Automated discovery of candidate biomarkers from multiple databases has been the central challenge in the Life Sciences in general and in the study of systemic processes such as those documented by The Cancer Genome Atlas (TCGA) in particular. &#xd;&#xa;&#xd;&#xa;The maturation of Semantic Web technologies offers solutions to those problems by allowing the query to be defined by navigating a normally represented domains of discourse instantiated by the data. &#xd;&#xa;&#xd;&#xa;We address the systems challenge of The Cancer &#xd;&#xa;Genome Atlas initiative (http://cancergenome.nih.gov/), which generates a large scale repository of high throughput molecular biology data generated and processed at 5 academic facilities across the USA

Helena Deus

English

Nature Precedings

Conclusions
Exposing The Cancer Genome Atlas (TCGA) as a SPARQL endpoint 
Helena F Deus1,2, Diogo F Veiga1, Pablo R Freire1, Kadir Akdemir1, Tiago L Carvalho1, Bradley M Broom1, John Weistein1 and Jonas S Almeida1
1The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computational Biology, 1515 Holcombe Blvd, Texas, USA 
2Instituto de Tecnologia Química e Biológica, Oeiras, Portugal
Heading
Methods
Using The Cancer Genome Atlas as a case study, and 
the S3DB (1,2,3) application as the engine of 
integration, we developed a data model / ontology to 
integrate the clinical and molecular data and expose 
them as Semantic Web Services. 
Since sensitive and proprietary data is always a 
preoccupation with this type of studies, the core data 
model includes the management of user permissions 
on individual data elements.
The architecture for this novel resource provides a 
template for web-based solutions that bridge between 
data silos within a domain of knowledge and between 
the bench and the clinical point of care. 
References:
[1] "Comprehensive genomic characterization defines human glioblastoma genes and core pathways," Nature, vol. 455, pp. 1061-8, Oct 23 2008.
[2] L. Chin and J. W. Gray, "Translating insights from the cancer genome into clinical practice," Nature, vol. 452, pp. 553-63, Apr 3 2008.
[3] J. S. Almeida, C. Chen, R. Gorlitsky, R. Stanislaus, M. Aires-de-Sousa, P. Eleuterio, J. Carrico, A. Maretzek, A. Bohn, A. Chang, F. Zhang, R. Mitra, G. B. Mills, X. Wang, and H. F. Deus, 
"Data integration gets 'Sloppy'," Nat Biotechnol, vol. 24, pp. 1070-1, Sep 2006.
[4] H. F. Deus, R. Stanislaus, D. F. Veiga, C. Behrens, I. I. Wistuba, J. D. Minna, H. R. Garner, S. G. Swisher, J. A. Roth, A. M. Correa, B. Broom, K. Coombes, A. Chang, L. H. Vogel, and J. 
S. Almeida, "A Semantic Web Management Model for Integrative Biomedical Informatics," PLoS ONE, vol. 3, p. e2946, 2008.
Figure 5. Permission management by embedding the 
corresponding propagation model in S3DB’s core architecture, 
See Deus et. al 2008 for an illustrative application where this 
feature is used to “incubate” a shared data store.
Background
• Data, data everywhere: Automated discovery of 
candidate biomarkers from multiple databases has been 
the central challenge in the Life Sciences in general and 
in the study of systemic processes such as those 
documented by The Cancer Genome Atlas (TCGA) in 
particular. 
• Foundations for a better solution: The maturation of 
Semantic Web technologies offers solutions to those 
problems by allowing the query to be defined by 
navigating a formally represented domains of discourse 
instantiated by the data. 
We address the systems challenge of The Cancer 
Genome Atlas initiative (http://cancergenome.nih.gov/), 
