Design and Implementation of a Geis for the Genomic Diagnosis
using the SILE Methodology. Case Study: Congenital Cataract
Manuel Navarrete-Hidalgo
, José Fabián Reyes Román
and Óscar Pastor López
PROS Research Center, Universitat Politècnica de València, Valencia, Spain
Department of Engineering Sciences, Universidad Central del Este, San Pedro de Macorís, Dominican Republic
Keywords: Conceptual Modeling, Preventive Diagnosis, GeIS, Precision Medicine, ETL, CMHG.
Abstract: Biomedicine and the preventive diagnosis of diseases open a series of lines of research as diverse as
proposed solutions. However, the information that the humans contain within the genome represents a great
challenge related to the processing and management of their biological information, whose success will
depend directly on the structures that will be generated through the application of conceptual modeling
techniques. In this context, this research work presents the development of a prototype of "Extraction-
Transformation-Load", where biological information can be obtained from multiple scientific repositories
that do not have direct interaction. For that reason, the Conceptual Model of the Human Genome (CMHG)
proposed is used as a holistic representation of the domain with the aim of generating Genomic Information
Systems (GeIS), which facilitate an efficient management of all the existing knowledge in the genome in
order to enhance “Precision Medicine” (PM). This work defines a GeIS for the preventive diagnosis of
congenital cataracts”, whose condition is not related to age and lifestyle, but to the genetic component of
each person. In this way, we can provide an early diagnosis and possible means of personalized treatments.
Since the beginning of mass sequencing, one of the
challenges within the biological context has been to
understand the molecular basis of organisms.
However, the large amount of data collected has
generated a problem with the management of this
information. Although a multitude of computational
tools and strategies have been developed for the
evaluation of this information (Staden, 1979), there
is a problem associated with the access and
consultation of the data stored in the genomic
repositories, because they have offered them in a
heterogeneous, redundant and dispersed way,
(elements that are part of the so-called "Genomic
Chaos"). To advance knowledge and its application,
most researchers indicate the need of an
improvement in the capacity for analysis and
management of that information (Solomon, 2014).
Therefore, the extraction of biological
information should be automated through the
development of software tools for ETL (Extraction,
Transformation and Loading) (Muñoz et al., 2011),
which should be able to consult the different
repositories of genomic information and adapt the
existing data according to the requirements of a
conceptual model, such as the Conceptual Model of
the Human Genome (CMHG) proposed by the
PROS Research Center, whose conceptual
representation of the genome opens a standard of
access, consultation, exploitation and generation of
preventive tools for diseases caused by genetics.
These software tools would reduce the
interaction time between the human and the genomic
repositories to obtain information, because in this
phase more resources are invested, and more errors
are made (due to the human factor). This would
allow the direct application of genomic information
in public health issues, such as in the preventive
detection of cataracts.
Cataract is a disease that affects human and
animal vision, mainly caused by the loss of
transparency of the lens, which is located just behind
the pupil (Boyd, 2016). People with cataracts suffer
from blurred vision, inability to appreciate the
contour of what they see, loss of color intensity and
hypersensitivity to glare, as well as headaches and
visual fatigue. This is produced because the lens
becomes opaque by the high concentration of
proteins in its cells and the development of dense
Navarrete-Hidalgo, M., Reyes Román, J. and Pastor López, Ó.
Design and Implementation of a Geis for the Genomic Diagnosis using the SILE Methodology. Case Study: Congenital Cataract.
DOI: 10.5220/0006705802670274
In Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2018), pages 267-274
ISBN: 978-989-758-300-1
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
bodies (The National Eye Institute, 2017)
One of the ways to improve detection and
provide prevention and/or cure mechanisms, as well
as to study their direct link with other diseases,
would be to analyze genomic repositories for genetic
indicators for "congenital cataract" whose
appearance is not related to the natural aging of the
human being but to the genetic component of each
The paper is divided into the following sections:
Section 2 presents the state of the art (conceptual
modeling applied in genomics and cataracts).
Section 3 shows a brief description of the CMHG.
Section 4 contains the SILE Methodology used to
the "congenital cataract". Finally, Section 5 presents
the conclusions and outlines future work.
