=Paper=
{{Paper
|id=None
|storemode=property
|title=Analysis of Cancer Omics Data In A Semantic Web Framework
|pdfUrl=https://ceur-ws.org/Vol-698/paper1.pdf
|volume=Vol-698
|dblpUrl=https://dblp.org/rec/conf/swat4ls/HolfordMCK10
}}
==Analysis of Cancer Omics Data In A Semantic Web Framework==
https://ceur-ws.org/Vol-698/paper1.pdf
Analysis of Cancer Omics Data In A Semantic
Web Framework
Matthew E. Holford1 , James P. McCusker2 , Kei-Hoi Cheung3,4,5 , and Michael
Krauthammer1,2
1
Interdepartmental Program in Computational Biology & Bioinformatics,
2
Department of Pathology,
3
Department of Computer Science,
4
Center for Medical Informatics
5
Department of Genetics
Yale University
New Haven, CT
Abstract. Our work concerns the elucidation of the cancer (epi)genome,
transcriptome and proteome to better understand the complex interplay
between a cancer cell’s molecular state and its response to anti-cancer
therapy. To study the problem, we have previously focused on data ware-
housing technologies and statistical data integration. In this paper, we
present recent work on extending our analytical capabilities using Seman-
tic Web technology. A key new component presented here is a SPARQL
endpoint to our existing data warehouse. This endpoint allows the merg-
ing of observed quantitative data with existing data from semantic knowl-
edge sources such as Gene Ontology (GO). We show how such variegated
quantitative and functional data can be integrated and accessed in a uni-
versal manner using Semantic Web tools. We also demonstrate how De-
scription Logic (DL) reasoning can be used to infer previously unstated
conclusions from existing knowledge bases. As proof of concept, we illus-
trate the ability of our setup to answer complex queries on resistance of
cancer cells to Decitabine, a demethylating agent.
1 Introduction
The Yale Specialized Program in Research Excellence (SPORE) in skin cancer
is a large translational cancer project, which aims at rapidly moving biological
insights from the “bench to bedside”. As part of the effort, the SPORE collects
skin cancer samples from mostly malignant melanoma patients and performs a
multitude of Omics studies, probing the melanoma genome, epigenome, tran-
scriptome and proteome. The idea is to integrate this data with clinical outcome
information to derive prognostic and predictive biomarkers, i.e. genomic markers
that predict patient survival and drug therapy effectiveness, respectively. Con-
ventionally, these markers are either derived statistically in an unbiased fashion
[33], or by prior knowledge and candidate (gene) selection [17]. We are interested
in combining these approaches, and are developing means for unbiased assess-
ment of Omics data using existing knowledge on cellular processes that affect
2 M. Holford et al.
drug effectiveness. In particular, we are employing Semantic Web technology to
create RDF graphs that define the genomic state of cancer cells and the func-
tional annotation of the cells’ molecular entities (i.e. genes or proteins). We use
SPARQL to query these graphs to better understand the molecular basis of drug
resistance and sensitivity.
We start by retrieving quantitative data from a large relational database,
a component of the Corvus architecture [19], storing melanoma Omics data.
We do this by providing a new semantic component of Corvus, a SPARQL
endpoint which relies upon Hibernate 1 for Object Relational Mapping (ORM).
Through this endpoint, we can dynamically create RDF graphs of the data stored
within. We then merge such graphs with SKOS-converted Gene Ontology (GO)
[1] information to annotate genomic elements with functional data, such as their
involvement in certain cellular processes.
As a case study, we used the new Corvus SPARQL endpoint to create an RDF
graph with data representing drug response to Decitabine, a demethylating agent
that has been shown to be clinically active in melanoma [16]. Using SPARQL, we
queried Corvus for melanoma samples with information on promoter methylation
status and gene expression before and after Decitabine treatment. The resulting
graph is augmented with functional annotations from GO. It is then interrogated
for the molecular mechanisms explaining why some samples have better response
to Decitabine treatment than others.