which generates a large scale repository of high 
throughput molecular biology data generated and 
processed at 5 academic facilities across the USA [1, 2]. 
Currently, the heterogeneity of domains (genomic, 
transcriptomic, epigenetic effects, proteomic, clinic and 
demographic, etc) that are part of the TCGA data is 
aggregated at a single point of access charged with 
providing syntactic interoperability to all of the data – the 
TCGA portal.
The objective of this work is therefore the exposure of 
the highly heterogeneous TCGA data from various 
sources through RESTful SPARQL endpoints.
Objectives
Figure 2. Identification of TCGA data 
elements: Breaking the TCGA datasets into their 
individual data elements is not a trivial task. The 
data elements first need to be extracted from 
their original hierarchical sources before they 
can be represented as RDF triples. For example, 
the path to raw data files in an FTP server (1) is 
separated into its constituent slash-separated 
portions and the resulting data elements, 
according to their position in the path, are 
assigned to values of statements. For example, 
the string “ht_hg-u133a” is assigned as the 
object of a statement where the subject is a URI 
identifying an instance of a platform and the 
predicate is the URI of the rule that links S3DB 
collection “Platform” and attribute “Platform 
Code.” Similarly, each analyte is identified by a 
barcode (2), which broken down into its 
constituent elements renders the values for 
attributes Sample ID, Sample Type and Sample 
Collection Center Code, among others.
Figure 1. A caching service for TCGA 
archives: The TCGA datasets are typically 
compact assemblies of data elements with 
various levels of structure, of which the 
largest elements are the “archives” where 
the raw data is contained. To overcome the 
problem of having to bulk download each 
large archive, a caching service was 
developed to find and retrieve the latest 
version of an array from the TCGA archives 
given a set of attribute-value pairs (for 
institution, platform and sample (1)). The 
dynamic link generator, made publicly 
available at 
http://tcga.s3db.org/TCGAsync.php, 
recursively browses the TCGA datasets to 
return the raw data file corresponding to the 
correct array (2.1). If the archive has been 
compressed and the raw data file is not 
available as a symbolic link (2.2), then the 
TCGA archive is first downloaded and 
decompressed and the correct array is 
returned. It is not uncommon for an array to 
be requested multiple times. When the array 
is not being requested for the first time, the 
correct raw data file is downloaded from the 
caching service.
root:transcriptomic
root:genomic
root:epigeneticEffects
root:proteomic root:clinical
S3QL
@prefix root: <http://root.s3db.org>
root:TCGA
S
3Q
L
S3QL
S3QL
S3QL
Figures 3 and 4. Data distribution in multiple S3DB 
deployments by serialization of SPARQL: each S3DB 
deployment (in green) is equipped with a RESTfull API (in 
yellow) through which data is programmatically accessed / 
inserted / updated using the S3DB query language (S3QL). 
Additionally, deployments are uniquely identified by URI which 
are resolved at a unique root location (http://root.s3db.org, in 
red). This simple system of resolving the URL of the 
deployments based on their unique identifiers  assists the 
process of finding data elements which might be stored in S3DB 
deployments other than the one that was queried. When a query 
