:SIO_000008 f e r
blastso:BLAST_Alignment re
has attribute
sio
hmmrso:HMMR_Alignment is :SIO_ sio:SIO_000008 sio:SIO_000629
su lsrn:GO_Identifier
bje 000 is subject of
ct 6 has attribute
sio:SIO_000332 of 29
is about lsrn:GO_Record
sio:SIO_000629
sio:SIO_010049 is subject of
Molecular Site lsrn:Pfam_Record
sio:SIO_000629
sio:SIO_000008 is subject of
has attribute
lsrn:Pfam_Identifier goaso:GO_Function_Annotation
Our modelling facilitates many different Omics-related queries and
is also future-proof because many SADI services, based on different
but related data sources, can use the same or compatible modelling.
4 SADI services.
We have created almost 60 SADI services specifically for our fish
toxicology use cases, that expose resources mentioned in Section 2.
Where possible, we wrapped existing Web services implemented by
database and tool providers, as SADI services to access live data.
This ensures results are current and helps to avoid the maintenance
9
cost associated with data mirrors. Descriptions of the services are
provided at http://cbakerlab.unbsj.ca:8080/FISHTOX-SADIServices.
We only briefly describe some of the services here.
ArrayExpress-based services. Several services which find exper-
iments deposited in ArrayExpress and retrieve information about
them, were created by wrapping RESTful services provided by Ar-
rayExpress. The data was modelled using a combination of EFO
(natively supported by the database) and SIO properties. The fol-
lowing figure shows the IO modelling for the service
SpeciesName2AEExperimentalRecord that searches for ArrayExpress
records by a species name:
sio:SIO_010000 rdfs:subClassOf rdf:label
string
organism
microarrayso:has_instance
sio:SIO_000028
has part
efo:EFO_0000635 a
organism part
sio:SIO_000231
is input in
efo:EFO_0002694 a
experimental process
sio:SIO_000629
is subject of
a
lsrn:ArrayExpressExperiment_Record
sio:SIO_000008
has attribute
a
lsrn:ArrayExpressExperiment_Identifier string
sio:SIO_000300
has value
The service implementation simply extracts the species name string
from the input RDF, makes a request to the ArrayExpress search
service and converts the resultant ArrayExpress record IDs to RDF.
Sequence search and alignment. The HMMER Web site pro-
vides a RESTful service for sequence search and alignment, so we
created a SADI service that, given an amino acid sequence, finds
Pfam records for protein domains similar to parts of the input se-
quence. The input class of the service is a ’protein sequence’ and
the output class is defined in the hmmrso ontology as a class that ’has
attribute’ some (lsrn:HMMR Alignment that (’is about’ some
(’molecular site’ that (’is subject of’ some
lsrn:Pfam Record)))) (here and throughout the rest of the paper
single-quoted labels represent the corresponding identifiers from
SIO). Similarly, SADI services for BLAST were created by wrap-
ping NCBI Web services. Their inputs can be protein, DNA or RNA
sequence strings, depending on the variant of BLAST. The output
is defined using a special alignment class, in an approach similar to
10
HMMER3 services. For example, the output for the BLAST on pro-
teins is defined in the blastso ontology as ’has attribute’ some
(blastso:BLAST Alignment that (’refers to’ some
(’protein sequence’ that (’is subject of’ some
lsrn:NCBI NP Record)))).
Database ID mapping. Answering many queries requires cross-
mapping of information from different databases, in one form or
another. For example, we may know a RefSeq ID of a protein se-
quence, but retrieving Gene Ontology annotation requires UniProt
IDs of the protein. The RefSeq-to-UniProt mapping is already avail-
able from RefSeq and, in general, biomedical databases often refer-
ence other databases, so we only need an appropriate modelling in
order to implement the DB record mapping as a SADI service. Our
RefSeqNP2UniProt service takes an instance of (lsrn:NCBI NP Record
and (’has attribute’ some (lsrn:NCBI NP Identifier and (’has
value’ some string)))) as input and annotates it as an instance of
the following output class: (’is about’ some (’protein sequence’
and (’is subject of’ some lsrn:UniProt Record))).
Gene Ontology annotation. We created several SADI services
annotating proteins with GO identifiers of known molecular func-
tions they have and biological processes they participate in. The
data for several model organisms, such as humans, zebrafish and
house mouse, originating from the corresponding model organism
databases, are taken from the GO website [5] that contains up-
to-date annotations. The services expect URIs of model organism
database records as input and return outputs conforming to the fol-
lowing class: ’is about’ some (’deoxyribonucleic acid sequence’
and (’is transcribed into’ some (’ribonucleic acid sequence’
and (’is translated into’ some (’protein sequence’ and
(’is subject of’ some (goaso:GO Function Annotation or
goaso:GO Process Annotation))))))), where the annotation classes
represent resources that are directly linked to some GO records.