2 Methods
To attain these goals, we needed to build a model that integrated quantitative
Omics data with functional information. Our model incorporates gene expression
and methylation data for seven melanoma cell lines [13]; it also contains Gene
Ontology (GO) annotations for the whole of the human genome. Expressing this
model as an RDF triple store affords us a number of advantages. First, it provides
a way for others to borrow from and build upon our work. It allows us to use the
standardized SPARQL interface to perform queries that bridge quantitative and
functional knowledge. It also gives us the capability to infer previously unstated
information by reasoning over the data with a Semantic Web aware Description
Logic (DL) reasoner. We attempted wherever possible to borrow terms from well-
established OBO ontologies [30]. Doing so places our work under the auspices of
community defined best practice and allows our model to be used in conjunction
with similarly designed knowledge bases. Building the model involved the use of
a variety of cutting-edge Semantic Web technologies and required the creation
of several novel tools. The work proceeded along two major lines: (i). Conversion
of relational data from melanoma cell lines to RDF/OWL and (ii). Integration
of specific gene annotations with the Gene Ontology.
The issue of integrating quantitative and functional biological information to
infer relevant new information has been frequently explored. A notable example
1
http://www.hibernate.org
Semantic Web Reasoning on Translational Data 3
is HyBrow, a tool for the generation and evaluation of biological hypotheses [24].
The user can derive hypotheses from HyBrow’s knowledge base of functional bio-
logical information and test them against various high-throughput data sources.
BioBIKE offers an environment for users to integrate a wide variety of experi-
mental and genomic data to reach new conclusions [11]. Originally released as
a LISP interactive library [18], the software is now web-based to accommodate
users lacking in programming expertise. When combined with the BioDeducta
module, it enables automated reasoning [28]. Although both HyBrow and Bio-
BIKE make extensive use of ontologies, neither is Semantic Web enabled. Recent
efforts by the National Cancer Institute as part of the caBIG initiative [12] have
focused on addressing the integration issue though the use of an Extraction-
Transform-Load (ETL) strategy. Notably, the caIntegrator2 2 project uses ETL
to integrate quantitative Omics data from caArray [14] and functional biological
data from caBio [9]. The Bio2RDF project is notable for providing normalized
URIs for a wealth of identifiers and relationships from functional biology in the
hopes of allowing easier integration of diverse data sets [4].
2.1 Quantitative Data From Melanoma Cell Lines
We examined data derived from seven melanoma cell lines (WW165, YUMAC,
YUGEN8, YUSAC2, YUSIT1, YULAC and YURIF). These lines have been ex-
perimentally classified using IC50 values from dose-response analysis as being ei-
ther sensitive to (YUMAC, YUSAC2, YULAC, YUSIT1, YUGEN8) or resistant
to (WW165, YURIF) decitabine (5-Aza-2’-deoxy-cytidine, Aza), a DNA methyl-
transferase inhibitor. Specifically we looked at relative methylation values prior
to administration of AZA and the ratio of gene expression following administra-
tion of AZA to before. The methylation values were obtained from a Nimblegen
promoter array using the Methyl-DNA immunoprecipitation (MeDIP) technique
[22, 23]. Gene expression ratios were obtained using a custom 2-channel Nimble-
gen array. Data from both arrays are available for download through ArrayEx-
press 3 . We used the Gene Element Ontology (GELO) to align the array probes
to RefSeq identifiers [32].
Rationale for building a SPARQL endpoint These data were stored in
a relational database component of Corvus, a data warehouse for experimen-
tal data, which currently holds over 4 million observations from diverse Omics
experiments across melanoma cell lines. Presently, Corvus exists as a Java li-
brary with object-relational mapping (ORM) accomplished through Hibernate.
Quantitative cancer omics data is stored in a standard database schema spec-
ified by the ORM. We present here a new semantic interface to Corvus which
retrieves data in the form of RDF triples. Unfortunately, the sheer volume of
data contained within our local Corvus database would result in a triple store of
such size as to be untenable for the purposes of DL reasoning. What was needed
2
http://cabig.nci.nih.gov/tools/caIntegrator2
3
http://www.ebi.ac.uk/arrayexpress/
4 M. Holford et al.
instead was a way to retrieve a subset of the Corvus model containing only the
information essential to the problem at hand. Ideally this could be accomplished
in a dynamic fashion.