is performed where a data element that is stored in another 
deployment is requested, S3DB will perform the necessary 
S3QL query, 
Privacy concerns and data sharing issues are solved by relying 
on the S3DB permission management model. User 
authentication need only occur at the deployment where the user 
has been registered. Once authenticated, a temporary key may 
be generated which is migrated with the serialized S3QL 
queries, providing the other deployments with the users 
credentials such that a level of permission may be determined. 
Each S3DB deployment is also a SPARQL endpoint. Therein, 
SPARQL triples are serialized into S3QL queries, which are then 
used to query any number of deployments simultaneously. The 
triple patterns in the SPARQL query mimic the definition of the 
domain rules by replacing the predicate portion of the rule triple 
with its identifier.
Figure 4. An 
interface for TCGA 
SPARQL endpoint: 
When the RDF 
representation 
includes a separation 
between the domain 
and its instantiation, 
navigating the 
domain to produce 
SPARQL triples is 
straightforward. This 
structure is imposed 
on the RDF 
representation of the 
data by the S3DB 
Core morel. Available 
at http://tcga.s3db.org
<GenomicCharacterization> <obtainedFrom> <Sample>.
<Sample> <obtainedFrom> <Patient> .
<Patient> <treatedAt > <SampleCollectionCenter > .
<SampleCollectionCenter> <name> “MD Anderson” .
?GenomicCha racterization :R3979 ?Sample .
?Sample :R247 ? Patient .
?Patient :R47487 ?SampleCollectionCenter .
?SampleCollectionCenter :R44596 ?Name
FILTER regex(?Name, “MD Anderson”) .
<select>*</select>
<from>statements</from>
<where>
<rule_id>44596</rule_id>
<value>MD Anderson</value>
</where>
@prefix : <http://tcga.s3db.org>
Rules
SPARQL
<select>*</select>
<from>statements</from>
<where>
<rule_id>3979</rule_id>
</where>
<select>*</select>
<from>statements</from>
<where>
<rule_id>247</rule_id>
</where>
<select>*</select>
<from>statements</from>
<where>
<rule_id>47487</rule_id>
</where>
(a)
(b)
(c)
(d)
Domain
Query
Serialization
Parallelized S3QL
(d)
(a)
(b)
(c)
S
P
A
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s
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3
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www.s3db.org, Nature Biotechnology (2006) 
24(9):1070-1071, PLoS ONE 3(8): e2946. 
doi:10.1371/journal.pone.0002946
rdfs:subClassOf
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:
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s
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rdfs:subClassOf rdfs:subClassOf CollectionrojectP Item
[Cid] [Iid] [Cid or L]
rdf:object
rdf:predicate
rdf:subject
rojectP
Deployment
Deployment
Unique Identifiers of entities:
Durl rdf:type s3db:Deployment
Pid rdf:type s3db:Project
Cid rdf:type s3db:Collection
Rid rdf:type s3db:Rule
Sid rdf:type s3db:Statement
Iid rdf:type s3db:Item
Uid rdf:type s3db:User
Gid rdf:type s3db:Group
rdfs:subClassOf
rdf:predicate
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dc:created_by Uid
dc:created_on date
dc:service {term of cv}
etc …
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Rule Statement
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rdf:object
rdf:predicate
rdf:subject
S3DB Entity (annotated using DC)
Relationship (defined using RDFS)
Permission (defined by s3db:permission)
Annotation of s3db entities:
Needed only if 
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[1] &quot;Comprehensive genomic characterization defines human glioblastoma genes and core pathways,&quot;

