Genetic Mapping of Diseases through Big Data Techniques

Julio C. S. dos Anjos

, Bruno Reckziegel Filho

, Junior F. Barros

Raffael B. Schemmer

, Claudio F. R. Geyer

and Ursula Matte

Universidade Federal do Rio Grande do Sul, GPPD, Institute of Informatics,

Caixa Postal 15.064, 91.501-970, Porto Alegre, RS, Brazil

Laboratory of Gene Therapy Hospital de Cl´ınicas de Porto Alegre,

Rua Ramiro Barcelos, 2350, 90035-903, Porto Alegre, RS, Brazil

Keywords:

Data Intensive Computing, MapReduce, Parallel Distributed Processing, Genome Annotation.

Abstract:

The development of sophisticated sequencing machines and DNA techniques has enabled advances to be made

in the medical ﬁeld of genetics research. However, due to the large amount of data that sequencers produce,

new methods and programs are required to allow an efﬁcient and rapid analysis of the data. MapReduce is a

data-intensive computing model that handles large volumes that are easy to program by means of two basic

functions (Map and Reduce). This work introduces GMS, a genetic mapping system that can assist doctors in

the clinical diagnosis of patients by conducting an analysis of the genetic mutations contained in their DNA.

As a result, the model can offer a good method for analyzing the data generated by sequencers, by providing a

scalable system that can handle a large amount of data. The use of several medical databases at the same time

makes it possible to determine susceptibilities to diseases through big data analysis mechanisms. The results

show scalability and offer a possible diagnosis that can improve the genetic diagnosis with a powerful tool for

health professionals.

1 INTRODUCTION

The information provided by DNA sequencing is

crucial to ensure the success of biological research.

Biotechnology is one of the ﬁelds that has beneﬁted

the most from this kind of information, especially for

the development of new pharmaceutical substances,

foodstuffs, pesticides and agricultural products, and

when conducting a clinical diagnosis (William J and

Palladino, 2012).

DNA sequencing was once a very expensive pro-

cess, but currently there are techniques and machines

that are considerably more cost-effective. The Na-

tional Human Genome Research Institute (NHGRI)

has shown that prices fell between the years 2007 and

2008, when the sequencing methodology migrated

from Sanger to what was then called Next Genera-

tion Sequencing (NGS). The time taken for sequenc-

ing has also increased. As shown in Table 1, a few

NGS machines (particularly those that use ion semi-

conductors such as Ion Proton

(Scientiﬁc, 2014) are

able to sequence millions of base pairs in a few hours,

and hence can produce large amounts of data very

quickly. However, at present there is a limited num-

ber of tools that can analyze the produced data in an

Table 1: Technical speciﬁcations of Ion Torrent sequencing

machines.

Machine Models

Characteristics PGM318 PI PII PIII

Sensor Number ∼11 M ∼165 M ∼660 M ∼1.2 B

Input Size ∼2 GB ∼10 GB ∼32 GB ∼64 GB

Execution Time 4∼7 hrs 2∼4 hrs 2∼4 hrs 2∼4 hrs

Average Read 400 BP 200 BP 100 BP 100 BP

Number of Reads ∼5.5 M ∼82 M ∼330 M ∼660 M

Key: M = Million B = Billion BP = Base Pairs GB = GigaBytes

automated and rapid fashion.

In this context, MapReduce computing is an at-

tractive solution as it implicitly offers a parallel dis-

tributed solution for processing large amounts of data,

such as those produced by sequencers. The MapRe-

duce framework is easier to program and has a dis-

tributed ﬁle system that other parallel systems do

not have, e.g. MPI. MapReduce improves accuracy

and reduces the complexity of biological applications,

such as, multiple sequence alignment algorithm (Zou

et al., 2014). This means that MapReduce is a frame-

work model that is suitable for this study. In addition,

the existing MapReduce implementations, such as

Hadoop (an open-source implementation maintained

279

Santos dos Anjos J., Reckziegel Filho B., F. Barros J., B. Schemmer R., Geyer C. and Matte U..