Bioinformatics is born from the interaction of
molecular biology and computer science, with the
objective of allowing biological data to be
processed. This data is characterized by its large
size and continuous growth, which is why it is
necessary to develop tools to manage the
information in an agile and efficient way, as well as
new algorithms and statistical solutions oriented to
the analysis of the DNA sequences and their
variations. In this sense, there are several standards
and formats for the representation of nucleotide and
protein sequences, programs of comparison of
variations and frameworks for the exploration of
genetic diseases, as well as databases with all
genome sequences, nucleotides, proteins, proteins
structures, human genetic diseases, and bibliography
available for public study and research.
The main biological databases including
sequence data were created in the USA, EU and
Japan (see Table 1). Their beginnings date from
1971 and continue to the present day.
On the other hand, all this biological information
must be perfectly structured and bounded in an
Information System (IS) that allows its treatment and
exploitation in an efficient way. Understanding the
genome is a complex task, and generating a correct
conceptual definition (Olivé, 2007) that addresses all
current genomic knowledge is essential to
understand the -genomic- domain. In addition, this
conceptual model must be subject to constant
evolutions product of new discoveries in the context.
Table 1: Most Popular Databases.
Name DB type Source Start
SNPedia Polymorphisms USA 2006
BioCyc Metabolic pathways USA 2005
Reactome Metabolic pathways EU 2004
Ensembl Genomics EU 2000
UCSC Genomics USA 2000
dbSNP Polymorphisms USA 1998
PubMed Bibliographical USA 1996
KEGG Metabolic pathways Japan 1995
OMIN Genetic diseases USA 1995
EMBL Nucleotides EU 1992
DDBJ Nucleotides Japan 1986
Uniprot Proteins EU 1986
GenBank Nucleotides USA 1982
PDB Proteins USA 1971
2.1 Conceptual Modeling in Genomics
In the field of genomic bioinformatics, the first
works of conceptual modeling were given by Paton
(Paton et al., 2000). His essays were supported by
previous work on modeling of protein structures
(Gray et al., 1990), being a pioneer in the conceptual
design of the eukaryotic cell, its genomic
organization, transcriptome, proteome and
metabolome modeling, among others; using the
unified modeling language -UML-
( In addition, Ram et al. (2004)
presents a conceptual modeling approach applied to
the protein context, this paper states that the use of
conceptual modeling facilitates the representation
without semantic loss and comparison and search
operations in complex structures, such as in the
modeling of the three-dimensional structure of
proteins, characterized by the large volume of data.
With these bases, the genome group of the PROS
Research Center, of the Universitat Politècnica de
València, started in 2008 a line of research focused
on the modeling of the human genome for analysis
and study the most basic expression, genes, and their
mutations within a chromosomal segment (Pastor,
2008). However, this model does not consider the
existence of processes such as the regulation or
coding of a single protein by two different genes, as
well as the combined action of multiple genes. As
detailed in the work of 2010 (Pastor et al., 2010),
this version of the conceptual model of the human
genome is called the "essential" model and is
composed of three views:
Genome View: models human genomes.
Gene-Mutation View: models the entities
Gene” and “Allele”, and the knowledge of
their structures.
ENASE 2018 - 13th International Conference on Evaluation of Novel Approaches to Software Engineering
Transcription View: models transcription
After this, the schema is extended with a fourth
view called "Phenotype View" that allows for the
phenotypic representation, that is, the
concrete/visible manifestation of a genotype in a
given context. Subsequently, the CMHG migrated
from modeling oriented to the study of genes to
another centered on the concept of the chromosome
(Reyes et al., 2016). This new focusing is called
CMHG v2 (Reyes et al., 2017).
