Ijcet 06 10_003

http://www.iaeme.com/IJCIET/index.asp 17 [email protected]

International Journal of Computer Engineering & Technology (IJCET)

Volume 6, Issue 10, Oct 2015, pp. 17-33, Article ID: IJCET_06_10_003

Available online at

http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=6&IType=10

ISSN Print: 0976-6367 and ISSN Online: 0976–6375

© IAEME Publication

___________________________________________________________________________

A NOVEL BIO-COMPUTATIONAL MODEL

FOR MINING THE DENGUE GENE

SEQUENCES

T. Marimuthu

1Research Scholar, Manonmaniam Sundaranar University,

Tirunelveli, Tamilnadu, India

V. Balamurugan

Department of Information Technology, AMET University,

Chennai, Tamilnadu, India

ABSTRACT

The evolution of dengue viruses has a major impact on the causes of dengue

disease around the world. The analysis and interpretation of relationship among

the dengue viruses have become a tedious problem due to the lack of

computational models. Although, the biological models available like

phylogenetic analysis which reveals the association between the dengue viruses,

the computational techniques are required for further analysis such as to find the

classification of new evolutionary virus type, DNA and RNA variation, protein

structure prediction, protein-protein interaction. In this paper, we propose a bio-

computational model called ‘Sequence Miner’ to interpret the relationship among

the dengue viruses. In addition to that, the proposed model performs the

classification among the given set of gene sequences based on novel periodic

association rules and visualizing the results through the interactive tool. If the

structure of a protein is known, it would be easier for the biologist to infer the

function of the protein. However, it is still costly to decide the structure of a

protein via biological models. On the contrary, protein sequences are relatively

easy to obtain. Therefore, it is desirable that a protein’s structure can be decided

from its sequence through computational models. The accuracy of the proposed

model is 96.74 % which is calculated by giving the 10,735 varying length of the

sequences as the input, 10, 198 sequences are correctly classified.

Key words: DNA, RNA, Protein, classification, periodic association rules,

phylogenetic tree and dengue virus.

Cite this Article: Marimuthu, T.

and Balamurugan, V. A Novel Bio-

Computational Model for Mining the Dengue Gene Sequences. International

Journal of Computer Engineering and Technology, 6(10), 2015, pp. 17-33.

http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=6&IType=10

T. Marimuthu and V. Balamurugan


1. INTRODUCTION

Bioinformatics has evolved and expanded continuously over the past four decades and

has grown into a very important bridging discipline in life science research. With the

advent of high-throughput biotechnologies, biological data like DNA, RNA, and

protein data are generated faster than ever. Huge amounts of data are being produced

and collected. The biologist needs computational models to help manage and analyze

such large and complex data sets. Database and Web technologies are used to build

plenty of online data banks for data storage and sharing. Most of the data collected

have been put on the World Wide Web and can be shared and accessed online. To

know the updated numbers of complete genomes, nucleotides, and protein coding

sequences, the reader can check the Genome Reviews of EMBL-EBI

(http://www.ebi.ac.uk/GenomeReviews/stats/). The researcher is also referred to

Protein Data Bank for the number of known protein structures. As for the analysis of

the data, data mining technologies can be utilized. The mystery of life hidden in the

biological data might be decoded much faster and more accurately with the data

mining technologies. To follow the scientific output produced regarding a single

disease, such as dengue, a scientist would have to scan more than a hundred different

journals and read a few dozen papers per day. Currently different biological types of

data, such as sequences, protein structures and families, proteomics data, gene

ontologies, gene expression and other experimental data are stored in distinct

databases [1]. Existing databases or data collection can be very specialized and often

they store the information using specific data formats [11].

The challenge lies in the analysis of a huge amount of data to extract meaningful

information and use them to answer some of the fundamental biological questions. So,

there is the need to develop an interactive tool to visualize the representation of

information together with data analysis techniques to simplify the interpretation of data.

The incidence of dengue has grown dramatically around the world in recent decades.

Over 2.5 billion people, 40% of the world's population, are now at risk on account of

Dengue. World Health Organization (WHO) currently estimates that there may be 50–

100 million Dengue infections worldwide every year. As per the medical record of

Government of TamilNadu, India, 15,535 persons were affected and 96 were expired in

the year 2009. The outbreak of dengue in India in the year 2012 was the worst in the

previous six years. In the months of December 2014 and January 2015 alone, nearly 20

persons including children, expired in the Virudhunagar District of TamilNadu [9].

Under these circumstances, the work on the genome sequence of dengue virus

plays a vital role in the diagnosis of the disease. Therefore, it is necessary to predict

the presence of co-occurrence patterns which are the similar elements present in

dengue gene sequences. The dengue virus belongs to Flavi viridae family that is

transmitted to people through the bite of the mosquitoes named Aedes aegypti or

Aedes albopictus. Serotype refers to the subdivisions of a virus that are classified

based on their cell surface. They are listed in the Table-1.