Genetic Mapping of Diseases through Big Data Techniques.

DOI: 10.5220/0005365402790286

In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 279-286

ISBN: 978-989-758-096-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

by Apache Software Foundation) handles as fault-

tolerance and performance mechanisms such as load-

balancing, automatically.

There is an huge variation in human genomes that

can be determined by massive parallel sequencing.

However, many of these variations are not clinically

relevant. Thus, there is a great need for methods

that can discriminate between disease-causing muta-

tions and normal genetic variability in a short run-

time (Frebourg, 2014).

The purpose of this work is to develop a genetic

mapping system that can assist doctors in the clin-

ical diagnosis of patients by conducting an analy-

sis of the genetic mutations contained in their DNA.

The scientiﬁc goal is to provide an efﬁcient method-

ology for the genetic mapping of diseases with big

data systems. The execution time of this GMS

must be scalable with regard to the amount of pro-

cessed data. The data is produced by sequencers

that employ the ion semiconductor technology. We

used the Hadoop MapReduce system and databases

from the Gene Report (NCBI, 2014) and Ensembl

(Kinsella et al., 2011). This work is available in

<https://github.com/GeneticMapping/GSM.git>.

The remainder of this work is structured as fol-

lows. Section 2 outlines the background that is re-

quired for the deﬁnition of the problem, the effects

of mutations on gene sequences and data required for

genetic diagnosis; it also, provides an overview of

the MapReduce programming model and the Hadoop

platform. Section 3 examines the related work in the

literature. Section 4 givesa detailed description of our

proposed solution. In Section 5 there is an account of

the methodology employed together with an analysis

of the obtained results. Section 6 summarizes the con-

clusions and make recommendations for future work

in the ﬁeld.

2 BACKGROUND

This section provides background information on

genetics and sequencing and their application in

medicine. It also examines the technologies used in

these areas and the problems currently faced in ﬁnd-

ing a suitable means of analyzing the impact of muta-

tions on the genome sequencing of patients.

2.1 Nucleotides, DNA, RNA and

Genome

Nucleotides are biological molecules that compose

the structure of nucleic acids (Alberts et al., 2014).

They are formed from a purine base (adenine or

guanine) or a pyrimidine base (thymine, cytosine or

uracil). Nucleotides are the basis for forming nucleic

acids that are large biological molecules, and essential

to all known forms of life. Deoxyribonucleic Acid

(DNA) contains the genetic code of a living being,

which comprises long sequences of nucleotides that

form a double helix structure. The Ribonucleic Acid

(RNA), which is often formed by a simple chain, syn-

thesizes the proteins from a cell on the basis of ge-

netic information contained in the DNA and conveyed

in the Messenger RNA (mRNA). The basic differ-

ences between the DNA and RNA structures lies in

the number of helices each has (the former has two

whereas the latter has only one), and in the change

of a base from thymine to uracil (William J and Pal-

ladino, 2012).

The DNA physical structure has a three dimen-

sional shape, which is the result of the forces exerted

by the electrons that compose the bases. Hence, any

change in one of the bases, alters the position of the

helix and its shape, and turns its function into a bi-

ological system. The change in molecular structure

can lead to a genetic mutation that may, or may not,

be associated with the propensity an individual has to

contract a certain disease (Sawyer et al., 2007).

By complying with the rules laid down by the ge-

netic code, the genetic information provided by the

DNA sequences via mRNA is translated into an amino

acid sequence, thus generating a protein (William J

and Palladino, 2012). In this process, as shown in

Figure 1 which is adapted from (William J and Pal-

ladino, 2012), each triplet of nucleotides is called a

codon, and each codon represents an amino acid, that

synthesizes a protein. However, only the genomic re-

gions deﬁned as coding regions are considered in the

process of protein translation.