5 SPARQL queries.
In this section we present three example queries which address the
types of questions pertinent to the analysis of fish toxicology ex-
periments with DNA microarrays. However, the SADI services we
have built, and the modelling we use, are not limited to these ex-
amples. Different combinations of our SADI services, together with
multiple public SADI services developed for other purposes, can be
used to answer other queries in contexts other than fish toxicology
– the methodology we are using here is widely applicable to gene
expression analysis in general.
We ran the queries with two SADI query engines – SHARE [25] and
Hydra [2], which compute SPARQL queries by picking and calling
11
suitable SADI services from a dedicated registry of fish toxicology-
related services. The engines are currently only proof-of-concept
prototypes, so we did not pay attention to the performance. At this
stage, we are primarily concerned with demonstrating the princi-
ple possibility of using such tools for data federation, which will
justify further efforts on improving the tool performance and the
framework itself – such work requires highly specialised skills and is
very costly, so extensive evidence of the utility of SADI is necessary
to obtain adequate government sponsorship or private investment.
We use two query engine prototypes to be able to execute as many
queries as possible in practically acceptable time.
5.1 Query I: Finding relevant microarray experiments.
Experimentalists in fish toxicology need to compare their work with
previous published experiments, which requires locating microarray
experiments with related parameters. For example, a biologist may
be interested in identifying genes whose expression has been mea-
sured in the tissue hypothalamus of the species largemouth bass
in existing experiments. Currently, the main option is to use Web
tools provided by experiment databases like ArrayExpress, which
requires multiple searches and manual inspections of many experi-
ments each of which may use different microarray platforms. SADI
allows it to be done in a much easier way. A declarative SPARQL
query whose simplified pseudo-SPARQL version is given below, ex-
presses the question formally:
SELECT ?experiment_id ?tissue_name ?platform_id ?gene_id
FROM
WHERE {
?org_class aeso:has_instance ?org_instance .
?org_instance a ncbi:NCBITaxon_27706 . # largemouth bass .
?org_instance ’has part’ ?org_part .
?org_part ’is input in’ ?exp_process .
?exp_process ’is subject of’ ?exp_record .
?exp_record ’has attribute’ [’has value’ ?experiment_id] .
?exp_record ’is about’ ?exp_process .
?exp_process ’has participant’ ?array .
?array ’is subject of’ ?array_platform_record .
?array_platform_record a lsrn:ArrayExpressPlatform_Record .
?array_platform_record ’has attribute’
[’has value’ ?platform_id] .
?array_platform_record ’is about’ ?array .
?array ’has part’ [rdfs:label ?gene_id] .
?org_part rdfs:label ?tissue_name .
FILTER regex(?tissue_name,"hypothalamus","i") .
}
12
Note that the RDF file large-mouth-bass.owl specified in the FROM
clause contains the URI of the species to instantiate ?org class.
The query was submitted to SHARE which resolved it by find-
ing and calling the SADI service SpeciesName2AEExperimentalRecord
to identify relevant ArrayExpress experiment records, the service
AEExperiment2Platform to retrieve the corresponding microarray
type (“platform”) records and the service AERecord2Microarray to
extract microarray details, such as the DNA sequences. No un-
derstanding of the ArrayExpress semantic idiosyncrasies or data
syntax was required to formulate the query. SHARE identified two
microarray experiments satisfying the query.
Despite the fact that the SADI registry used in our experiments
only contained services necessary for our experiments presented
here, the query was quite hard for SHARE: it required an overnight
run on a commodity quad-core server running both SHARE and
the services, and practically all the time was spent inside the query
engine itself. Hydra produced first answers in less than 4 minutes.
When it was terminated after 1 hour, it had produced over 12,000
answers and was generating more answers. Less than 1/3 of the time
was spent on behalf of Hydra itself, which is a good progress relative
to SHARE. Moreover, a majority of the executed service calls were
redundant, due to the current lack of corresponding optimisations
in Hydra, which allows to estimate that Hydra’s performance will
be several times better when such optimisations are introduced.
5.2 Query II: Finding functional information about
genes.
Gene Ontology annotation is very valuable for understanding toxic-
ity of chemicals as it tells the toxicologist what (parts of) biochem-
ical reactions are disrupted when a chemical affects the expression
of a particular gene. However, such functional annotation is not
directly available for many non-model species, such as largemouth
bass, so biologists have to infer GO annotations based on sequence
similarity with known genes in model organisms for which experi-
mental evidence is recorded in public repositories.
The following query annotates ten most significantly affected genes
in largemouth bass treated with the pesticide dieldrin [17]:
SELECT ?GO_record
FROM
WHERE {
?DNA_chip_sequence a ’deoxyribonucleic acid sequence’. # DNA
?DNA_chip_sequence ’has attribute’ ?alignment .