2.2 Cataract
Cataract is the most frequent pathology of the lens
and is the most common cause of reversible
blindness, and produces a series of disabilities as
explained in Section 1. According to the location of
the opacity, several types of cataract are
distinguished: nuclear, cortical and subcapsular. In
nuclear cataract, opacity lies in the central part of the
lens, while in the cortical and subcapsular the
opacity lies in the periphery of the lens and in the
central area of the posterior aspect of the lens
Currently, the only effective treatment for
cataract is surgical intervention, the most recent and
least invasive being Phacoemulsification
(Emulsifying and aspirating the nucleus of the lens
with a high-frequency ultrasonic needle), followed
by the Small Incision Cataract Surgery (Reinstein et
al., 2014). Others like the Extracapsular Cataract
Extraction and the Intracapsular Cataract
Extraction (Kirchhof, 2017) are more complex, with
slower recovery times, sutures and higher induced
injuries. In all of them the lens is removed, and an
intraocular lens is placed. At the biological level, the
appearance of cataracts is associated with certain
common cellular mechanisms (Jobling et al., 2002)
(Michael et. al., 2011), which has allowed to know
which habits and risk factors associated with cataract
development (Rakel, 2014), such as exposure to
sunlight and UV, stress, tobacco, excess of alcohol,
malnutrition, vitamin deficiencies, obesity,
dehydration, chronic diseases, medications, as well
as metabolic disorders such as galactosemia and
diabetes, plus genetic and hereditary reasons. The
latter scenario is centered in the case study, the
hereditary congenital cataract, which includes four
types (Hejtmancik, 2008):
Autosomal recessive: Two copies of the allele
are needed to develop the disease.
Autosomal dominant: Located on non-sexual
chromosomes, with a single copy of the allele
responsible for developing the disease.
Sporadic: It is due to a spontaneous mutation
that affected all the cells of the individual
including the sexual ones, provoking
predisposition to suffer the disease and being
heritable, although their parents did not suffer.
X-linked: It is due to an allele predisposed to
suffer from the disease located on the sex
chromosome X, which causes in women it
depends on the dominance or recessiveness of
the allele.
The Conceptual Model of the Human Genome
(CMHG) version 2 is composed of six independent
but related views (Pastor et al., 2016). These views
are structural, transcription, variations, phenotype,
pathways and bibliography references. Once the
CMHG has been defined, it is necessary to have an
entity that allows the storage and access to this
information in a fast and structured way. These
operations are guaranteed by a relational database
schema called “Human Genome Database
(HGDB), which currently models the structural
view, variations, bibliography references, and
validations through tables. For more information,
see the full view and description in (Reyes et al.,
2016). This model remains in constant study, so it is
necessary for the CMGH to continue evolving
according to new discoveries and non-contemplated
genetic structures, such as haplotypes support
(Reyes et al., 2016), as well as the study of quality
metrics to ensure the best definition of the domain.
SILE (Search-Identification-Load-Exploitation) is a
methodology whose objective is to perform a load of
selective information with “curated data”, given that
the existing information is dispersed, heterogeneous
and often redundant. Performing a massive data load
would suppose managing and working with invalid
or obsolete information. For this, the SILE
methodology is composed of four stages. In the first
place, a search of genes is made in the genomic
repositories most used by the scientific community.
In the next stage, an accurate identification and
Design and Implementation of a Geis for the Genomic Diagnosis using the SILE Methodology. Case Study: Congenital Cataract
validation of genes and variations associated with
the genetic disease of study is performed. To do this,
there is a group of experts in areas of molecular
biology, biomedicine and/or biotechnology coming
from different hospital centers with which the PROS
Center has collaborations, such as, the Hospital
Universitari i Politècnic La Fe, the INCLIVA, as
well as companies specializing in genetic studies,
such as IMEGEN and tellmeGen. Then, the data
obtained are loaded into the HGDB and finally
exploited using the genomic framework called
4.1 Search
First, it is necessary to search all genes directly or
indirectly associated with congenital cataracts (Table
2). These data were obtained from two reviews
(Hejtmancik, 2008), (Shiels & Hejtmancik, 2013)
and various research articles (Cobb et al., 2000; Fu
& Liang, 2002; Faiyaz-Ul-Haque et al., 2007;
Richter et al., 2008; Sagona et at., 2014; Javadiyan
et al., 2016).
Table 2: Genes associated with congenital cataract.