Figure 1: Central dogma of molecular biology.

The genome represents all the genetic information

inherited by an organism. The human genome con-

sists of a large amount of DNA divided into organized

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structures called chromosomes. The human genome

contains 23 chromosome pairs and an estimated num-

ber of 32,000 genes. The total number of DNA bases

is around 3.6 billion.

2.2 Mutations, Polymorphism and

Clinical Genetics

A mutation is deﬁned as a change in the nucleotide

sequence of an organism. Mutations can be caused by

the irreparable damage suffered by the genome, errors

in the replication process or the insertion/deletion of

DNA fragments by mobile genetic elements. Several

studies, such as those carried out by (Johnsen et al.,

2013), suggest that, if a mutation changes the pro-

tein that a gene produces, the end result will likely

be harmful to the organism. The coding region of a

genetic code begins with a start codon and ends with

a stop codon; this region is the focus of our analysis.

However, it should also be noted that, some mutations

do not modify the amino acid generated (i.e., even if

a mutation takes place, the generated protein remains

the same).

Polymorphism is a kind of mutation that takes

place with a frequency greater than 1% in a popu-

lation, and can be divided into distinct, well-deﬁned

classes. An example is the groups of the ABO blood

group system (classes A, B, AB and O). According to

(Nussbaum et al., 2013), there are three classes of mu-

tations, namely those that affect the number of chro-

mosomes in a cell (also called genomic mutations),

those that alter the structure of speciﬁc chromosomes

(chromosomal mutations), and mutations that change

individual genes (gene mutation). This work focuses

on conducting an analysis of gene mutation; however,

the impact of other mutations on the health of patients

will be addressed in future work.

2.3 MapReduce

MapReduce is a programming framework that ab-

stracts the complexity of parallel applications by par-

titioning and scattering data sets across hundreds or

thousands of machines, and by bringing computation

and data closer (Dean and Ghemawat, 2010). The

Figure 2, adapted from (White, 2012), shows the MR

data ﬂow. The Map and Reduce phases are handled by

the programmer, whereas the Shufﬂe phase is created

while the job is being carried out. The input data is

split into smaller pieces called chunks, that normally

have a size of 64 MB. The data is serialized and dis-

tributed across machines that compose the Distributed

File System (DFS).

When running an application, the job is split by

the master into several Map and Reduce tasks; fol-

lowing this, it assigns tasks to workers that then

run each processing stage. The machine that is

given a Map task, handles a Map function and emits

key/value pairs as intermediate results that are tem-

porarily stored in the worker disks. The execution

model creates a computational barrier, which allows

tasks to be synchronized between the Map and Re-

duce. A Reduce task does not start its processing until

all the Map tasks have been completed.

A hash function is applied to the intermediate data

produced during the Map phase to determine which

keys will compose a Reduce task. All the pairs com-

bined with these keys are transferred to one machine,

during the Shufﬂe, so that they can be processed by

a Reduce task. After a reduction function has been

applied to this data, a new key/value pair is issued.

The result is then stored in the distributed ﬁle sys-

tem and thus can be made available to the client who

submitted the job. The Shufﬂe phase consists of two

stages: one performed on the machine that processes

the Map task, which sorts the keys and serializes the

data. The other is performed after the intermediate

data has been sent to the reducer machine, which ar-

ranges the received data to allow the keys to be prop-

erly grouped and then runs the Reduce task. The

Hadoop implements the Combiner function to save

bandwidth. The Combiner is a preprocessing of inter-

mediate keys into memory of workers during the map

phase to form the output for input data in the Reduce

phase. (White, 2012).

Figure 2: MapReduce data ﬂowchart.

