Use of GeIS for Early Diagnosis of Alcohol Sensitivity

José Fabián Reyes Román

1,2

and Óscar Pastor López

Research Center on Software Production Methods (PROS), DSIC, Universitat Politècnica de València,

Camino Vera s/n, 46022, Valencia, Spain

²Department of Engineering Sciences, Universidad Central del Este (UCE), Ave. Francisco Alberto Caamaño Deñó, 21000,

San Pedro de Macorís, Dominican Republic

Keywords: GeIS, SILE, Genomic Diagnostic, Massive Load, Selective Load, Bioinformatics.

Abstract: This study focuses on the importance of Genomic Information Systems (GeIS) today; the results of this

research provide great benefits to the medical community through technological potential. The development

of SILE (Search-Identification-Load-Exploitation) to GeIS improves the databases management with

curated data. The studies are focused on improving the quality of data and time optimization. With SILE we

perform a selective loading of genes and variations found for a specific disease from different data sources

like: NCBI, dbSNP and others. When we worked with a selected group of genes/variations it is possible

guaranteeing a more reliable diagnosis, thus sustaining the increase accuracy of the results with respect to

data quality and improvements over time. Also, we integrate the association of genes/variations with

population studies, for this way providing an early diagnosis for any disease of genetic origin.

1 INTRODUCTION

The development of Genomic Information Systems

(GeIS) brings a great challenge due to what is

known nowadays as genomic chaos, making its

application difficult to genetic tests. Developing and

managing databases for handling this data gives

many benefits to the scientific and technological

community. The area of Bioinformatics requires us

to have a strict control on data manipulation, as it is

based on large amounts of information and data

from different sources, so we must carry out

thorough study that would allow us to ensure

reliable results.

Some of the current data sources, such as NCBI

(Sherry et. al., 2011), OMIM (Hamosh et. al.,) and

Ensembl (Hubbard et. al.,) provide an extensive set

of information, so it is necessary to extract concise

information to help obtaining precise results. That's

why SILE methodology (Search, Identification,

Load, and Exploitation) has come to improve the

tasks of Extraction and Treatment of existing data

sources used.

The Search-Identification-Load-Exploitation

(SILE) is a methodology developed by Óscar Pastor

López within the PROS research group of the

Universitat Politècnica de València, aimed to

improve our process to load the genes and variations

of our Human Genome Database (HGDB).

Its initials are defined as follows:

Search - It consists of the exhaustive search of

scientific information (publications, articles, etc.)

To support the genetic association with a specific

disease.

Identification - is the process that involves the

medical intervention to provide support in

filtering genes and variations that have greater

incidence in the population.

Load - The process of loading the database, is

where we proceed to insert the genes,

chromosomes and variations to the database with

the information treated (curated) and validated,

the load is carried out through various tasks and

data sources.

Exploitation - The exploitation is based on the

contents and presentation of the final result.

SILE methodology is performed in various

databases that facilitate search and query of

biomedical information, in this case study we will

use NCBI. The National Center for Biotechnology

Information is a database (library) that collects

information on biomedicine, biotechnology,

biochemistry, genetics, genomics, and genetic

diseases, and others. NCBI also provides some

284

Román, J. and López, Ó.

Use of GeIS for Early Diagnosis of Alcohol Sensitivity.

DOI: 10.5220/0005822902840289

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 3: BIOINFORMATICS, pages 284-289

ISBN: 978-989-758-170-0

bioinformatics tools for sequence analysis of DNA,

RNA, and proteins; BLAST is a tool for sequence

alignment of the most used (Sherry et. al., 2001).

NCBI was established on November 4, 1988 in order

to maintain and quickly distribute the amount of

information about molecular biology (NCBI, 2015).

Figure 1: SILE methodology.

1.1 SILE Methodology Applied to

Alcohol Disease

Several studies over the years have supported and

justified the genetic implication of this disease.