Table 1 Types of Dengue Virus Serotypes

Virus type Name of the Virus

DEN-1 Strain Hawaii

DEN-2 Strain New Guinea C

DEN-3 Strain H87

DEN-4 Strain H241

A Novel Bio-Computational Model for Mining the Dengue Gene Sequences


There are three main types of dengue infection viz. Classic Dengue Fever (CD),

Dengue Hemorrhagic (DH) fever and Dengue Shock Syndrome (DSS). All the types

of dengue fever begin with noticeable symptoms within four to seven days after the

Aedes aegypti mosquito’s bite. The symptoms of CD include headache, pain behind

the eyes, joints and muscles, vomiting and body rash. It also reduces the count of

White Blood Cells (WBC). DH fever includes all the classic symptoms with higher

fever and sharp decrease in the number of platelets in the blood. Platelets are small,

disk shaped fragments that are the natural source of growth factors. They are

circulated in the blood and involved in the formation of blood clots. As a result of

this, victims bleed from the nose, gums and skin. DSS is the most severe form of the

disease that causes massive bleeding and fall in the blood pressure [14]. Each virus

type has its own characteristics.

In Section 2, the work related to bio-computational tools is outlined. Section 3

demonstrates the methodologies related to the proposed sequence miner tool. Section

4 exhibits the experimental results that were obtained using dengue virus serotype

dataset. Finally, Section 5 describes conclusion.

2. RELATED WORK

Basic biological research includes a wide range of studies focused on learning how

the dengue virus is transmitted, how it infects cells and causes disease. Further many

research works investigate several aspects of dengue viral biology that includes

exploration of the interactions between the virus and humans as well as the repetition

of dengue virus serotypes. Researchers have also been studying the dengue viruses to

understand the factors that are responsible for transmitting the virus to humans. They

found that specific viral sequences are associated with other severe dengue symptoms

[5]. Therefore, the literature on dengue fever viewed as three aspects viz. biological,

computational and bio-computational. For this paper, the related work focused on bio-

computational aspect.

There are several computational biology tools that have been developed over the

last two decades. These tools are selected to cover the range of different

functionalities and features for data analysis and visualization [2]. Some of the tools

are reviewed here.

Medusa [8] is a Java application oriented and is available as an applet. It is an

open source product under the General Public License (GPL). The visualization is

based on the Fruchterman-Reingold [6] algorithm and it provides two dimensional

representations of networks of medium size for up to a few hundred nodes and edges.

It is less suited for the visualization of big datasets. Medusa uses non-directed, multi-

edge connections, which allow the simultaneous representation of more than one

connection between two bio-entities. Additional nodes can be fixed in order to

facilitate pattern recognition and the spring embedded layout algorithms help the

relaxation of the network. It supports weighted graphs and represents the significance

and importance of a connection by varying line thickness. The compatibility of

Medusa has its own text file format that is not compatible with other visualization

tools or integrated with other data sources. The input file format allows the user to

annotate each node or connection. It allows the selection and analysis of subsets of

nodes. A text search, which supports regular expressions, can be applied to find

nodes. The status of a network can be saved and reloaded at any time when Medusa is

not currently connected to any data source.



Cytoscape [13] is a standalone Java application. It is an open source project under

LesserGPL (LGPL) license. It mainly provides two dimensional representations and is

suitable for large-scale network analysis with hundredth thousands of nodes and

edges. It can support directed, undirected and weighted graphs and comes with

powerful visual styles that allow the user to change the properties of nodes or edges.

The tool provides a variety of layout algorithms including cyclic and spring-

embedded layouts. Furthermore, expression data can be mapped as node color, label,

border thickness, or border color. Cytoscape comes with various data parsers or filters

that make it compatible with other tools. The file formats that are supported to save or

load the graphs are Setup InFormation (SIF), Geography Markup Language (GML),

eXtensible Graph Markup and Modeling Language (XGMML) and Biology

PAthaway eXchange (BioPAX). It also allows the user to import messengerRNA

(mRNA) expression profiles and gene functional annotations from the Gene Ontology

(GO). Users can also directly import GO Terms and annotations from gene

association files. It is highly interactive and the user can zoom in or out and browse

the network. The status of the network as well as the edge or node properties can be

saved and reloaded. In addition, Cytoscape comes with a network manager to easily

organize multiple networks. The user can have many different panels that hold the

status of the network at different time points which makes it an efficient tool to

compare networks between each other. It also comes with efficient network filtering

capabilities. Users can select subsets of nodes and/or interactions and search for active

sub networks or pathway modules. It incorporates statistical analysis of the network

and makes it easy to cluster or detect highly interconnected regions. The main purpose

of this tool is the visualization of molecular interaction networks and their integration

with gene expression profiles and other data. It also allows the user to manipulate and

compare multiple networks. Many plug-ins created by users are available and allow

more specialized analysis of networks and molecular profiles.