The management mechanisms, data replication

and execution control were added to the framework

during Hadoop implementation. The management ar-

chitecture is based on the master/worker model, while

a slave-to-slave data exchange requires a P2P model

(White, 2012). Hadoop MapReduce is a implemen-

tation for a wide range of Big Data applications used

by large companies like Facebook, Amazon, Cloud-

era and IBM. Most works, (including this system) are

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based on a Hadoop implementation which is regarded

as the most advanced open-source implementation of

MapReduce (Dean and Ghemawat, 2010).

3 RELATED WORKS

The advanced technique provides a collective solu-

tion for the problems currently facing mankind and

the NGS genome. It is a challenge to data process-

ing, in terms of computational resources (O’Driscoll

et al., 2014). When dealing with big data, it is nec-

essary to build new models or adjust existing ones by

taking account of the data dispersion and processing

capability. In establishing the relationship between

MapReduce and genomic analysis, it is necessary to

understand the characteristics that make the model

important for this research ﬁeld. When supported

by the MapReduce model, the application develop-

ment is particularly well suited to common “map-

reduce scan” pipelines. Schatz introduced some im-

portant tasks for application development that can be

achievedwith MapReduce (Schatz, 2010), such as the

following:

1. Map: Genomic problems which are composed of

many sequences that can be mapped to the refer-

ence genome in parallel with multiple machines.

2. Reduce: Sequences of alignments are aggregated

so that all the alignments on the same chromo-

some or locus are grouped together and sorted by

position.

3. Scan: The sorted alignments are scanned to iden-

tify biological events, such as polymorphism or

different expressions.

On the basis of the above, it is worth discussing

the open MapReduce framework (Apache Hadoop)

that is used to build apps that conform to the needs

of genomics. In addition, there are libraries such as

Hadoop-BAM (Niemenmaa et al., 2012) that are used

to dealing with BAM ﬁles (a compressed ﬁle format

that is used for NGS and usually well suited to pro-

cessing genomic sequences in a large number of ma-

chines). Crossbow (Gurtowski et al., 2012) is another

example of how Hadoop can be used to discover Sin-

gle Nucleotide Polymorphism (SNP) from sequences

of data in cloud systems or Hadoop clusters. In a sim-

ilar way, our work is able to ﬁnd gene mutations at

the ﬁrst moment and afterwards, when making com-

parisons with databases, may detect possible diseases.

In the genomic mapping of sequences, CloudBurst

(Schatz et al., 2010) and ClodAligner (Nguyen et al.,

2011) are both cases where Hadoop can be used for

data analysis based on NGS sequencers for discover-

ing SNP sequences, as well as to build alignment for

short-reads or long-reads of genomic data in reference

to a human genome. It is worth taking note of the ca-

pability for scalability in these works through cluster

and cloud, as is shown in our work.

From the perspective of adopting an interface that

is user-friendly and easy for users, there are a range of

approaches that use MapReduce, such as the Genome

Analysis Toolkit (GATK) (McKenna et al., 2010)

where the aim is to create a functional language con-

trol ﬂow by supporting the development of an NGS

application program. When compared with a tradi-

tional programming structure, it requires less time for

development. Another example is CloudDOE (Chung

et al., 2014) that frees scientists from the need to carry

out complicated procedures. Our study makes possi-

ble to create a system that saves time for user when

are genomic problems are being addressed.

In the case of health-care, Hansen stresses the ben-

eﬁts that can be derived, (such as Big Data), from un-

derstanding factors such as the prevention of diseases,

detection of modiﬁable risk factors for disease and

intervention to bring about behavior change (Hansen

et al., 2014). As mentioned earlier, accessibility is

an important point to take into account. Our study

checks several disease databases to ﬁnd a possible

relation of diseases with genomic mutations, with a

clear and simple method, based on the data output of

the Clinical Hospital of Porto Alegre (HCPA) scien-

tists. This study has the potential to help to ﬁnd mu-

tations that are able to generate pathologies prior to

their action on an individual.