Alcoholism is a progressive disease, chronic,

degenerative and genetic, with symptoms that

include a strong need to drink in spite of negative

consequences.

To illustrate these cases, in 2003 "Genetic

alterations related to alcoholism" was published in

the Journal of Neurology (Escarabajal, 2003) until

then, so continued and extensive research was done

on this; for years 2010 and 2011 there were many

contributions validating genes already found, and

showing future projections for new genes that are

under study (Bierut, 2011), (Wang et. al., 2011).

These works present the progress made and confirms

if our genes are related with the alcohol sensitivity

or not. This disease is harmful to the whole body and

especially to the liver, pancreas, heart and the entire

nervous system. Given the importance of the disease

and considering that this can directly affect the

general population, regardless of social status, age or

culture, for this reason we have started to carry out

experiments.

This paper is divided as follows: Section 2

presents the state of the art, which shows the

problems of today within bioinformatics and

manipulation of the big data sources. Section 3

shows the process of the methodology. Section 4

presents the studies to verify (validate) our

hypotheses, in this way we seeing increased quality

of genomic diagnostics. The results of these studies

are presented in Section 5. Finally, Section 6

presents the conclusions and future work.

2 GEIS EVOLUTION (GENOMIC

INFORMATION SYSTEMS)

Currently, the bioinformatics plays an important role

due to the contributions and advances provided to

medicine and technology. In the case of genetic

tests, it has served to provide diagnostic screening to

help preventing and/or treating genetic diseases. The

area of bioinformatics, as well as the study of

genetic diseases associated stays in constant

evolution. It was in 1977 when the DNA sequencing

and the development of software to analyze data

quickly started and for the following year the first

complete gene sequence of an organism was

published (Baxevanis & Ouellette, 2004). There are

countless benefits of genetic testing because they

allow us to identify mutations or alterations in genes,

which is of great use and interest to clinical

medicine, favoring the early diagnosis of diseases

(Villanueva et. al., 2012), (Villanueva Del Pozo,

2011). Similarly, bioinformatics community gives

us: the management of biological databases,

metabolic processes and population genetics,

artificial intelligence, and others (Dawyndt &

Swings, 2006).

Between the bioinformatics branches we can find

researches about: sequence analysis, genome

annotation, analysis of mutations in cancer,

comparative genomics modeling biological systems,

protein-protein coupling, etc. (Baxevanis &

Ouellette, 2004). For the year 2008 there were about

1,200 tests (Reyes, 2013) but they were limited and

expensive, so companies looked for ways to reduce

the cost to facilitate access to people from the

comfort of their homes.

Importantly 23andme is a private U.S. company,

which has been a major driving force in genetic

diagnostics. They have a wide range of services;

they provide information about the genetic history

(related to the ancestors) and personal health (risk of

disease). This data presented is based mostly on

probabilities (Goetz, 2007). Other companies have

been also developed like the case of Genotest

(Reyes, 2013), which provides genetic testing with a

variety of diseases and offers easy access to the

public (Genotest, 2015).

Use of GeIS for Early Diagnosis of Alcohol Sensitivity

285

2.1 Data Manipulation (Genomic

Databases)

Lots of studies have been carried out in the medical

and informatics community, with the aim to find

solutions to the problems of management of

genomic databases, mainly by having to manage

large data sources, so we need to invest more in

time, storage, research, etc. to further improve it.

With current tools and search engines, including:

dbSNP (Sherry et. al., 2011), OMIM (OMIM, 2015),

and others, we are unable to solve certain problems,

but we know we can improve it greatly, because the

use of curated databases allows us to optimize the

performance and report greater precision and

quality. With the execution of the SILE tasks we are

taking a step forward, because we get a better

definition of information, and our main goal is being

better and better. Another issue of discussion is the

relationship between genes (variations, mutations)

and a specific population, where we see how these

are more prevalent in one population or another. By

integrating (insertion) this part within our model of

the database, we will create a custom web services

with great reliability.

