=Paper=
{{Paper
|id=None
|storemode=property
|title=Diagen: A Model-driven Framework for Integrating Bioinformatic Tools
|pdfUrl=https://ceur-ws.org/Vol-734/PaperDemo14.pdf
|volume=Vol-734
|dblpUrl=https://dblp.org/rec/conf/caise/VillanuevaVLP11
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==Diagen: A Model-driven Framework for Integrating Bioinformatic Tools==
https://ceur-ws.org/Vol-734/PaperDemo14.pdf
Diagen: A Model-driven Framework for
Integrating Bioinformatic Tools
Maria José Villanueva, Francisco Valverde, Ana Levín, and Oscar Pastor
Centro de Investigación en Métodos de Producción de Software
Universitat Politècnica de València
Camino de Vera S/N 46022, Valencia, Spain
{mvillanueva, fvalverde, alevin, opastor}@pros.upv.es
Abstract. Nowadays, the diagnosis of disease based on genomic infor-
mation is feasible by searching genetic variations on DNA sequences.
However, geneticists struggle with bioinformatic tools that are supposed
to simplify DNA sequence analysis. As a universal tool to support ev-
ery requirement is far from be implemented, geneticists themselves must
solve the data exchange among several tools. Due to the fact that there
are no standards to support this integration task, it must be managed in
every analysis. This paper proposes addressing this integration by means
of a model-driven framework. The Diagen framework is a software imple-
mentation based on conceptual modeling principles that formalizes data
exchange and simpli es bioinformatic tool integration. First, we analyze
how conceptual modeling can be used to deal with data exchange among
tools. And then, as a proof of concept, the presented framework is used
to search for variations on the BRCA2 gene using real DNA samples and
a set of speci c bioinformatic tools.
Keywords: Model-Driven Development, Tool Integration, DNA sequence
analysis
1 Introduction
Recent genetic discoveries have opened the door to personalized disease diagnosis
based on DNA sequence analysis. Nowadays, it is possible to predict the risk of
getting a certain disease by searching for speci c genetic variations on the DNA
sequence [1].
Geneticists perform DNA sequence analysis aided by bioinformatic tools.
Even though these tools are functional and useful for reducing time and com-
plexity, none of them completely ful ll all the geneticists' requirements [2]. As a
consequence, geneticists are forced to use several tools in order to gather all the
functionality and, eventually, accomplish the complete DNA sequence analysis.
One important issue regarding these tools is that data exchange among them
is required. The problem lies in the fact that each of these tools is isolated and
uses its own data format to report the computed information. For this reason,
data exchange among tools is a non-trivial task that geneticists must address
106 Pre-proceedings of CAISE'11 Forum
in each analysis according to the following procedure: 1) Export data from the
source tool; 2) Understand the semantics of the tool-speci c data format; 3)
Perform a translation into the target tool format; and nally, 4) Import the
data into the target tool.
As geneticists usually lack Software Engineering knowledge, most of them
perform this task manually or develop programming scripts. Although these
speci c scripts are useful in solving minor problems, they are far from being
compliant with good practices of Software Engineering. The implemented scripts
to support data exchange are often coupled solutions that integrate only two
speci c tools. In the end, these solutions cannot be reused and compromise the
geneticists exibility for using other tools.
As a solution, this paper proposes the application of conceptual modeling to
develop a model-driven framework that formalizes data exchange and simpli es
tool integration. In order to provide a high quality solution, this work has been
developed in the context of a collaboration with geneticists from the Genomic
Medicine Institute (IMEGEN). As a proof of concept, the proposed framework
integrates several tools that are used by IMEGEN geneticists in their daily rou-
tine to search for genetic variations using real DNA samples of the BRCA2 gene
(a gene related to Breast Cancer).
The paper is organized as follows: Section 2 presents a brief summary of
other proposed solutions to solve the tool integration problems in DNA sequence
analysis. Section 3 explains the proposed model-driven framework for integrating
bioinformatic tools. Section 4 presents how the framework is used for disease
diagnosis support using samples of the gene BRCA2 and a set of bioinformatic
tools. And nally, section 5 presents the conclusions and future work.
2 Related Work
Several works have attempt to overcome current DNA sequence analysis tool
issues. These proposals follow two dierent approaches.
Several sequence le formats for expressing bioinformatic tools results have
emerged. Examples of these formats are: 1) Variant calling formats, such as
the Variant Call Format (VCF) proposed for the 1000 Genomes Project [3];
2) Alignment results formats, such as the Sequence Alignment/Map Format
(SAM) [4], which provides a compressed textual representation, and the Genome
Variation Format (GVF) [5], which provides a textual format using the Sequence
Ontology [6].
All these formats have been de ned for the purpose of providing interop-
erability among dierent DNA sequence analysis tools. The implementation of
decoupled data exchange mechanisms is feasible using any of the above examples
as a standard format. However, their main drawbacks are the complexity of each
textual format and the mandatory implementation of a low- level mechanism
to extract the data. As a consequence, none of them have become a widely ap-
plied standard and are only used in the research context where they have been
proposed.