3.1 Genome Annotation

According to Costa, it is difﬁcult to ﬁnd an equivalent

system to genome annotation (Costa, 2014). There

are few Big Data projects that are aimed at supporting

the clinical area. The author cites Appistry Cloud, as

a cloud computing system for genetic analysis which

is concerned with oncology research and the develop-

ment of new drugs. Unlike our implementation, this

seeks to provide a tool to annotate generic patholo-

gies. CLC Bio is another work cited by Costa, as a

system for diagnosing cancer pathologies, although

this requires the user’s computer to install an appli-

cation to insert data and create scripts. These ap-

proaches are very complex for users without experi-

ence, in contrast with GMS, where the user only needs

to execute a web application.

The DNAnexus project (BCM, 2014) provides so-

lutions for DNA sequencing centers using NGS se-

quencers, since the application is developed for a

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Cloud platform, but with an analysis and sequenc-

ing linked to DNA alignment. Although the analysis

does not involve polymorphism detection. The NGS

project (McKenna et al., 2010) shows a toolkit imple-

mentation in a MapReduce framework for an exome

analysis, the goal of which is capture sequencing , and

unlike other studies, our implementation is aimed at

identifying signiﬁcant genetic mutations from a gene.

4 DESCRIPTION OF THE MODEL

Currently there are only a few applications for clinical

diagnosis that provide an automated genetic analysis

of patients with high scale of amount of data gener-

ated by NGS. A manual method was previously used

from the clinical diagnosis of patients by researchers

at the Clinical Hospital of Porto Alegre (HCPA), in

Brazil. Genetic data for each research study was ob-

tained from sequencers and entered by hand into Ex-

cel spreadsheets. After the data had been allocated

correctly, they had to be separated into codons and

comparedwith a mutation list that included the known

diseases. This process is time-consuming.

The Ion Torrent sequencers can generate data in

multiple formats, such as BAM, VCF and FASTA

(MEDLINE, 2013). The databases that provide ref-

erences for research are widely dispersed among a

hierarchy of classes. Several of these databases are

maintained by Government bodies such as the Na-

tional Center for Biotechnology Information (NCBI):

the database that stores information about several spe-

ciﬁc genetic mutations (dbSNP); the database that ref-

erences to the dbSNP and provides information about

mutations (Ensembl); the database that keeps infor-

mation about the composition of several coding ar-

eas of genes (CCDS) and the Gene Report, a database

that keeps information about mutations that are con-

sidered pathogenic, directly related to diseases. The

input data format used for these studies is similar to

FASTA.

This work implements a code by using the MR

programming model of Hadoop to analyze and pro-

cess the amount of data generated by the sequencing

process. Before starting the MR processing, the in-

put data is previously preprocessed by python scripts.

This preprocessing step indexes a cluster of genomic

analysis from several patients and forms a single in-

put data ﬁle. Each line of this ﬁle is composed by one

gene from one patient. The line is tagged by the pa-

tient identiﬁer and the name of the gene. These data

are used in MR processing to determine how the gene

reference base should be used in the processing. This

model assumes that the input ﬁle is formed by differ-

ent kinds of genes and patients.

Figure 3 shows an overview of the ﬂowchart of

this proposal. The ﬂowchart is divided into three

stages. The ﬁrst stage is responsible for persisting

the input data composed by the patients’ gene in the

Hadoop distributed ﬁle system (HDFS), where the

data is split into chunks. Each chunk consists of sev-

eral lines of the input data, where each line corre-

sponds to one gene from each patient. In the sec-

ond stage, a preprocessing of the input ﬁle is carried

out to ﬁnd gene’s list from all patients. One Python

script search in CCDS database to ﬁnd a reference

gene code, and index this information in a local mem-

ory cache of each worker node. The third stage is to

compose the annotation by using the code that imple-

ments the MR processing ﬂow. The code consists of

two methods.

Figure 3: Flowchart of the proposal.