?alignment ’refers to’ ?sequence_hit .
?sequence_hit ’is subject of’ ?refseq_record .
?refseq_record ’is about’ ?protein_sequence .
13
?protein_sequence ’is subject of’ ?UniProt_record .
?UniProt_record a lsrn:UniProt_Record .
?UniProt_record ’is about’ ?UniProt_protein_sequence .
?UniProt_protein_sequence ’is subject of’ ?GO_annotation .
?GO_annotation a goa:GO_Function_Annotation .
?GO_annotation ’is subject of’ ?GO_record .
?GO_record a lsrn:GO_Record .
}
The gene sequences are provided in the file 10genes.rdf. We ran
this query with Hydra and its earlier version with SHARE. To ex-
ecute the query, the Hydra engine calls the BLAST service
BLASTx2RefSeqProtein to find proteins similar to the ones corre-
sponding to the input DNA in the NCBI RefSeq database, retrieves
the protein IDs for them with RefSeqNP2UniProt, and then retrieves
GO annotations for these proteins from a number of GO datasets
for different model organisms with services like
HumanEBIUniProtRecord2GO. The results obtained by Hydra indicate
that the exposure to dieldrin might affect ribosomal processes. Ret-
rospectively this is not surprising, but it shows that our methodol-
ogy may be useful in future experiment analyses.
This query provides an interesting observation. The line
?refseq record ’is about’ ?protein sequence seems to duplicate
?sequence hit ’is subject of’ ?refseq record because ’is about’
is inverse to ’is subject of’ and the user could also postulate that
the property ’is about’ is functional for RefSeq records. Unfortu-
nately, we cannot replace the query with a more natural version
by merging these two lines because neither SHARE nor Hydra cur-
rently support this level of expressivity.
In addition to tabular results, the RDF returned from the query
captures detailed information regarding how candidate functions
relate to the query inputs. For example: the quality scores for the
alignments of similar proteins and the experimental evidence for
the assigned functions of candidate orthologs.
This query also required an overnight run with SHARE. Hydra was
able to execute it in less than 1.5 hour on the same hardware.
5.3 Query III: Identifying protein domains.
Another way to informatively characterise an affected gene – in
addition to GO annotations – is to identify domains, i. e., subse-
quences with known biological functions, the corresponding protein
contains. Often, this cannot be done for microarray sequences di-
rectly, because they are incomplete, and the use of ORF prediction
tools is necessary to identify plausible protein sequences. Then, a
sequence search and alignment procedure, such as HMMER, can
be used to retrieve the domains. The following query implements
14
this combination of tools and retrieves Pfam names for domains in
proteins produced by the same ten largemouth bass genes as in the
previous query:
SELECT ?protein_domain_name
FROM
WHERE {
?DNA_chip_sequence ’is transcribed into’ ?RNA_sequence .
?RNA_sequence ’is translated into’ ?protein_sequence .
?protein_sequence ’has attribute’ ?alignment .
?alignment ’is about’ ?molecular_site .
?molecular_site ’is subject of’ ?pfam_record .
?pfam_record rdfs:label ?protein_domain_name
}
Three services are called by SHARE: the service DNA2RNATranscriber
transcribing a DNA into an RNA, the service ORFPredictor pre-
dicting the ORF and translating the RNA to a protein sequence,
and the service HMMR3PFamA retrieving Pfam domain IDs for subse-
quences of the protein. In several minutes the query engine returned
the answer that a domain related to ribosomal activities was found
on the gene UF Msa AF 100231, which accords with the results for
Query II. The low coverage on genes (1/10) is not surprising given
that our HMMER3 SADI service was configured to retrieve only
strongly similar sequences. The service could be given more liberal
parameters, but the service configuration functionality is not yet
implemented in SHARE or Hydra.
6 Discussion.
We have demonstrated that semantic data federation with SPARQL
querying of SADI services representing multiple bioinformatics
databases and analytical tools can help to answer relevant research
questions in fish toxicology. The SADI service kit we have cre-
ated exposes the databases and programs in a semantically explicit
way and enables diverse and powerful queries using query engines
like SHARE or Hydra. When using this framework, an ecotoxicolo-
gist would not have to understand the semantics and technicalities
of the underlying resources in order to formulate queries across
databases and tools. He would not have to program any additional
scripts of workflows as the query execution happens completely au-
tomatically.
Novel and emergent tools and algorithms that can be utilized for
query resolution, such as new sequence alignment methods or read-
ing frame prediction, can be incorporated into query resolution by
implementing a SADI web service wrapper that uses the relevant
15
service ontologies. The new services are registered to the SADI
repository and these services will be incorporated without chang-
ing the query.