Gene Official name
BCOR BCL6 corepressor
BFSP2 Beaded filament structural protein 2
CHMP4B Charged multivesicular body protein 4B
CRYAA Crystallin alpha A
CRYAB Crystallin alpha B
CRYGC Crystallin gamma C
CRYBB1 Crystallin beta B1
CRYBB2 Crystallin beta B2
CRYBB3 Crystallin beta B3
CRYBA4 Crystallin beta A4
CRYGD Crystallin gamma D
CRYGS Crystallin gamma S
GCNT2 Glucosaminyl (N-acetyl) transferase 2
GJA3 Gap junction protein alpha 3
GJA8 Gap junction protein alpha 8
HSF4 Heat shock transcription factor 4
LIM2 Lens intrinsic membrane protein 2
MAF MAF bZIP transcription factor
NHS NHS actin remodeling regulator
MIP Major intrinsic protein of lens fiber
PITX3 Paired like homeodomain 3
VSX2 Visual system homeobox 2
In addition, there are studies that highlight the
clinical utility of the genomic variations, indicating
that the detection rate of mutations in affected
patients is 70% using massive sequencing
techniques. This indicates that the genomic studies
would be useful for early detection of the disease
and improving clinical advice, as they provide
significant additional diagnostic information,
allowing the existence of a personalized medicine at
the genomic level (Ding et al., 2017). Jointly, the
Eye Genetics Research Group of Children's Medical
Research Institute (CMRI, 2017) is expanding the
genomic knowledge of congenital cataracts, which
offers opportunities to analyze this information.
4.2 Identification
Once the cataract related genes are identified, we
proceed to the identification of variants, location and
cataract types (AD, autosomal dominant; AR,
autosomal recessive; S, sporadic; XL, X-linked) that
have been recently studied, covering a timespan
from 2014 to 2016 (Ma. et al., 2016) (Table 3):
Table 3: Variations associated with congenital cataract.
Gene Chromosome Type Variation-SNP
BCOR Xp11.4 X rs864309680
CRYAA 21q22.3 AD rs864309685
CRYAB 11q23.1
CRYGC 2q33.3
CRYBB1 22q12.1
CRYBB2 22q11.23
GJA3 13q12.11 AD rs864309687
GJA8 1q21.2 AD
MAF 16q23.2 AD rs864309678
MIP 12q13.3 E/AD rs864309693
NHS Xp22.13 AD
It should be noted that identification allows us to
delimit all existing knowledge, and opens an
important aspect about what information is obsolete
or very relevant.
4.3 Load
The loading step of the data obtained through the
application of the SILE methodology in the human
genome database is composed of three subprocesses
called extraction, transformation and loading. These
ENASE 2018 - 13th International Conference on Evaluation of Novel Approaches to Software Engineering
subprocesses are part of the ETL concept
(Extraction-Transformation-Load) that allows and
guarantees the extraction of information from
different data sources for analysis, conversion and
compliance with the constraints of the target system,
i.e., the HGDB, like shows the Figure 2.
Figure 1: ETL Process.
The table structure of the HGDB currently
accommodates the structural, variations,
bibliography references, and validations view,
which can be consulted or reviewed in the following
work (Navarrete-Hidalgo, 2017).
The origins of the information, used for this
work were the biological repositories of the National
Center for Biotechnology Information
( These are: Gene,
offers all the information associated with the genes.
dbSNP, compiles simple nucleotide polymorphisms
among several species. ClinVar, shows information
on variations, types, nucleotide changes, etc.
Nucleotide, links genome sequences and data to
genes and their transcripts. Pubmed, database of
research articles (Gibney et. al., 2011). Although
each source contains very specific information and
encourages a trivial data location, the characteristics
of the views require the need to consult several
repositories and be able to relate them in an agile
and concurrent way.
The programming language used for application
development and ETL subprocesses has been Java
in version 8.144 (Oracle Corporation, 2017). The
choice of this language is due to its extensive
documentation, libraries, as well as its robustness,
security and portability, as it is independent of the
architecture and platform. On the other hand, the
output format of the repositories is the meta-tagged
language (XML¸, Java has
an XML processing API called SAX (Oracle
Corporation, 2017), free access and event-based.