The ﬁrst method is calling Map and is responsible

for reading the input data formed by the gene analysis

in patients and to compare the data with the reference

gene. The Map function notes the position where the

differenceis found, the reference tuple of the gene and

the input tuple of the patient gene. When a difference

is found in the input, this is compared with a reference

of genetic mutation in both Ensembl and Gene Report

databases. This comparison executes into Combiner

on Map phase.

The Combiner receives this data and searches in

the mutation databases. The search uses the position

and the name of the gene to query the databases and

writes the pathology data found. If one pathology is

reported, the Map emits a key/value pair intermedi-

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283

ate. The key is formed by the position in the gene and

the value within a tuple of a reference gene and the

patients’ gene.

After, the Reduce function emits a new key/value

pair with the information about any associated

pathologies, for each patient and saves on HDFS.

Only mutations found in the databases are written in

the output, followed by any messages from one asso-

ciated pathology to this mutation.

5 EVALUATION

This section describes the environment setup and re-

sults of the evaluation as a means of demonstrating

the features and scalability of our proposal.

5.1 The Environment Setup

Two environments have been considered. The ﬁrst is

a cluster machine comprising 19 nodes with a hetero-

geneous conﬁguration. In more detail, the cluster is

composed of 5 machines with a P4 2.79GHz proces-

sor, 2GB of RAM, and 400GB of hard disk; 14 ma-

chines with an Intel P4 2.79GHz Hyper Thread pro-

cessor, 2GB of RAM and 1TB hard disk; and a mas-

ter node with a QuadCore 3.1GHz processor, 4GB of

RAM and 1TB hard disk. The system is the Hadoop

version 1.0.4. The second is a cluster in Cloud Mi-

crosoft Azure that comprises 19 nodes, A1 type. This

node comprises a single core of Intel Xeon E5-2660,

1.75GB of memory RAM and 30GB of HDD. All of

the nodes have the same conﬁguration. This study

relies on the same version of Hadoop and Linux ker-

nel in Azure in an attempt to approximate the results.

Azure settings were used to ensure that all the nodes

were allocated in the same geographic location.

In the experiments, we vary the number of used

worker nodes (i.e. 1, 2, 5, 10, 15 and 19) and the input

data size (i.e. 2GB, 10GB and 20GB). The number of

executions generated 18 different cases and each case

was executed 30 times, thus resulting in 540 experi-

ments being performed in the cluster. In all the tests,

the block size used by Hadoop for the data replication

and processing was 64MB and the replication factor

was set to 3. The standard deviation was lower than

5%.

5.2 Results and Analysis

The experiment introduced by Figure 4 and Figure 5

shows the GMS execution time of the cluster and the

cloud. In the same conﬁguration, it is often observed

that as the data input size increases, the executiontime

increases proportionally in the same way. The execu-

tion time is ≅ 2 hours for one standalone machine

(Intel P4 2.79GHz, 4GB RAM, HD 500GB).

Figure 4: GMS execution time in the GradeP cluster.

Similarly, when more nodes were added to al-

low the processing task to be carried out in a paral-

lel way, the time decreases proportionally to an in-

crease in the number of nodes. This occurs because

the data-intensive applications, developed by means

of Hadoop and the MR programming model, have a

greater scalability and better performance when there

is a larger volume of data.

Figure 5: GMS execution time in Cloud (Microsoft Azure).

The results showed that even the cloud conﬁgu-

ration has more powerful resources like newer CPU

cores than those of the cloud, the execution time is

slower than with the cluster. This can be explained

in function of shared network overhead on cloud. In

addition, Azure has to control the state of the virtual

machine that increases this overhead. However, both

of the conﬁgurations are scalable, which reduces the

run-time of all the input sizes in all the variations of

the node numbers. This shows that the solution pro-

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posed by our work can be used either in cloud or clus-

ter environments, because it is scalable when there

is a large conﬁguration of machines, and since the

speedup is considerable, it sets out from a sequential

time provided by one node. In addition, the increase

of the input size in the DNA sequencing increases the

requirements and the problem of the processing can

be resolved by adopting these approaches and tech-

niques.

