=Paper=
{{Paper
|id=None
|storemode=property
|title=Semantic Web Atlas: Putting Gene Expression Data into Biological Context
|pdfUrl=https://ceur-ws.org/Vol-952/paper_11.pdf
|volume=Vol-952
|dblpUrl=https://dblp.org/rec/conf/swat4ls/JuppPM12
}}
==Semantic Web Atlas: Putting Gene Expression Data into Biological Context==
https://ceur-ws.org/Vol-952/paper_11.pdf
Semantic Web Atlas: Putting Gene Expression
Data Into Biological Context
Simon Jupp, Helen Parkinson, and James Malone
Functional Genomics Group, European Bioinformatics Institute, Wellcome Trust
Genome Campus, Hinxton, Cambridge CB10 1SD.
jupp@ebi.ac.uk, malone@ebi.ac.uk
Abstract. We present an RDF representation of the Gene Expression
Atlas (GXA) at the European Bioinformatics Institute. The RDF rep-
resentation provides new opportunities for data integration, querying,
error detection and curation. The GXA RDF connects to other EBI re-
sources including Ensembl, UniProt and Reactome following linked data
principles. These links enable queries across resources such as querying
differential gene expression in the context of pathway and gene product
functions. We show how the RDF representation of the GXA adds value
to the existing data through a series of novel biological questions which
interrogate the different resources and which were not previously possible
through the existing GXA.
Keywords: RDF, gene protein pathway integration, Gene expression
Atlas
1 Introduction
The Gene Expression Atlas database (GXA) [1] [2] at the European Bioinfor-
matics Institute contains meta-analyses of gene expression experiments from over
3,000 publicly available biomedical investigations taken from the ArrayExpress
(AE) archive [3]. Data stored in the GXA is manually curated by expert biologists
and makes use of ontologies and controlled vocabularies to perform meaningful
annotation across experiments. Genes are annotated using ENSEMBL [4] and
experimental variables are annotated using the Experimental Factor Ontology
(EFO) [5]. These annotations are then stored inside a relational database and en-
able biological queries to be performed across the data. For example, which genes
are differentially expressed in liver cancer or under which conditions is HOXA1
differentially expressed can be asked. Ontologies allow for much richer queries by
additionally searching across synonyms entered in searches and by utilising the
subsumption hierarchy within the ontology to return subclasses when a higher
level superclass is used in the query (e.g. cancer returns experiments annotated
with leukemia).
Understanding the role of genes within biological processes, such as diseases,
is an important area of research [6]. Gene expression, however, is only one part
of the larger biological picture. Once a list of genes of interest is produced, a
researcher will often wish to then investigate further by identifying, for example,
which proteins, biological processes or signaling pathways correspond to the bio-
logical conditions of interest [7]. Typically this process will require the biologist
to perform similar types of searches across multiple resources such as UniProt or
Reactome before aggregating the resulting data and attempting to make sense
of them.
In the post-genomic era, the heterogeneity of data models and formats has
made interoperability and data integration difficult [8]. The emergence of bio-
ontologies has improved this problem, although divergences across the ontology
community also present their own challenges [9]. Despite these challenges, a
wealth of ontologies now exist that are providing the necessary vocabulary for
consistent annotation of a wide variety of biological data. These ontologies are
readily available through resources such as the NCBO BioPortal [10], and the
OBO foundry [11]. This availability has been key to the adoption of these on-
tologies to annotate and integrate resource and database like those hosted at the
EBI.
Providing universal access to the often complex and heterogeneous data being
generated in the life sciences continues to be a challenge in bioinformatics. Many
databases now provide programmatic access to data through the use of web
browsers and web service based APIs. These APIs are useful for exposing the
data within a single resource, but integrating and querying across many resources
is still problematic. Where ontological annotations are concerned, few resources
exploit the full potential of the ontological structure to answer queries [12].
Moreover, the ability to ask semantically meaningful queries across resources
such that one item of data is informed by data from another resource is either
not possible, requires all relevant data to be aggregated into a single platform
(e.g. Endeavour [13]) or requires very complex workflow solutions.
