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

1,2

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.

1 INTRODUCTION

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

267

bodies (The National Eye Institute, 2017)

(https://nei.nih.gov/).

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

person.

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.

2 STATE OF THE ART

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-

(http://www.uml.org/). 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

268

 Transcription View: models transcription

processes.

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

respectively.

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.

3 CONCEPTUAL MODEL OF

THE HUMAN GENOME

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.

4 SILE METHODOLOGY

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

269

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

"VarSearch".

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

rs864309702

CRYAA 21q22.3 AD rs864309685

rs397515625

CRYAB 11q23.1

rs144451841

CRYGC 2q33.3

rs587778872

CRYBB1 22q12.1

E/AD

rs864309682

CRYBB2 22q11.23

E/AD

rs864309683

GJA3 13q12.11 AD rs864309687

rs864309691

rs864309694

GJA8 1q21.2 AD

E/AD

AD/AR

E/AD

rs80358205

rs864309677

rs864309684

rs864309688

rs864309703

MAF 16q23.2 AD rs864309678

rs786205222

rs864309692

rs864309695

MIP 12q13.3 E/AD rs864309693

NHS Xp22.13 AD

XL/AD

rs864309679

rs111534978

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

270

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

(https://www.ncbi.nlm.nih.gov/). 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¸ https://www.w3.org/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

files.

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).

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271

Table 4: Arguments allowed in the query URL.

Argument Parameter Description

db gene

snp

pubmed

nuccore

protein

Selection of the

database to consult.

id Identifier Identifier of the item

to consult.

retmode xml

txt

json

Output format of the

data. By default, is

XML.

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

272

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 http://geneslove.me).

5 CONCLUSIONS AND FUTURE

WORK

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.

ACKNOWLEDGEMENTS

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-

80811-P).

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