A Semantic Web Management Model for Integrative Biomedical Informatics,&quot;

Data integration gets 'Sloppy',&quot;

Exposing The Cancer Genome Atlas (TCGA) as a SPARQL endpoint

AbstractAutomated discovery of candidate biomarkers from multiple databases has been the central challenge in the Life Sciences in general and in the study of systemic processes such as those documented by The Cancer Genome Atlas (TCGA) in particular.The maturation of Semantic Web technologies offers solutions to those problems by allowing the query to be defined by navigating a normally represented domains of discourse instantiated by the data.We address the systems challenge of The Cancer Genome Atlas initiative (http://cancergenome.nih.gov/), which generates a large scale repository of high throughput molecular biology data generated and processed at 5 academic facilities across the USA.</jats:p

Crossref

ConclusionsExposing The Cancer Genome Atlas (TCGA) as a SPARQL endpoint Helena F Deus1,2, Diogo F Veiga1, Pablo R Freire1, Kadir Akdemir1, Tiago L Carvalho1, Bradley M Broom1, John Weistein1 and Jonas S Almeida11The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computational Biology, 1515 Holcombe Blvd, Texas, USA 2Instituto de Tecnologia Química e Biológica, Oeiras, PortugalHeadingMethodsUsing The Cancer Genome Atlas as a case study, and the S3DB (1,2,3) application as the engine of integration, we developed a data model / ontology to integrate the clinical and molecular data and expose them as Semantic Web Services. Since sensitive and proprietary data is always a preoccupation with this type of studies, the core data model includes the management of user permissions on individual data elements.The architecture for this novel resource provides a template for web-based solutions that bridge between data silos within a domain of knowledge and between the bench and the clinical point of care. References:[1] "Comprehensive genomic characterization defines human glioblastoma genes and core pathways," Nature, vol. 455, pp. 1061-8, Oct 23 2008.[2] L. Chin and J. W. Gray, "Translating insights from the cancer genome into clinical practice," Nature, vol. 452, pp. 553-63, Apr 3 2008.[3] J. S. Almeida, C. Chen, R. Gorlitsky, R. Stanislaus, M. Aires-de-Sousa, P. Eleuterio, J. Carrico, A. Maretzek, A. Bohn, A. Chang, F. Zhang, R. Mitra, G. B. Mills, X. Wang, and H. F. Deus, "Data integration gets 'Sloppy'," Nat Biotechnol, vol. 24, pp. 1070-1, Sep 2006.[4] H. F. Deus, R. Stanislaus, D. F. Veiga, C. Behrens, I. I. Wistuba, J. D. Minna, H. R. Garner, S. G. Swisher, J. A. Roth, A. M. Correa, B. Broom, K. Coombes, A. Chang, L. H. Vogel, and J. S. Almeida, "A Semantic Web Management Model for Integrative Biomedical Informatics," PLoS ONE, vol. 3, p. e2946, 2008.Figure 5. Permission management by embedding the corresponding propagation model in S3DB’s core architecture, See Deus et. al 2008 for an illustrative application where this feature is used to “incubate” a shared data store.Background• Data, data everywhere: Automated discovery of candidate biomarkers from multiple databases has been the central challenge in the Life Sciences in general and in the study of systemic processes such as those documented by The Cancer Genome Atlas (TCGA) in particular. • Foundations for a better solution: The maturation of Semantic Web technologies offers solutions to those problems by allowing the query to be defined by navigating a formally represented domains of discourse instantiated by the data. We address the systems challenge of The Cancer Genome Atlas initiative (http://cancergenome.nih.gov/), which generates a large scale repository of high throughput molecular biology data generated and processed at 5 academic facilities across the USA [1, 2]. Currently, the heterogeneity of domains (genomic, transcriptomic, epigenetic effects, proteomic, clinic and demographic, etc) that are part of the TCGA data is aggregated at a single point of access charged with providing syntactic interoperability to all of the data – the TCGA portal.The objective of this work is therefore the exposure of the highly heterogeneous TCGA data from various sources through RESTful SPARQL endpoints.ObjectivesFigure 2. Identification of TCGA data elements: Breaking the TCGA datasets into their individual data elements is not a trivial task. The data elements first need to be extracted from their original hierarchical sources before they can be represented as RDF triples. For example, the path to raw data files in an FTP server (1) is separated into its constituent slash-separated portions and the resulting data elements, according to their position in the path, are assigned to values of statements. For example, the string “ht_hg-u133a” is assigned as the object of a statement where the subject is a URI identifying an instance of a platform and the predicate is the URI of the rule that links S3DB collection “Platform” and attribute “Platform Code.” Similarly, each analyte is identified by a barcode (2), which broken down into its constituent elements renders the values for attributes Sample ID, Sample Type and Sample Collection Center Code, among others.Figure 1. A caching service for TCGA archives: The TCGA