The Semantic Web promises solutions to many of the challenges of large
scale data integration [14], and has subsequently received a lot of interest and
adoption from the life sciences [15] [16]. At the core of the Semantic Web is
the Resource Description Framework (RDF) 1 , which provides a mechanism for
publishing and describing data on the Web. RDF is a data model for describing
graphs, where the semantics of relationships between nodes can be made explicit.
RDF benefits from being built on existing Web technology. Uniform Resource
Identifiers (URIs) provide a mechanism for identifying resources or data on the
Web. HTTP provides a communication protocol for retrieving information about
those resources, such as relationships to other resources. A common XML based
exchange format for RDF exists along with a standardised query language called
SPARQL 2 . Linking Web resources using RDF is at the core of the Linked Open
Data movement which is a growing set of resources linked by RDF [17].
An increasing amount of life science datasets are becoming available as RDF,
and publishing guidelines are emerging through community efforts such as the
W3C Health Care and Life Science working group (HCLS) [18]. Projects such
1
http://www.w3.org/RDF
2
http://www.w3.org/TR/rdf-sparql-query
as Bio2RDF [19] provide access to a wide range of linked life science data and
previous efforts such as the LODD dataset [20], and KUPKB [21] show how
Semantic Web technology can enable novel biological discovery [22]. As a major
bioinformatics service provider, the EBI is committed to adding value to existing
resources. Providing production quality RDF that is synchronised with release
cycles is a major step in that direction.
Two notable resources publishing RDF are the UniProt databases of protein
sequence [23] and functions and the Reactome database of biological path-
ways [24] both developed in collaboration with the EBI. These two resources
are already cross linked via UniProt accessions. Several other EBI resources are
beginning to publish RDF, such as the ChEMBL database of bioactive drug-like
small molecules [25], which is generating linked RDF as part of the OpenPhacts
project to create a connected knowledge base of pharmacological data [26]. The
ultimate goal is to produce an integrated set of production quality EBI resources
published as RDF. In this paper we report on the development of the GXA
database RDF resource and describe how we link to other EBI resources. Com-
bining gene expression data with protein function information right through to
the pathways where those proteins are active enables us to explore these datasets
from multiple biological perspectives. We demonstrate some of the added value
we gain from this RDF representation by describing some of the new queries we
can now ask, show how the representation can improve error detection and illus-
trate some of the additional biological insights that can be gained by querying
multiple integrated resources at once with an example relating to the study of
diabetes.
2 Method
2.1 The GXA data
The GXA represents a subset of experiments from the ArrayExpress archive,
that have data amenable to the GXA statistical analysis methodology, and meet
a minimum criteria related to the numbers of replicates and ability to map the
array design to a current genome build [2]. For each study the re-analysis of
the raw expression data produces lists of differentially expressed genes relating
to certain biological conditions. For each assay within an experiment, multiple
biological conditions may be under investigation. Both the biological condition
and sample descriptions are composed using a simple type/value notation. e.g.
type = organism part, value = liver. The GXA statistics are only generated for
studies where the samples are annotated to more than one different biological
condition e.g. liver and lung. The type/value pairs go through both a manual
and semi-automated curation pipeline 3 in order to clean up and normalise the
annotations (this is in addition to the curation that has already taken place
within ArrayExpress). This data cleanup combined with the ontological markup
3
http://zooma.sourceforge.net
adds value to the source data and enables tighter integration and more powerful
queries.
We can conceptually separate the GXA model into three components that
capture different aspects of the data. The first component is the notion of the
study itself. Each study has associated meta-data such as the accession number,
description, submitter, submission date and related publications. This informa-
tion is derived from the original Investigation Description File (IDF) submitted
to the AE archive, however, only a subset is retained for the GXA. We initially
focus on capturing only this subset in RDF. A complete conversion of the AE
data, and microarray data in general, into RDF is beyond the scope of this work
and the subject of other ongoing work [27].
Each experiment has a set of associated assays. Each assay has an accession
number, and links to a set of experimental factors and may also link to related
sample information. Each factor and sample has a type and value annotations,
along with any optional ontological annotations. Figure 1 shows the basic model
for capturing assay and sample information. This information originates from
the Sample and Data Relation File (SDRF) originally associated with the AE
submission. This information is subjected to manual and semi-automated cura-
tion to normalise property types, and apply ontological annotation to property
values using the Experimental Factor Ontology (EFO).