3 METHODOLOGY

The methodology used in the experiments are

implemented with SILE for the genomic domain.

3.1 Search

The SILE methodology to beside the medical

progresses (scientific community), and conducting a

series of studies to the alcohol sensitivity, have

allowed us to refer to a group of genes that are

highly associated with the disease.

Figure 2: Search and Identification Process (SILE).

3.2 Identification

Our studies focus on genes ADHIB, ALDH2,

GABRA2, which have validation and large amounts

of evidence and studies in the medical field. These

studies have allowed us to analyse variations as units

which are developed further in one population or

another.

Figure 2 presents the genes in which higher

incidences for alcohol sensitivity were found. In the

table 2 we can see information about the location of

genes, including a study of the population taken

from GenesCard (Bierut, 2011) as a summary of

NCBI (Wang et. al., 2011) of tests carried out to

different users. When we manage the selective load

(Reyes, 2013), we spent more time in the initial

stages of research and identification, this task helps

us to achieve a list of “genes + variations”

(mapping) that are curated and validated, which in

the future will guarantee a diagnosis more reliable

and with higher quality.

3.3 Load

The loading process is done with the database

schema presented in the figure 3 (Martín M., 2011):

Table 1: View of Database (version 3).

Table Description

Structural

Genome

Version of DNA with which to work.

Chromosome

Elements that form the DNA sequence

chromosomes.

Chr_Elem

Stores each of the elements belonging to

chromosome.

Gene

Gene table (Table Chr_Elem specialization) is

defined as one of the most important

asic units

within the chromosome, and containing the

information necessary for the synthesis o

macromolecules, proteins usually with a specific

cellular function.

Sequence_Ng

Reference sequences of each of the genes foun

in the table stored Gene.

Transcript

Chromosome element that stores the resulting

transcript of the transcription process.

xon

Basic unit forming the mRNA transcript.

xon_Transcript

Associating table which serves to form part o

the transcript exons reference has an associate

gene.

rotein

Result of the translation of the mRNA for those

genes that code for protein.

Variations

Variation

Is the main table in this view and represents the

differences between different individuals.

recise

Variation specialization table representing the

variations detected with known position within

the chromosome in the DNA sequence.

recise_SeqNg

Is the variation that is associated with the

osition that such variation is compared to

reference sequence of a gene.

henotype

Phenotype is associated with the one or more

DNA variations.

Consideration of the Population

opulation

With this table we can associate the variations

found for certain diseases, and thus see the

impact of variations against other when we

crossed the population aspect.

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The Table 1 shows different views to the

database that we use at the moment of our selective

load.

Figure 3: Database Schema: Variation + Structural View.

 Structural view: shows the structure of the

genome of organisms.

 Variations view: models the knowledge

related to differences in the DNA sequence to

different individuals.

And finally, it presents the consideration of

treatment of the populations for variations, and thus

the ability to generate an early diagnosis for any

disease.

3.4 Exploitation

The exploitation is the last stage of the SILE

methodology and their aim is the generation of

content from the point of view of customers or

stakeholders. The exploitation presents the results at

the end of the previous stages.

These contents include: text, images, etcetera.

And they must be clear and concise, so that

stakeholders can understand and manipulate all the

information effectively.

In our case, the exploitation process was

conducted with the design and implementation of a

Web Service, called “GenesLove.Me” (Reyes,

2013), that facilitates the acquisition of genetic tests

direct to consumers (GenesLove.Me, 2015).

4 IMPLEMENTATION OF SILE

The experiments will be conducted to verify the

application of SILE to a Genomic Information

Systems (GeIS), and thus achieving in this way:

efficient, reliable and agile systems.

4.1 Improved Quality and Time

Optimization

In case 1, we want to see if we can improve SILE

applying such circumstantial quality in generating

diagnoses, as well as achieving optimize load times.