Figures 6 and 7 show the speedup. This met-

ric represents the acceleration of sequential execution

time when more than one node is used. The ideal

speedup is always the number of nodes used in the

execution.

Figure 6: GMS speedup in a Cluster.

Figure 7: GMS speedup in Cloud (Microsoft Azure).

The results in both cluster and cloud show that the

increase of speedup is related to the size of the input

data. In all the scenarios, the speedup improves when

more data are employed in the processing; this be-

havior is linked to MapReduce execution in Big Data.

The overhead required in Cloud for managing the re-

sources causes wait for the data to be processed.

The size of the input data can cover this time.

These results prove that large distributed machines

(ranging from a dozen to thousands of computed

nodes) correspond to the size of the data generated by

NGS sequencers, like Ion Torrent for example. The

last and most important point that needs to be made

is the question of achieving a better speedup by clus-

ter than by cloud. This performance is linked to the

execution time and the overhead (related by cloud)

which involves managing costs and sharing resources

through a network connection with the nodes.

5.2.1 Output Analyzis

The Figure 8 shows one example from the GMS

output. The detection of mutation A>G Glu-Gly

(pathogenic germline) in Position 2 of gene 297 com-

pared with reference DNA indicates the susceptibility

of colorectal cancer. The detection of mutation G>A

Arg-Gln in Position 2 of gene 957 compared with ref-

erence DNA, indicates the susceptibility to gallblad-

der cancer. This information demonstrates the correct

framework execution.

Figure 8: GMS output.

6 CONCLUSION

This work proposes and implements GMS to ana-

lyze and compute large amounts of data generated by

NGS sequencers such as Ion Torrent. In addition, this

study is a part of a research project that seeks to auto-

mate the process of collecting and processing of pa-

tient gene analysis, using techniques like a Big Data

framework and distributed environments to overcome

the limitations of traditional technologies, related to a

single personal computer.

The proposed GMS addressed the problems aris-

ing from automation and the handling of the data.

The detection of mutations e.g. A>G in Position

2 of gene 297 compared with reference DNA and

G>A in Position 2 of gene 957 compared with ref-

erence DNA, indicating the susceptibility of deter-

mined cancer type, demonstrates that our GMS pro-

posal achieves its initial goal. This work makes use of

two scenarios involving distributed machines, a clus-

ter and cloud computing. In the same scenarios, the

application proposes to enhance performance when

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285

using a greater number of resources. The results sug-

gest that both cluster and cloud can be used to achieve

a reduction of run-time, but the cloud scenery have a

overhead improved.

In future work, we believe that the researches

should to improve the features supported by the pro-

posed, such as those that concern generating feedback

from a mutation found in a patient gene to a database

of mutations like Ensembl or Gene Report can be im-

plemented. Other future study could attempt to im-

plement this system, for example, to ﬁnding an online

user-friendly solution in cloud. However, many issues

need still to be discussed to be explored graphic inter-

faces for the use of cloud to persist gene analysis of

the patients.

ACKNOWLEDGEMENTS

This work was supported by several institutions, in-

cluding the following: CNPq through grant number

MCT/CNPq N

70/2009 – PGAEST- MCT/CNPq;

FAPERGS via grant FAPERGS/CNPq 008/2009. It

was also assisted by the research project “GREEN-

GRID: Sustainable HPC”; Grid5000, a grid platform

developed by the INRIA ALADDIN project, and the

support provided by CNRS, RENATER and other uni-

versities. Some experiments were assisted by Mi-

crosoft Azure environment from Microsoft Research.

The research was also partly sponsored by CAPES

grant 99999.014966/2013-01(through DSE program)

and by CNPQ-PIBITI-UFRGS FUNTTEL - MARE-

MOTO project. Members from the GPPD MapRe-

duce group also assisted in the development of this

project.

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