Fig. 1. Abstract model for the data in GXA. Experiments are linked to samples, that
are used in some analysis to compute gene lists.
Finally, each experiment can have multiple data analyses associated with
it, each of which produces a set of gene lists. The analysis should record the
provenance of when the analysis was done, which software was used and the set
of input files used. Some studies have more than one type of array design, so
separate gene lists are computed for each platform within the study. A gene list
is generated for each value of a biological condition for a given type (e.g. values
cisplatin and untreated for type compound). For each list one of the biological
condition values is acting as the independent variable. All remaining assays for
that condition type play the role of the control.
For example, after analysis, experiment E-GEOD-24868 4 has two gene lists.
The first is differentially expressed genes where the independent variable is cell
type / PC-3/S, and a second gene list where the independent variable is cell
type / PC-3/Mc. The gene lists includes a reference to the probe sets, or design
elements, for the given platform, along with a measure of differential gene ex-
pressions and an associated confidence measure. In order to map the probe ID
to a given gene, the GXA generates mappings to Ensembl genes at every release.
2.2 Ontologies
The GXA already uses EFO as the primary annotation ontology for biological
conditions and bio-sample information [5]. EFO is an application ontology that
imports terms from a set of reference ontologies based on the criteria below,
and defines terms locally where a suitable term cannot be found in an external
ontology that meets these criteria:
– Coverage - that the ontologies include classes that describe concepts in the
experimental data;
– That are available in OWL or OBO format;
– That are maintained or are actively developed;
– Have some evidence of usage across the community.
Figure 1 provides an outline for the RDF graph needed for modeling GXA
data. Using the ontology selection criteria above, we used the NCBO bioportal
to survey a wide range of ontologies for typing resources in the GXA model. We
found considerable overlap for many of the terms, especially for more general
concepts such as experiment, assay, sample, gene list and gene expression. The
Ontology of Biomedical Investigations (OBI) [28] provides good term coverage
for microarray experiments due to the inclusion of many terms from the original
MGED ontology [29]. Despite the good term coverage, the OBI model is complex
when it came to asserting relations between concepts. OBI provides a framework
for creating very accurate semantic descriptions of any type of biomedical in-
vestigation. As such it has a very large, overarching framework to satisfy the
very wide range of queries that may be possible about an investigation for any
domain. This often involves making many distinctions and assertions that add
4
http://identifiers.org/gxa.expt/E-GEOD-24868
complexity to the model but add little extra value in the context of the nar-
rower queries needed in this work, such as distinguishing between a written plan
specification and the duplicated execution of this plan in a process as separate
entities. These descriptions add considerable complexity to the underlying RDF
model that makes querying and exploring the data harder.
Instead, a lighter weight schema, that makes fewer ontological commitments,
but has sufficient semantics to satisfy our collected competency questions was
required. As such, some of the more general purpose vocabularies were also
sourced to provide predicates that capture relationships between data. In ad-
dition to OBI, we looked at the OBO relation ontology [30], the Information
Artefact Ontology (IAO) 5 , Semantic-science Integration Ontology (SIO) 6 and
PROV-O 7 . There was significant overlap between several of these ontologies al-
though only SIO provided all of the classes and predicates required for capturing
the model required for the GXA RDF. In order to maximise external integra-
tion, we created equivalence statements between classes and predicates which
overlapped and incorporated both into the RDF store. The schema is currently
available at http://wwwdev.ebi.ac.uk/fgpt/efosemweb.html.
It is worth noting that there is ongoing work as part of the W3C HCLS group
to provide a best practice note on describing gene expression data in RDF but
that this is presently incomplete. Our initial focus for this work is to ask novel
queries of the existing GXA data rather than to produce a model by which all
microarray or sequencing data should be described.
2.3 Identifiers
A URI scheme was devised for the GXA to ensure that the data conforms to
Linked Open Data guidelines 8 Best practice for linked data state that you
should:
– use URIs to name things
– use HTTP URIs so that people can look up those names
– When someone looks up a URI, provide useful information, using the stan-
dards (RDF, SPARQL)
– Include links to other URIs. so that they can discover more things.