That is, SAX (Simple API for XML) parses the
document sequentially and as it locates a start or end
tag launches an event to read the attributes of the
same, or perform another action. SAX is
characterized by having reduced memory
consumption and is oriented to reading large XML
4.3.1 ETL (Extraction)
The extraction is the first stage of the ETL process
and its main function is to extract the data from the
different sources of information, which may be local
repositories or located on the Internet. In addition to
accessing them, the extraction stage must analyze
them, check their structure, and in case of not
complying with the necessary requirements, reject
them, since during this phase the data are converted
to a previous format that serves as intermediate for
stage of transformation. In this sense, the origin of
the information required comes from the biological
and scientific repositories of NCBI: dbSNP,
ClinVar, Gene, Nucleotide, Protein and Pubmed.
ClinVar is consulted to obtain the detail of the
variation, that is to say, the change of nucleotide, its
chromosome position, as well as the type of
variation carried out. To obtain gene information,
such as his official name, abbreviation, description,
synonyms, the Gene repository is queried. Thirdly,
to obtain the information of the proteins resulting
from the variation, the Protein repository is
consulted, from which it’s official name and its
amino acid sequence are obtained. On the other
hand, to obtain the chromosome sequences and the
transcribed elements of the variation, Nucleotide is
consulted. Finally, PubMed is consulted, from which
all the bibliographical sources and research articles
are obtained, being the views documented by the
repositories. However, there is a problem of access
and consultation to these repositories that the present
work intends to face. First, the relationship and
consultation between them is not direct.
In addition, access to each one is done
individually, sequentially and with complex
parameterized queries from which the information is
extracted in XML format. The XML information of
each repository has a particular structure, repetitive
information in different elements and tags that need
to be analyzed to discard those which are less
descriptive. All the extracted information has to be
processed later and transformed to obtain the
identifiers and arguments of consult, which causes
that the stage of extraction of some repositories does
not begin until the transformation finished, and both
the identifiers and the rest of attributes are obtained.
The query consists of a URL composed of four
elements that allow specific arguments (Table 4).
Design and Implementation of a Geis for the Genomic Diagnosis using the SILE Methodology. Case Study: Congenital Cataract
Table 4: Arguments allowed in the query URL.
Argument Parameter Description
db gene
Selection of the
database to consult.
id Identifier Identifier of the item
to consult.
retmode xml
Output format of the
data. By default, is
In the case of ClinVar, it is important to note that
the construction of the URL varies slightly. While
access to dbSNP is performed with the unique
identifier of the variation, the step to ClinVar may
result in one or more queries with one or more
identifiers depending on whether the variation
makes one or more nucleotide changes.
To know these identifiers, an intermediate query
must be made in which the body of the previous
URL is maintained, but with new arguments as:
dbfrom: Source database (SNP). db: Target database
(ClinVar). id: dbSNP variant identifier. This query
generates an XML with ClinVar variations inside the
tags <Link> <Id> and </Id> </Link>. Also, it
shows the query parameters used, the source
database (dbSNP), the identifier and the query
database (ClinVar). Once the ClinVar identifiers are
obtained the next step is to formulate the URL with
the parameters required for the database, whose
structure is: Body URL, gives access to fetch (NCBI
Explorer tool). db: Selection of the database to be
consulted (ClinVar). rettype: Record type returned
(Variation). id: ClinVar variant identifier.
The result of this query contains all the variation
data in XML format, which must be processed in the
next step in order to adapt the information to the
requirements of the destination information system,
i.e., the Human Genome Database (HGDB).
4.3.2 ETL (Transform)
Once the information is extracted from the different
genomic repositories, the transformation stage is
responsible for the processing and validation of the
data, in order for the information to comply with the
requirements and rules of the target repository
system. For this, changes of format and codification
of values are performed, as well as obtaining of data
from other repositories, generation of new
identifiers, combination or division of attributes.
The following describes some of the transforma-
tions carried out to comply with the requirements of
the CMGH and allow the successful loading of the
data in the HGDB:
a) The CMHG contemplates four types of possible
variations: insertion, deletion, indel and
inversion. In contrast, ClinVar represents ten.
b) The identifier that NCBI assigns to the
transcripts and genes is obtained by
processing the "Others_identifiers" attribute of
the “Variation” class, eliminating the change
produced by the variation in each context.
c) ClinVar represents the strand by means of the
"-" and "+" signs, whereas in the CMHG the
characters "M" are defined from minus to "-"
and "P" from plus to "+".