We implement SILE in the “selective load”,

which helps us to improve efficiency to generate the

results (more accurate results). Because with this

type of load we focused only on the variations

associated with a disease and have been highly

proven through various studies. The other form is

with the "massive load" (Reyes, 2013) which taken

all genes and variations that are found for a disease,

without scientific merit because they are in

study/research, and that is why the percentages of

Table 2: INFO of Genes+SNPs and Populations Associated with Alcohol Sensitivity [Source: GenesCard].

Use of GeIS for Early Diagnosis of Alcohol Sensitivity

287

these diagnoses tend to be lower and require more

work. The data treatment with the selective load,

allows us to create clean and reliable databases

(repositories).

We must have processes that ensure information

filtering, otherwise, we are going to create databases

that are loaded with irrelevant data, and which do

not add value when we generated the diagnosis

(results). So, the selective load helps us to maintain

repositories with quality and optimal approaches.

4.2 Population

With this experiment we identify important trends

that can add value to the database. After applying

the “load process” with the help of selective-SILE in

the previous study, we found that combining this

with aspects related to the population, we take

another step forward as it would generate a result of

greater accuracy and especially that could secure an

early diagnosis for end-users.

Through many studies we have found that there

are genes which develop more in one population

than another, and this has major implications in the

results, because when we take into account and work

with general variations for all individuals, we

likewise generalize the diagnosis. Now, when we

use the specific variations, and we take the most

affected population in consideration, results can be

more precise.

5 RESULTS

5.1 Improved Quality and Time

Optimization

The use in our case of the selective load (Table 3),

increases genomic diagnostic quality when studying

a specific disease, and if we detected a small number

(curated) of gene + variations; we can obtain higher

precision percentages for the results (Figure 4).

Table 3: Precision Studies by Genes+Variations (Alcohol-

S).

5.2 Population

The study of population consisted of men and

women of full age, of different races and cultures.

Figure 4: Shows graph according to the type of load to

obtain accuracy level with respect to genes + variations.

Table 4: Genes and Variations by Population (Example).

When we use the massive load, it generates a

very general result, but turning to the selective load

it is possible to provide early diagnosis, simply

setting the initial observation of an ethnic group or

population of origin (Table 4).

6 CONCLUSIONS AND FUTURE

WORK

With the application of the SILE methodology we

obtained positive results, which as shown can be

improved circumstantially. Through our experiments

we have discovered key trends that help us add value

to our database.

We have concluded that we obtain an increase in

quality and optimization time using the selective

load, as we have the necessary data to generate

diagnoses, instead of having a database loaded with

all the information they provide, which is not

curated (filtered) and much of it without medical

validation.

We have also learned that by including

population information to previous studies, we can

deepen the diagnostics and take a step forward, since

there are variations of genes that affect some

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populations more than others.

The implementation of this allows us to generate

and provide to the end-users an early diagnosis

about any disease (of genetic origin) with great

quality. The future work is oriented in the

implementation of the haplotypes for the human

genome database (HGDB). These are of great

interest because there are diseases that are diagnosed

by the particular combination of alleles for two or

more SNPs (Single-nucleotide polymorphism) that

are in the same chromosome. It is important in the

genetic diseases that are identified by the haplotype

variation and not as a single variation.

The development and growth in this area is

beneficial in the generation of diagnostics, and

particularly in the incorporation of

biopharmaceuticals for treatment and prevention to

the end-users. As the years pass, new variations are

considered to be associated with the alcohol

sensitivity, and it is only a matter of further research

and analysis of new samples that would allow the

medical community to give the approval for new

genes/variations.

ACKNOWLEDGEMENTS

The author thanks Ainoha Martín Mayordomo,

Mercedes Rossana Fernández Alcalá, David Roldán

Martínez and Edgars Groza for critically reading this

manuscript. We also thank the members of the

PROS Center Genome group for fruitful discussions.

In addition, it is also important to highlight that

this work has been supported by the Ministry of

Higher Education, Science and Technology

(MESCyT). Santo Domingo, Dominican Republic.

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