Integration of traditional relational databases with RDF has been extensively
explored in recent years [27]. Typically the approach is to create a generic map-
ping between relational and RDF schema. This has been done either through
automatic mappings, where relational tables correspond to RDFS classes and re-
lational columns to RDF predicates [6], or with domain-specific semantics [26].
Some tools, such as d2rq [5], provide for both and allow user customization for
complex cases such as when mappings are not one-to-one. Mappings may be
stored in a variety of formats, ranging from XML configuration files to custom
languages such as R2O [2]. These mapping artifacts can then be used to dy-
namically generate SQL queries to the relational database based upon queries
expressed according to the RDF schema, usually using SPARQL.
We experimented directly with the d2rq framework, which allows a relational
database to be queried like a triple store using SPARQL. Using a configuration
file to map Corvus database fields to RDF properties, we were able to generate
SPARQL queries that retrieved a manageable subset of the Corvus database.
However, we found that the SQL generated by the tool to query the relational
database was inefficient and data retrieval took longer than expected. We de-
cided instead to leverage the Hibernate mappings already part of the Corvus
model to interact with the relational database. We wrote a SPARQL interface
to the Corvus model which interacts directly with the Java library, taking ad-
vantage of Hibernate’s ability to optimize and cache relational queries. To the
best of our knowledge, although the issue of mapping SPARQL to object ori-
ented representations such as Hibernate has been discussed [7, 15], no tools for
doing this have been released to the public. Our approach is to create wrapper
classes around the Hibernate mapping classes which map the property getters to
RDF predicates. Indirect mappings make possible situations in which the RDF
and relational schemas do not correspond one to one. Though this approach is
not necessarily a universal solution, we felt that given Corvus’ ability to repre-
sent such a broad swathe of Omics data, the performance gain offered by these
customized mappings more than justified the up-front expense of their creation.
Corvus model to RDF mapping We mapped fields from the Corvus database
to classes and relationships from OBO ontologies. In particular, we employed
terms from Information Artifact Ontology (IAO) 4 and Ontology for Biomedical
Investigations (OBI) [8]. In addition to being actively developed, these ontolo-
gies are notable for building upon the foundation Basic Formal Ontology (BFO)
5
and the OBO Relation Ontology (RO) [31] which were specially designed to
be extensible by any biomedical ontology. This allows our modeled Corvus data
to be incorporated with other OBO ontologies with relative ease. It should be
4
http://code.google.com/p/information-artifact-ontology
5
http://www.ifomis.org/bfo
Semantic Web Reasoning on Translational Data 5
noted that we are simply borrowing terms from these ontologies, not incorpo-
rating them in their entirety as doing so would have a significant deleterious
effect on reasoning performance. This does not pose a hindrance to our goals
as we do not need to make inferences across the whole hierarchy of terms in
these ontologies. By using the terms, however, we provide an entry point for
others who may wish to explore this type of inferencing in the future. Quan-
titative data storage in the Corvus model is centered around the Observation
class. Instances of this class represent individual data points in a collection of
data, such as an array. They contain the numerical value of the data as well as
pointers to other classes indicating the type and provenance of the data. These
other classes include Dataset, which holds metadata on experimental conditions,
Measure, which specifies details about the type of data being measured, Sample,
which describes the cell line being measured and Reporter, the genomic feature
(typically a gene) for which data is being reported. We mapped Measurement to
the IAO class measurement datum and used the IAO data property has measure-
ment value to associate numerical data values. Dataset was linked to the IAO
class data set. Individual Observations can be specified as belonging to a Dataset
using the RO property part of. Samples were declared as instances of the OBI
class cell culture. Association of an Observation with a Sample was done using
IAO’s is about property. Reporter was linked to the Genomic Region class from
the GELO ontology. This class is defined as a superclass of the OBO Sequence
Ontology’s (SO) [10] biological region class and it attaches properties to assign a
reference location for a genomic element within the genome. For the purposes of
our data, Reporter s were made instances of SO’s transcript class, as the reference
sequence (RefSeq) was used. We used the Uniform Resource Identifiers (URIs)
for RefSeq sequences provided by the Bio2RDF project. Using this normalized
identifier allows us to easily link with other resources describing the same genes.