We address issues 1, 2 and 3 by generating URIs using the identifiers.org [31]
registry, hosted at the EBI. Identifiers.org provides resolvable persistent URIs
which can be set to redirect HTTP request to the relevant resource. This mech-
anism will ensure that the URIs used in the GXA RDF will always resolve to
some resource and removes any dependency between GXA RDF URIs and the
GXA web server.
5
http://code.google.com/p/information-artifact-ontology
6
http://code.google.com/p/semanticscience
7
http://www.w3.org/TR/prov-o
8
http://www.w3.org/DesignIssues/LinkedData.html
All external terminologies and ontologies referenced by the GXA RDF model
conform to the same set of linked data principles, which ensures we satisfy criteria
4. In addition to linking out to other resources relating to aspects of the study
and study design, we also wanted to link the gene lists to relevant resources.
GXA currently maps probe set ids to gene accessions from the Ensembl genome
database. Ensembl are yet to publish their data in RDF, however, they have
registered a URI scheme with identifiers.org. We can now link out from GXA
genes to Ensembl URIs using the Identifers.org schema, thus future proofing our
links.
Given the central importance of a gene to so much of the study of biology, it
naturally provides us with a vast amount of linking potential from the transcrip-
tomics data described in the GXA. Another important dataset is the UniProt
database, which provides a comprehensive, high-quality and freely accessible re-
source of protein sequence and functional information. UniProt already publish
their data as RDF and have a persistent URL strategy based on PURLs 9 . As
part of the GXA RDF build, we generate simple RDF datasets that provides
links from Ensembl genes to their associated UniProt Proteins. These map-
pings are derived from the monthly UniProt release available from the UniProt
FTP (ftp://ftp.uniprot.org/pub/databases/uniprot). Linking to UniProt
enables us to link the GXA data out into other RDF datasets such as the Gene
Ontology Annotations (GOA) for protein function, Reactome for pathway data,
and ChEMBL for chemical compounds.
2.4 Data processing
The pipeline for building the GXA RDF is written in Java and uses the Open-
RDF Sesame framework [32], the OWL-API [33] and GXA API 10 . Upon each
release of the GXA, the API is used to read data out of the GXA database, each
experiment is processed and converted into an individual OWL/RDF file. The
file is classified using an OWL reasoner to precompute any additional entail-
ments. Once all the files have been generated a separate pipeline is used to load
each file into a triple store. We are currently using the OWLIM-SE [34] version
5 triple store within the OpenRDF Sesame framework. The full pipeline is set
to run on the EBIs computing cluster and currently takes around 18 hours to
complete.
2.5 User group competency questions
User engagement is a key component of this work. In order to understand how
we could usefully extend the Gene Expression Atlas, we engaged in a series of
user interviews to elicit requirements. These requirements were shaped into the
form of competency questions which we would build the representation towards
answering and which would then form a set of evaluation criteria which we could
9
http://purl.oclc.org
10
https://github.com/gxa/gxa
test the resource with. These users were from a diverse set of backgrounds and
held a varying array of expertise which included mouse phenotype biologists,
bacteria experimentalists, data analysts and clinicians.
Examples of questions included the following:
– Which genes are differentially expressed in adult mice bred in oxygen rich
vs oxygen poor environments?
– Which genes with antigen binding function are differentially expressed, where
and with regard which diseases and which pathways?
– Which genes involved in Basigin interactions are differentially expressed in
Malaria?
– Which genes are differentially expressed in a knockout mouse strain versus
the wildtype?
– Which genes are differentially expressed in diseases which occur in the uri-
nary tract?
3 Results
Using the described model, an RDF store of data from version 12.7 of the Gene
Expression Atlas at EBI was produced. This included a total of 3,317 experi-
ments which is the majority of the current Gene Expression Atlas and covered
328,645 genes across 83,233 assays. 28,702 gene lists were described as a result
of the transformation into the RDF model. The total number of triples loaded
is 773,025,372. The generated RDF files will soon be available to download, and
are already available via a public SPARQL endpoint 11 .