We conclude that altogether the approach promises to be practi-
cally relevant at least for Omics-based analyses of ecotoxicology
data. We would like to emphasise that the queries we consider in
this paper can be used in contexts other than ecotoxicology: e.g.,
the ability to search for relevant microarray experiments or Gene
Ontology annotation of DNA sequences may be useful in many
contexts where DNA microarray experiments are used.
Currently, query composition required a semantic-web domain ex-
pert to compose SPARQL queries in close consultation with a bi-
ologist. Query composition required careful consideration of the
semantic modelling, the available pool of services, and the perfor-
mance limitations of the query-resolution engine. In many instances
additional services were required to resolve new queries, so the de-
sign and testing of queries was an iterative process. We expect that
query composition should become a faster and less painful pro-
cess as the ecosystem of SADI tools and services matures. Client
performance limitations are already being addressed with the de-
velopment of the Hydra engine.
Regarding what could be improved in SADI and SADI query clients,
our current observations support most conclusions of [21], namely
(i) better user interfaces are needed to relieve bioinformaticians
from the necessity of writing SPARQL and for easier exploration of
semantic data schemas, (ii) query answers should carry verifiable
provenance information so that they can be used beyond the discov-
ery phases of research, and (iii) the ontology-based data modelling
part of the SADI service development process could be better sup-
ported, although this will be less of a problem when a critical mass
of reusable public SADI services is created. One item we would like
to add to this wish list is better logical expressivity of queries, as
illustrated in Section 5.2.
We would like to discuss (i) in more detail here. In principle, non-
programmer users can be trained to write SPARQL queries: for ex-
ample, the third author, who is an undergraduate Physics student,
actively participated in composing and debugging the queries. A
query is typically composed incrementally: the user write a few lines
initially, runs a query engine, assesses the results, adds a few more
lines, an so on. This process will be significantly simplified when
tools for extraction of semantic schemas from service descriptions
and ontologies are implemented: such tools facilitate easy look-up
of classes and properties that are meaningful in a particular query
context.
However, even simplified by schema navigation, SPARQL querying
is not sufficient to fully realise the vision of self-service querying
16
because it is difficult to expect that it can appeal to the mass
user. More friendly graphical or keyword-based query interfaces, as
in [23], have to be used on top of SPARQL. Intutitive graphical
representation of queries would relieve the users from the need to
learn the SPARQL notation. Facilities for mapping keywords to
query fragments would help users to focus the class and predicate
look-up better. Finally, the incremental query composition driven
by concrete (instance-level) data rather than just a schema, can be
supported by a form of faceted browsing (see, e. g., [26]).
7 Related and future work.
The SADI framework has been compared to related approaches
in a number of publications – see, e.g., [27, 21], so we omit this
discussion here and focus on the work on SADI applications.
In an early bioinformatics case study [21], SADI was used as a
medium for deploying text mining software that extracts mentions
of mutations and their impacts on protein properties from biomedi-
cal texts. Similarly to what we do in this paper, the utility of SADI,
especially its integrative power, was demonstrated in a number of
biologically meaningful scenarios through a SPARQL interface. The
data obtained by text mining was integrated with multiple sources
of data on genes, proteins, biochemical reactions and drugs, as well
as some molecular structure visualisation programs.
In [10, 9] Chepelev et al conduct two cheminformatics case studies.
The first paper describes a SADI-based prototype for integrating
components of a lipid classification pipeline – a molecular substruc-
ture detection program and an ontology-based molecule classifier –
with each other and with external biomedical data. The second pa-
per describes a package of SADI services based on a Java library for
cheminformatics and discusses, as a use case, detection of drug-like
chemicals by ontology-based querying of the SADI services.
The study presented here differs from the ones mentioned above
by exploring real use cases, albeit simplified, corresponding to real
research questions, as opposed to realistic ones used in the earlier
projects, and the involvement of a working biologist as a target end
user. Another difference is the focus on the analysis of experiment
results, as a type of research activity where semantic data federation
may have a particularly strong impact.
We would also like to mention a case study [22] for SADI that ex-
plores the possibility of using it for clinical intelligence purposes,
more specifically for surveillance of hospital-acquired infections, al-
though it primarily focuses on using SADI as a vehicle for semantic
querying of relational databases rather than for data integration.
A natural continuation of the work presented in this paper is to
try to answer some open fish toxicology questions. To this end, we
17
are currently extending the repertoire of services and queries tar-
geting a number of questions about the effects of pesticides and
steroids on fish, that can be answered by federated querying of
public biomedical resources. As another future work direction, we
will try to reproduce a subset of microarray and toxicology work-
flows from the myExperiment repository [16] with SADI services
and SPARQL queries.
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