4.3.3 ETL (Load)
The data loading is the last stage of the ETL process
(and the SILE "Load" stage). It should be noted that
the loading stage introduces all extracted and
transformed data into the destination repository
(HGDB). The information to enter is unique and
does not allow the existence of duplicates, which is
why before carrying out any load the genome
version (i.e., GRCh build 38) must be checked and
new studies regarding the variation must be checked.
In case the information stored in the HGDB is the
same as that extracted from the different
repositories, the load will not be carried out.
The prototype developed and one of the main
contributions of this research work begins with the
establishment of the connection to the database. For
this, the prototype has a section of management of
connections with which direct communication with
the server is verified. Once established and
validated, the application reports on the state of the
connection and allows the information load
extraction process (from the genomic repositories:
ClinVar, dbSNP, Gene, Nucleotide and Pudmeb).
If the information extracted from the repositories
is useful and if it is desired to be entered into the
database for subsequent exploitation, prior to
insertion, the application performs a duplication
check, and later, regardless of whether it performs
the validation, introduces all the biological
information obtained in the HGDB.
4.4 Exploitation
The last stage of the SILE methodology is the
exploitation of the data cured through the VarSearch
genomic framework, whose function is to obtain
knowledge by processing the information contained
in patient samples (provided by VCF or Sanger files,
ENASE 2018 - 13th International Conference on Evaluation of Novel Approaches to Software Engineering
which represent and store variations), and that
available one in the database of the human genome.
The web-based "VarSearch" genomic framework
starts with the selection and processing of the VCF
study file, which will contain the information of the
variations organized into blocks, representing the
position in which the variation occurs, the
chromosome, the unique identifier of the variation,
as well as the reference alleles and other biological
data. Once the analysis of the VCF file is finished,
the application shows two types of results: a)
variations found, which indicate the relationship
with the cataract; and b) variations not found, that is,
those found in the study VCF file (sample), but
whose information is not supported / stored by the
Genomic Information System (GeIS), and therefore,
are not related to cataracts. The detail of the
exploitation process can be consulted in (Navarrete-
Hidalgo, 2017).
However, the GeIS and the knowledge obtained
does not have a direct application in the population,
since the genomic framework acts as an interface
between the HGDB and domain experts (i.e.,
geneticists, clinical laboratories, biologists, among
others). Its application to end-users is intended to be
facilitated through what are known as "direct genetic
tests to the consumer", and for them a parallel
project called "GenesLove.Me" has been developed
(Reyes et al., 2017). The purpose of this web service
is to offer various tests for genetically based
conditions, such as: androgenic alopecia, lactose
intolerance, alcohol sensitivity or dupuytren's
disease (see in detail in
The design and implementation of a GeIS for
genomic diagnosis has been carried out using the
SILE methodology. In this work, a large part of the
processes has been automated, and in a greater
proportion the loading stage. This prototype ETL
has worked well for the case study, congenital
cataract. It has been shown that the developed ETL
application works as a tool for obtaining biological
information from multiple repositories from a single
input parameter, which reduces human interaction
with data sources from hours to seconds. To obtain
the biological information that documents the
variations different scientific repositories of the
NCBI have been studied, such as Gene, ClinVar,
dbSNP, Nucleotide, Protein and Pubmed. In this
sense, there would be a line of improvement
regarding the exploration of new data sources and
their standardization.
The most important future work would be to
provide the application with mechanisms for
automatic updating of stored information. This
would allow a direct comparison between the
existing information and that obtained from the
repositories, which are continuously updated with
new biological data, as well as the selective loading
of information depending on its version, veracity
and biomedical utility. On the other hand, the
application allows for a possible conversion of
desktop tool to web tool, thus allowing its use from
place, device and platform.
The authors would like to thank the members of the
PROS Research Centre Genome group for the
fruitful discussions regarding the application of CM
in the medicine field. In addition, we would like to
thank Fernando Cervera, Rubén Casatejada and
Mariano Collantes as experts in Biology for their
contribution to this research. This work has been
supported by the Generalitat Valenciana through
project IDEO (PROMETEOII/2014/039) and the
MICINN through project DataME (ref: TIN2016-
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