We have included mappings between probeset IDs, Ensembl identifiers and
UniProt identifiers. We loaded version 40 of the Reactome RDF database so that
we can integrate queries across UniProt accessions. Identifiers.org provided us
with URIs to reference Ensembl genes, which provide us with a large amount of
integration potential. Finally we can integrate gene products to UniProt acces-
sions held in the UniProt SPARQL endpoint 12 . Examples are given on the GXA
RDF website 13 that demonstrate for the first time federated queries across the
GXA and UniProt resources. For example, we can now query for genes differen-
tially expressed and filter according to the gene product GO annotations from
UniProt. The use of identifers.org to mint URIs for GXA resource, in addition
to our efforts to link out to other RDF resources puts the GXA firmly in the
11
http://wwwdev.ebi.ac.uk/fgpt/gxa-sparql
12
http://beta.sparql.uniprot.org
13
http://wwwdev.ebi.ac.uk/fgpt/gxa-sparql
growing Linked Open Data cloud. More importantly the rich biological informa-
tion held in GXA serves as a valuable addition to the set of life science datasets
already published as RDF.
3.1 Integrating across resources
One of the objectives of this work was to enable querying across a wider range
of biological resources than is currently possible with the GXA, with particular
reference to queries required by our target user group. Our RDF model for de-
scribing GXA results enables queries such as “find differentially expressed genes
in liver cancer versus normal tissue” to be framed, which was not previously pos-
sible. In addition, with the expressiveness granted by including the EFO within
a framework native to RDF, queries which utilise some of the axioms can now
be expressed. For example, find differentially expressed genes which have dis-
ease location in genitourinary system which calls upon the axioms within EFO
using the object property has disease location to link disease to organism parts.
A further type of query is when we wish to be explicit about which results we
do not want to return. For example, querying for experiments in which the in-
dependent variable is not disease rather than asking for independent variables
that are annotated with normal. It is noteworthy that these are two different
queries, even though they would seem identical. Normal is often used to describe
some background normality within the experiment and is therefore contextual;
this does not necessarily imply it is free from disease (or never was). We can
see examples of this in multiple experiments, such as E-TABM-1171, in which
the experiment is described as differential expression in cancer samples com-
pared to normal samples. Here, the normal sample is annotated as normal and
also benign; the annotation should really be interpreted as not cancer. With the
GXA RDF store we can ask this query making use of the NOT EXISTS filter in
SPARQL, by simply asking for independent variables which are not of the type
disease.
3.2 Answering biologically relevant questions
Our engagement with user groups, as described in the previous section, produced
a list of queries that were desirable of the new GXA RDF store. In order to
evaluate these competency questions, we first designed SPARQL queries intended
to answer these questions and secondly, performed an evaluation of the results
using the literature. Examples of these queries can be seen on the ].
One category of query often requested by users is the ability to view differen-
tially expressed genes in the context of pathways. It is known that most pheno-
types observed are the result of combination of genes. When studying a particular
phenomenon for gene expression, techniques such as enrichment analysis exploit
known information about individual genes to see if if groups of related genes are
being differentially expressed. These groups are typically categories taken from
the Gene Ontology, but similar analysis can be performed using alternate gene
annotation, such as associated pathways. We can begin to explore this kind of
query using the GXA RDF by exploiting the new links between gene expression
and the Reactome pathways data.
One query of interest to our biologist user group concerned pathways in which
genes were differentially expressed in muscle tissues where the sample is taken
from an experiment studying diabetes vs normal. We can construct a SPARQL
query to get all differentially expressed genes in muscle where the experimental
factor is diabetes. This initial query returns 287 genes. We can now refine this
query to only include genes with known human pathway associations from Re-
actome, which reduces the set of genes to just 31. From that list of genes we see
experiment E-GEOD-1659 (which is investigating gene expression in mice mus-
cle before and after exercise) has found differential expression in one of those
genes - the Receptor (calcitonin) activity modifying protein 1 (Ramp 1 14 ) being
down regulated in mice. The query also tells us that Ramp1 has an association to
the Calcitonin-like ligand receptors pathway 15 . A short literature survey con-
firms the association of Ramp genes to the Calcitonin pathway. It also suggests
evidence for associations between this pathway to energy homeostasis, obesity
and diabetes [35] [36]. What these results demonstrate is that, even though the
experiments are studying different conditions (diabetes in one, exercise in an-
other), they report similar genes which are involved in similar pathways and that
literature can confirm that this pathway may have roles to play in all of these
conditions. The data contains such information but it is only made apparent
once everything is integrated together. Where literature does not confirm such
associations, these can be considered potential hypothesis for further investiga-
tion.