To capture information from the Corvus Measure class, instead of mapping to
an instance of a class, we forwarded two of Measure’s fields to properties in the
domain of measurement datum. These were the IAO is quality measurement of
property and the IAO has measurement unit label property. Finally, we used
the Dublin Core [34] annotation properties title and identifier to assign names
for Samples, Datasets and Reporters and reference identifiers to Reporters. A
detailed view of this model is provided in figure 1.
Querying the Corvus SPARQL endpoint To retrieve a subset of our Corvus
database that was sufficient for our ultimate querying purposes, we issued a
SPARQL query that would retrieve all relevant information for the seven cell
lines mentioned above. We used a SPARQL DESCRIBE query which simply
returns all relevant properties for a type into an RDF graph. Our query retrieves
all Observations associated with the cell lines and pulls in information on the
lines and experimental conditions from the Sample and Dataset tables and all
genes with values from the Reporter table. We issued the following SPARQL
query for each of the seven cell lines:
PREFIX obo:
6 M. Holford et al.
PREFIX dc:
PREFIX ro:
DESCRIBE ?rep ?obs ?data ?samp
WHERE {
?samp dc:title ‘‘YUMAC’’ .
# IAO_0000136 = ’is_about’
?obs obo:IAO_0000136 ?samp .
?obs ro:part_of ?data .
?obs obo:IAO_0000136 ?rep .
}
Retrieval of a populated RDF graph containing the approximately 120,000 obser-
vation for a cell line using our Hibernate-based mapping typically took between
one and two minutes.
Fig. 1. Diagram showing Java classes in the Corvus model (orange boxes) next to their
corresponding OWL classes (blue boxes). Data or annotation properties are shown as
gray ellipses. Edge labels show the Java method used to call the Corvus model in red
and the RDF property used in the semantic model in blue.
Semantic Web Reasoning on Translational Data 7
2.2 Annotated GO Terms
To include functional information about genes, we decided to incorporate the
well-known Gene Ontology (GO). GO is presented in the OBO format, a simple
model for expressing hierarchies of terms and the relationships between them.
Although significantly less powerful for inferencing than a fully DL-compatible
language like OWL, the OBO language makes it straightforward to declare rela-
tionships between classes of object. We found an effective compromise to be the
use of the Simple Knowledge Organization System (SKOS) [21]. In this ontol-
ogy, written in OWL, terms such as those in OBO taxonomies are expressed as
instances of a Concept class. Class subsumption is handled though OWL object
properties that describe Concepts as broader or narrower than other Concepts.
In this system, properties can be assigned easily to class-like terms without vi-
olating the strictures of OWL-DL. This approach offers significant advantages
for querying and reasoning, as the common alternative, creation of restrictions
on classes, is computationally expensive while still requiring the creation of in-
dividual instantiations to infer properties. Using the OBO to SKOS conversion
tools developed at University of Manchester 6 , we created a GO-SKOS ontol-
ogy which converts GO terms to instances of Concept and is a relationships to
broader relationships.
We downloaded the standard human genome annotations provided by the
Gene Ontology consortium. In order to easily merge with our Corvus graph, we
converted the GO annotation file’s HUGO symbols to RefSeq identifiers using
conversion tables made available from Entrez 7 and used the Bio2RDF nor-
malized URIs. In fitting with the Corvus model, we cast individual refseqs as
instances of the SO:transcript class. We then used three basic relationships from
RO to link the gene to its appropriate term in whichever of GO’s three main
hierarchies. Genes annotated with a Biological Process term were linked using
participates in; those labeled as expressing a Molecular Function were linked
using has function and genes marked as being located in a particular Cellular
Component were linked using part of. We also wished for the properties assigned
to genes to propagate up the chain of hierarchy. In other words, if a particular
gene participates in a specific biological process, we wanted the reasoner to be
able to infer that it also participates in the more generic process. For example,
genes participating in apoptosis also participate in the more general process of
cell death and in biological processes in general. To accomplish this, we used an
OWL property chain, a new feature in OWL 2, to associate participates in with
broader, stating that if A participates in B and C is a broader concept than B,
then A participates in C as well. This type of inference is possible because the is-
a (subsumption) relationship between SKOS concepts is a relationship between
individuals rather than between classes. The relationship is illustrated in figure
2. We made the same declarations for the has function and part of properties.
With these declarations in place we were able to run the ontology through
a DL reasoner and create a greatly expanded set of RDF triples with all infer-
6
http://www.cs.man.ac.uk/ sjupp/skos
7
http://www.ncbi.nlm.nih.gov/Entrez
8 M. Holford et al.
Fig. 2. Diagram showing the propagation of the participates in property up the class
subsumption hierarchy. This inference is achieved by using an OWL 2 property chain
associating the participates in property with the SKOS broader property.