Diagen: A Model-driven Framework for Integrating Bioinformatic Tools 107
Several bioinformatic development frameworks have also been implemented.
Some examples of these frameworks are Biojava [7], BioPython [8], or BioPerl [9].
These frameworks provide an API that supports common functionality for DNA
analysis tasks. Additionally, they provide several format conversion operations
to transform le formats among dierent tools.
These frameworks have been de ned to provide geneticists with the freedom
to implement their personalized tools. However, the geneticists still have to worry
about low-level programming details and integration issues.
3 An Integrative Framework for Bioinformatics
This work presents a model-driven framework for the integration of DNA se-
quence analysis tools and retrieval of genetic information. Diagen is classi ed
as a model-driven framework because each of its components (classes, data enti-
ties, operations) is a projection of the Conceptual Schema of the Human Genome
(CSHG) [10]. The CSGH is a conceptual model created with the collaboration
of geneticists, where biological concepts related to the human genome have been
precisely addressed and de ned. The framework uses this conceptual model to
support the following DNA sequence analysis tasks (Figure1):
Fig. 1. General View of the Framework
1. Sequence Treatment: A DNA sequence is rebuilt from the fragments gener-
ated by the sequencing machines.
2. Sequence Alignment: A DNA sequence is aligned to a reference sequence in
order to determine the dierences between them.
3. Variation Knowledge: Using data gathered in genomic databases, each se-
quence dierence that is related to a disease is reported.
Data exchange among tools is a di cult task because there is a great variety
of formats to express the dierent results. Taking into account that data ex-
change is required when a tool calculates data that another tool requires, it can
be assumed that both tools must share a set of common concepts. Therefore, it
108 Pre-proceedings of CAISE'11 Forum
is possible to de ne a conceptual model that represents those shared concepts
and establishes well-de ned boundaries and vocabularies.
Diagen establishes the common context to guide data exchange among tools
that de ne a conceptual model for each task transition:
The Sample Treatment Report conceptual model (Figure 2) de nes all the
concepts related to the reconstructed sequence in the sequence treatment
task (T1) to be analyzed in the sequence alignment task (T2).
Gene
Reference
-id
-sequence
-transcript_id
1 1
1 1 *
*
Segment
Exon
-id
-num
-sequence
-consensus
-electropherogram
-starpos
-endpos
Fig. 2. Sample Treatment Report Conceptual Model
The Alignment Report conceptual model (introduced in [11]) de nes all the
concepts related to the dierences found in the sequence alignment task (T2)
to be characterized in the variation knowledge task (T3).
The Knowledge Report conceptual model (Figure 3) de nes all the concepts
related to the characterized variations to be used for other task (for example,
a diagnosis report creation task).
Data exchange among tools that perform these tasks usually requires the
implementation of a translation mechanism to understand each other. In that
case, data expressed in a concrete format needs to be translated into a dierent
format. However, the use Diagen avoids these coupled implementations because
a tool to be integrated in the framework only needs a translator that expresses its
outputs in terms of the underlying conceptual model. This translator is easier
to implement since it only requires establishing the relationships between the
output and the conceptual model.
Each task that is supported by the framework has been implemented to be
independent from the others, and, therefore, it can be used separately. Thanks to
this modularity, it is possible, for example, to use the alignment task in another
environment. In this case, the input data should be provided in terms of the input
conceptual model (Sample Treatment Report) and the output report should be
read in terms of the output conceptual model (Alignment Report).
Diagen: A Model-driven Framework for Integrating Bioinformatic Tools 109
Reference
KnowledgeReport 1 DNASequence
-geneId : string -refSource : string
1..* -sequence : string
Query
1..*
*1
1
Variation
-startPos : int Bibliography
-endPos : int Knowledge
-title
-fsrigth : string documentacion -phenotype : string 0..* paper -authors
-fsleft : string -isSNP : bool -abstract
-heterocygous : bool -certainty : string
1..1 -publication
-HGVSGenomic 1 0..* -source : string -URL : String
-HGVSCoding
-HGVSProtein
Insertion Deletion Substitution
-bases : string -length : int -bases : string
Fig. 3. Variation Knowledge Conceptual Model
The Diagen framework has been implemented using the Java language. Ad-
ditionally, each conceptual model involved in data exchange has software corre-
spondence with a set of Java classes and a XML representation. In order to man-
age both representations (Java and XML) JAXB (Java Architecture for XML
bindings) [12] has been used. This is a speci c API that allows Java objects to
be parsed in a XML data and vice-versa.
4 Using Diagen for Disease Diagnosis Support of the
BRCA2 Gene
As a proof of concept, the framework has been used to develop a prototype for
disease diagnosis support of Breast Cancer. This speci c framework con guration
integrates several bioinformatic tools that are used daily by the geneticists of
IMEGEN.