Scanning the list of 31 candidate genes and pathways from the previous query
requires a trained eye from a scientist working in the domain in order to assess
whether they have any relevance. They key point is that the integration is work-
ing to bring relevant information together and provide alternate facets over the
data. In this case we facet the results by pathways, but we could easily link out to
UniProt to retrieve GO annotations for protein function and facet along those.
This kind of data exploration can enable potentially new hypotheses generation
or can help to validation known or expected information, show contradictory
information or simply open up new avenues for exploration.
3.3 Annotation coverage and error detection
One of the benefits of the RDF model is the ability to ask questions about indi-
viduals relating to their type. An example of where this is particularly useful is
when looking for possible annotation inconsistency or error. For the factor type
‘disease state, for example, we would expect to only see annotations which are
types of disease. To test this, we performed a SPARQL query for factor value in-
dividuals which are not types of disease where the factor type was ‘disease state.
The results of this query reveal two findings. Firstly, that there are annotations
14
http://identifiers.org/ensembl/ENSMUSG00000034353
15
http://www.ncbi.nlm.nih.gov/biosystems/106379
of the type ‘normal and ‘normospermic’ (normal sperm levels) annotated under
‘disease state’ within the data; this may or may not be an error in annota-
tion, since, as previously discussed normal is contextual - but it is arguable that
normal should not be considered a disease state. Secondly, it highlights annota-
tions which are not types of disease but should be, that is gaps in coverage in
the ontology. For example, ‘rectovaginal endometriosis’ and ‘Juvenile Idiopathic
Scoliosis’ were two such annotations both of which represent missing coverage
in the ontology.
4 Conclusion
The primary goal of this work was to enable new biologically interesting queries
to be asked of the data in the Gene Expression Atlas by integration with external
resources including protein databases and pathways. This work demonstrates
that a lot can be gained from relatively simple integration between resources
and, moreover, that the key biological entities of genes and proteins can be seen
as anchors which connect a lot of biomedical data. The addition of ontologies
such as EFO and GO allow richer, more expressive queries to be asked of the
data - made possible because the data are curated with such ontologies, thereby
demonstrating the power of well-annotated data.
The choice of model here is relatively minimal and reuses existing concepts
where possible. Our initial approach has been to import a lot of the data into
the RDF store but the longer term aim is to use federated querying to integrate
these data. New SPARQL endpoints at EBI for Genome Wide Association Study
(GWAS), ChEMBL and BioModels, along with the offering from the NCBO will
all provide more integration points. The GXA RDF is a starting point towards
a more integrated RDF offering from resources at EBI. Our approach to this is
agile; we develop in small iterations, document and refine with users involved at
each stage. In this respect, engineering RDF is no different from good software
engineering practices.
Future work will include extending the model to capture more general infor-
mation about a microarray experiment. The end goal will be to provide RDF
data for all experiments in the ArrayExpress archive. This will require more
engagement with the community through efforts such as the HCLS, to generate
agreement on the model and terminology. In particular, through our integration
with the SIO we would like to explore how we can expose our service to semantic
service platform such as SADI [37].
Whilst SPARQL provides us with a low level query language for exploring and
mining the data, this form of advanced API will only be directly useful to a small
subset of our users. The next step is to develop new user interface components
that allow the user to exploit these rich data connections, whilst at the same
time shielding the user from the underlying technology. Previous work [38] has
shown that without developing user facing tools for biologist, much of the added
value in the RDF is not available to most users. We are currently developing
tools to do gene set enrichment analysis based on the GXA annotations and
an improved user interface to allow better interaction with our biologist target
audience.
5 Acknowledgements
We acknowledge funds from EMBL (JM, HP) and The National Center for
Biomedical Ontology, one of the National Centers for Biomedical Computing
supported by the NHGRI, the NHLBI, and the NIH Common Fund under grant
U54-HG004028 (SJ). We are also grateful for discussion and comments from
Tony Burdett, Maryam Soleimani, Johan Rung at the EBI, Robert Petryszak,
Eleanor Williams and Maria Keays and the Gene Expression Atlas production
team, and Michel Dumontier and members of the HCLS interest group.
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