Semantic Web Reasoning on Translational Data 9
ences spelled out (i.e. all annotation properties propagated along the hierarchy).
There is a trade-off here as we gain faster query times by precomputing all in-
ferences at the expense of additional storage space and less flexibility, as we
need to recompile when the underlying data changes. Creation of the fully en-
tailed GO annotation RDF graph took approximately five minutes on our Linux
workstation using 8 GB of memory.
Merging of RDF graphs The GO annotation model could at this point be
merged with the Corvus quantitative data model, the points in common being
the instances of SO transcript representing individual RefSeqs/genes. Because
we use identical URIs from the Bio2RDF namespace to describe these instances,
we can assure that we are referring to the same gene in the two sources. This
merged model could now be queried using SPARQL. The full architecture of
our setup for creating an RDF graph from Corvus and merging it with the GO
graph is shown in figure 3.
The Corvus SPARQL endpoint Application Programming Interface (API)
was written in Java making extensive use of the Jena API for RDF manipulation
and the closely related ARQ API for SPARQL processing 8 . The GO Annota-
tion pre-processing was handled by a Java program making use of the OWLAPI
OWL2 library [3] and the Pellet DL reasoner for Semantic Web data [29]. Merg-
ing of the ontologies was also handled by Java code using first the ARQ API to is-
sue the SPARQL query on the relational Corvus store and then OWLAPI to per-
form the actual merge. The merged dataset was loaded into an instance of TDB,
an RDF triple store employing the Jena libraries. It was then loaded into a run-
ning instance of Joseki, a web application allowing execution of SPARQL queries
over HTTP. Joseki also uses the Jena libraries extensively. An endpoint for the
merged dataset is available at http://doppio.med.yale.edu:2020/sparql.
3 Results and Discussion
We wanted to show that it was possible to use Corvus to execute arbitrarily com-
plex queries incorporating information across varied knowledge domains. To this
end, we tried to verify cell lines that were resistant or sensitive to Decitabine, a
demethylating agent used for melanoma therapy. Our formulated query asks for
genes involved in apoptosis with high methylation values prior to Decitabine
administration and increased gene expression following. We use values from
two datasets obtained from the Corvus SPARQL endpoint, relative methylation
values prior to treatment and ratio of gene expression post- to pre-treatment.
Apoptosis-related genes were found using the merged triples from the GO anno-
tations. Our SPARQL query was as follows:
PREFIX dc:
PREFIX ro:
8
http://jena.sourceforge.net
10 M. Holford et al.
Fig. 3. Diagram showing the architecture of the integrated model we used to perform
the queries in this paper.
Semantic Web Reasoning on Translational Data 11
PREFIX obo:
PREFIX go:
SELECT distinct ?rep ?samp
WHERE {
?ds dc:title "Methylation Relative" .
?obs ro:part_of ?ds .
# IAO_0000004 = ’has_measurement_value’
?obs obo:IAO_0000004 ?obsVal .
# IAO_0000136 = ’is_about’
?obs obo:IAO_0000136 ?rep .
?obs obo:IAO_0000136 ?samp .
# OBI_0100060 = ’cell celture’
?samp a obo:OBI_0100060 .
?ds2 dc:title "AZA Pre-Post Treatment Ratios" .
?obs2 ro:part_of ?ds2 .
?obs2 obo:IAO_0000136 ?rep .
?obs2 obo:IAO_0000136 ?samp .
?obs2 obo:IAO_0000004 ?obsVal2 .
?rep ro:participates_in go:0006915 .
FILTER ( ?obsVal > 2 ) .