Recently, the framework (Figure 4) has been applied to integrate:
1. Sequence treatment task: The Sequencher tool [13] is used to rebuild the
samples provided by a sequencing machine.
2. Sequence Alignment task: The implementation of the algorithm BLAST from
NCBI [14] is used to search for dierences in the sequence. There is also
an integrated tool that is based on the Smith-Waterman Algorithm (SW
Tool) and a tool that looks for known-variations in the sequence by aligning
anking sequences (Flanking Tool).
110 Pre-proceedings of CAISE'11 Forum
Fig. 4. IMEGEN con guration of the framework
3. Variation Knowledge task: Variation characterization is performed manually
by geneticists searching in several databases. However, this framework pro-
vides two mechanisms for genetic knowledge data retrieval. The rst mech-
anism obtains some data from the ENSEMBL database [15]. The second
mechanism retrieves genetic information from the HGBD database [16] based
on the Conceptual Schema of the Human Genome (CSHG) [10].
The prototype supports the three de ned tasks needed to perform a DNA
sequence analysis. As a result, it retrieves a personalized report containing the
genetic variations and the potential diseases of the individual.
The main advantages of the framework are: 1) A decrease in the execution
time, 2) A reduction in the eorts needed for data exchange among tools; and
3) The elimination of the need to search for variation data in the huge set of
databases spread around the Web.
The prototype has been tested with real samples of the gene BRCA2 (Table
1). The test was carried out analyzing the BRCA2 gene sample from ten dierent
patients (P1-P10). For each patient, the table shows the number of variations
characterized by IMEGEN, the number of variations characterized by Diagen,
and the accuracy that Diagen oers compared with the IMEGEN manual pro-
cess. IMEGEN performs the analysis in approximately four hours (depending on
the success achieved while searching for a dierence in the genetic repositories).
The preliminary test showed that Diagen oers the results almost instantly
and with an accuracy rate of between 60-90%. It is also important to emphasize
that the variations that were not characterized by Diagen were always the same
variations (7 variations in total) that appeared repeatedly in all the analyses.
Diagen: A Model-driven Framework for Integrating Bioinformatic Tools 111
Table 1. Preliminary BRCA2 tests
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
Characterized Var. IMEGEN 7 10 8 8 8 13 9 10 9 8
Characterized Var. Diagen 6 6 7 6 5 8 6 7 6 5
Accuracy rate % 86 60 88 75 63 62 67 70 67 63
5 Conclusions and Future Work
This work proposes a model-driven framework that is based on a well-de ned
conceptual model of the human genome in order to address DNA sequence anal-
ysis. As a proof of concept, the Diagen framework is con gured for the develop-
ment of a disease diagnosis support and is tested by means of real DNA samples
of the BRCA2 gene.
We have realized that the tools available actually accomplish some of the
geneticists' goals. The problem lies in the fact that geneticists' activities, specif-
ically in the DNA analysis domain, lack standard methodologies, well-de ned
tasks, xed vocabularies, and uni ed knowledge sources. As a consequence, the
execution of a DNA sequence analysis cannot be performed e ciently or without
geneticists' intervention.
The solution to these problems is not to reinvent new DNA sequence analysis
tools but to integrate the most suitable tools according to geneticists' needs. The
presented framework applies conceptual modeling to integrate dierent bioin-
formatic tools and to provide a common context to exchange data with each
other. The main advantage of the presented framework, over other integration
approaches is that Diagen is a high-level abstraction framework that provides
concise and signi cant tasks to geneticists instead of low-level tasks. Moreover,
with this framework, geneticists can perform a DNA sequence analysis and forget
about the data formats of dierent tools.
As genetics is a very innovative eld that is constantly evolving with new
discoveries, all concepts must be well-de ned without ambiguity. Thanks to the
conceptualization of the DNA sequence analysis tasks, all the involved concepts
are precisely formalized. As a consequence, it is easier to adapt the tasks to
changes or to support new concepts.
The preliminary results are promising, but there is room for improvement.
The low accuracy detected is because the missed variations were not described in
the integrated sources. As these sources are constantly improving, it is expected
that future versions will solve these issues.
As future work, the framework will be extended to support other bioinfor-
matic tasks. The main goal of this extension is to design a complete framework
that supports other genetic functionality besides DNA sequence variation anal-
ysis. Additionally, the next step is to apply the service-oriented paradigm to
provide a more exible development environment. With this approach, geneti-
cists could select only the required functionality, de ned as services, and easily
create a personalized tool.
112 Pre-proceedings of CAISE'11 Forum
Acknowledgments Thanks to the Instituto de Medicina Genómica (IMEGEN,
http://www.imegen.es) for its support and the provision of data. This work has
been developed with the support of MICINN under the FPU grant AP2010-
1985 and the project PROS-Req (TIN2010-19130-C02-02), and co- nanced with
ERDF.
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