datasets are typically compact assemblies of data elements with various levels of structure, of which the largest elements are the “archives” where the raw data is contained. To overcome the problem of having to bulk download each large archive, a caching service was developed to find and retrieve the latest version of an array from the TCGA archives given a set of attribute-value pairs (for institution, platform and sample (1)). The dynamic link generator, made publicly available at http://tcga.s3db.org/TCGAsync.php, recursively browses the TCGA datasets to return the raw data file corresponding to the correct array (2.1). If the archive has been compressed and the raw data file is not available as a symbolic link (2.2), then the TCGA archive is first downloaded and decompressed and the correct array is returned. It is not uncommon for an array to be requested multiple times. When the array is not being requested for the first time, the correct raw data file is downloaded from the caching service.root:transcriptomicroot:genomicroot:epigeneticEffectsroot:proteomic root:clinicalS3QL@prefix root: <http://root.s3db.org>root:TCGAS3QLS3QLS3QLS3QLFigures 3 and 4. Data distribution in multiple S3DB deployments by serialization of SPARQL: each S3DB deployment (in green) is equipped with a RESTfull API (in yellow) through which data is programmatically accessed / inserted / updated using the S3DB query language (S3QL). Additionally, deployments are uniquely identified by URI which are resolved at a unique root location (http://root.s3db.org, in red). This simple system of resolving the URL of the deployments based on their unique identifiers  assists the process of finding data elements which might be stored in S3DB deployments other than the one that was queried. When a query is performed where a data element that is stored in another deployment is requested, S3DB will perform the necessary S3QL query, Privacy concerns and data sharing issues are solved by relying on the S3DB permission management model. User authentication need only occur at the deployment where the user has been registered. Once authenticated, a temporary key may be generated which is migrated with the serialized S3QL queries, providing the other deployments with the users credentials such that a level of permission may be determined. Each S3DB deployment is also a SPARQL endpoint. Therein, SPARQL triples are serialized into S3QL queries, which are then used to query any number of deployments simultaneously. The triple patterns in the SPARQL query mimic the definition of the domain rules by replacing the predicate portion of the rule triple with its identifier.Figure 4. An interface for TCGA SPARQL endpoint: When the RDF representation includes a separation between the domain and its instantiation, navigating the domain to produce SPARQL triples is straightforward. This structure is imposed on the RDF representation of the data by the S3DB Core morel. Available at http://tcga.s3db.org<GenomicCharacterization> <obtainedFrom> <Sample>.<Sample> <obtainedFrom> <Patient> .<Patient> <treatedAt > <SampleCollectionCenter > .<SampleCollectionCenter> <name> “MD Anderson” .?GenomicCha racterization :R3979 ?Sample .?Sample :R247 ? Patient .?Patient :R47487 ?SampleCollectionCenter .?SampleCollectionCenter :R44596 ?NameFILTER regex(?Name, “MD Anderson”) .<select>*</select><from>statements</from><where><rule_id>44596</rule_id><value>MD Anderson</value></where>@prefix : <http://tcga.s3db.org>RulesSPARQL<select>*</select><from>statements</from><where><rule_id>3979</rule_id></where><select>*</select><from>statements</from><where><rule_id>247</rule_id></where><select>*</select><from>statements</from><where><rule_id>47487</rule_id></where>(a)(b)(c)(d)DomainQuerySerializationParallelized S3QL(d)(a)(b)(c)SPARQLRulesS3QLwww.s3db.org, Nature Biotechnology (2006) 24(9):1070-1071, PLoS ONE 3(8): e2946. doi:10.1371/journal.pone.0002946rdfs:subClassOfrdfs:subClassOfrdfs:subClassOf rdfs:subClassOf CollectionrojectP Item[Cid] [Iid] [Cid or L]rdf:objectrdf:predicaterdf:subjectrojectPDeploymentDeploymentUnique Identifiers of entities:Durl rdf:type s3db:DeploymentPid rdf:type s3db:ProjectCid rdf:type s3db:CollectionRid rdf:type s3db:RuleSid rdf:type s3db:StatementIid rdf:type s3db:ItemUid rdf:type s3db:UserGid rdf:type s3db:Grouprdfs:subClassOfrdf:predicaterdf:subjectrdf:objectrdf:predicaterdfs:subClassOfrdf:subjectrdf:object[Iid] [Rid] [Iid or L]UserGrouprdfs:subClassOf{Doublin Core:}dc:created_by Uiddc:created_on datedc:service {term of cv}etc …Collection ItemRule StatementUserGrouprdf:objectrdf:predicaterdf:subjectS3DB Entity (annotated using DC)Relationship (defined using RDFS)Permission (defined by s3db:permission)Annotation of s3db entities:Needed only if sharing with Project that is hosted by a distinct S3DBDeployment.Rule StatementAttribute Valuerdfs:subClassOfrdf:objectrdf:predicaterdf:subjectrdf:subjectrdf:objectrdf:subjectrdf:objectrdf:objectrdf:predicaterdf:subjectNature Precedings : doi:10.1038/npre.2011.6303.1 : Posted 30 Aug 2011

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Exposing The Cancer Genome Atlas (TCGA) as a SPARQL endpoint

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