FILTER ( ?obsVal2 > 1 )
}
This query returns the URIs of genes and cell lines that match the aforemen-
tioned criteria. Using features from the recently standardized SPARQL 1.1, we
can aggregate genes by cell line to get a count of highly expressed genes per cell
line. The slightly modified SPARQL query is:
PREFIX dc:
PREFIX ro:
PREFIX obo:
PREFIX go:
SELECT (count(?rep) as ?repcount) ?samp
WHERE {
?ds dc:title "Methylation Relative" .
?obs ro:part_of ?ds .
# IAO_0000004 = ’has_measurement_value’
?obs obo:IAO_0000004 ?obsVal .
# IAO_0000136 = ’is_about’
?obs obo:IAO_0000136 ?rep .
?obs obo:IAO_0000136 ?samp .
# OBI_0100060 = ’cell celture’
?samp a obo:OBI_0100060 .
?ds2 dc:title "AZA Pre-Post Treatment Ratios" .
?obs2 ro:part_of ?ds2 .
12 M. Holford et al.
?obs2 obo:IAO_0000136 ?rep .
?obs2 obo:IAO_0000136 ?samp .
?obs2 obo:IAO_0000004 ?obsVal2 .
?rep ro:participates_in go:0006915 .
FILTER ( ?obsVal > 2 ) .
FILTER ( ?obsVal2 > 1 )
} GROUP BY (?samp)
We can compare these counts to what we know from experimental data
regarding the level of sensitivity/resistance of various cell lines [13]. The re-
sults are shown in the following table: The sensitive cell lines with low IC50
Cell Line Gene Count IC50 (nM)
YUMAC 22 34
YUSAC 7 91
YULAC 9 110
YUSIT1 2 132
YUGEN8 6 139
WW165 2 239
YURIF 0 255
Fig. 4. Table showing the seven melanoma cell lines, the total number of apoptosis-
related genes positively expressed that were formerly methylated and the IC50 value.
values (YUMAC, YUSAC and YULAC) had the three highest gene counts,
whereas the two most resistant lines (WW165 and YURIF) had the lowest.
As the mechanism of Decitabine action is demethylation of gene promoters, and
(re)expression of the corresponding genes, these results give rise to the follow-
ing hypothesis: Decitabine targets apoptosis-related gene promoters predomi-
nantly in Decitabine-sensitive cell lines, thus conveying its cytotoxic effect by
activating the apoptosis pathway. The following validation steps are warranted
to strengthen the hypothesis: First, one might want to independently test in
vitro both the demethylation of the implicated gene promoters, as well as the
re-expression of the corresponding genes. Also, the finding should be repeated
in a larger cohort of melanoma samples. A current limitation of our SPARQL
query is that we only interrogate for fold change after Decitabine treatment. As
shown in prior work, the absolute change in expression values after treatment
should also be taken into account [25].
4 Conclusion
Our proof of concept query illustrates how easily data from various sources can
be integrated using the common framework of OWL/RDF. It reveals some of the
power of Semantic Web reasoning and querying tools for inferring and elucidating
discovered knowledge. It also shows the importance of customization in mapping
Semantic Web Reasoning on Translational Data 13
non-semantic data to RDF. While generic tools mapping relational data to RDF
have recently emerged, our experience with d2rq has shown that there are still
areas where direct mapping is significantly more efficient and flexible. Our work
also makes a strong case for the benefits of using linked data, as use of the
Bio2RDF normalized URI for RefSeqs made integration of the two branches of
our ontology a breeze.
The flexibility of the Corvus model will allow us to incorporate quantitative
Omics data from a variety of modalities. In the future, this could include cancer
data from caArray or caIntegrator or data obtained directly from ArrayExpress
using MAGETab2RDF [20]. Essentially, Corvus functions as a contextualized
observation repository and we intend to incorporate information from other con-
texts including clinical data and generic provenance data. We hope to use the
new semantic access point to Corvus to integrate this data with other types of
information such as pathway and pharmacological data. The simplicity and el-
egance of the integrated Semantic Web approach also suggests its usefulness as
an access point to making sense of variegated data for researchers unequipped
with the programming or mathematical expertise to work with traditional data
mining tools.
Acknowledgments. This work has been supported by the National Cancer
Institute (Yale SPORE in skin cancer - 5P50CA121974) and the National Li-
brary of Medicine (Yale Biomedical Informatics Research Training Program -
5T15LM007056).
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