Osprey [3] is a standalone application running under a wide range of platforms. It

can be licensed for non-commercial use and the source code is currently not available.

Osprey provides two dimensional representations of directed, undirected and

weighted networks. It is not efficient for large scale network analysis, various layout

options and ways to arrange nodes in various geometric distributions. The layouts

range from the relax algorithm over a simple circular layout to a more advanced dual

spiked ring layout that displays up to 1500 – 2000 nodes in a easily manageable

format. The user can change the size and the colors of most Osprey objects such as

edges, nodes, labels, and arrow heads. Data can be loaded into the tool either using

different text formats or by connecting directly to several databases, such as the

General Repository of Interaction Datasets (GRID) or BioGRID [15] database. In

addition to its own Osprey file format, the tool can also load custom gene network and

gene list formats, making Osprey compatible with other tools relying on the same file

formats. Osprey networks can be saved in Scalable Vector Graphics (SVG), Portable

Network Graphics (PNG) and Joint Photographic Experts Group (JPEG) format. The

tool provides several features for functional assessment and comparative analysis of

different networks together with network and connectivity filters and dataset

superimposing. Osprey also has the ability to cluster genes by GO Processes. Network

filters can extract biological information that is supplied to Osprey either by the user

or by instructions inside the GRID dataset. Connectivity filters identify nodes based

on their connectivity levels. Finally, Osprey includes basic functions such as selecting

and moving individual nodes or groups of nodes or removing nodes and edges. With

its various filtering capabilities, Osprey is a powerful tool for network manipulation.



The ability to incorporate new interactions into an already existing network might be

considered the tool's biggest asset.

ProViz [10] is a standalone open source application under the GPL license. It

comes with both two dimensional and pseudo- three dimensional display support to

render data. It can manipulate single graphs in large-scale dataset with millions of

nodes or connections. It generates appealing 3-Dimensional (3D) visualizations. In

addition, the tool also offers a circular and a hierarchical layout, which improve the

detection of metabolic pathways or gene regulation networks in large datasets. ProViz

is ideal to gain a first overview of networks because it allows fast navigation through

graphs. Graphs are saved and loaded in Tulip format which is a drawing package.

Networks can also be exported in PNG format. Subgraphs are produced by selection,

filtering or clustering methods and can be automatically organized into views. With

ProViz it is possible to annotate each node and each edge with comments or merge

different datasets into a single graph. Users can also enrich the networks by querying

available online databases. ProViz uses a controlled vocabulary on bio-molecules and

interactions, described in eXtensible Markup Language (XML) format. It has its

strength in the area of protein – protein interaction networks and their analysis using

arbitrary properties and taxonomic identifier. Its plug-in architecture allows a

diversification of function according to the user's needs.

Ondex [7] is a standalone freely available open source application. It provides two

dimensional representations of directed, undirected and weighted networks. It can

handle large scale networks of hundred thousands of nodes and edges. It also supports

bidirectional connections, which are represented as curves. Moreover, different types

of data are separated by placing them in different disk-circles interconnected between

each other. Data may be imported through a number of 'parsers' for public-domain

and other databases, such as TRANScription FACtor (TRANSFAC), TRANScription

PATH (TRANSPATH), CHEmical Entities of Biological Interest (CHEBI), GO, Kyto

Encyclopedia of Genes and Genomes (KEGG), Drastic, Enzyme Nomenclature,

Expert Protein Analysis System (ExPASy), Pathway Tools, Pathway Genome

DataBases (PGDBs), Plant Ontology and Medical Subject Headings Vocabulary

(MeSH). Graph objects can be exported to Cell Illustrator and XML formats. To

reload or feed into other applications graph objects may be saved as ONDEX XML or

an XGMML form. Ondex integrates various filters that selectively add or remove

connected nodes from the display according to user selectable rules of connectivity

type like distance, level or equivalence [12].

Pathway Analysis Tools for Integration and Knowledge Acquisition (PATIKA)

[4] is a web based non-open source application publicly available for non-commercial

use. It has its own license. It provides 2D representations of single or directed graphs.

There are no limitations regarding the size of the graphs. It offers a very intuitive and

widely accepted representation for cellular processes using directed graphs where

nodes correspond to molecules and edges correspond to interactions between them.

Even though the implemented variety of layout algorithms is rather limited, PATIKA

is able to support bipartite graph of states and transitions. It represents different types

of edges: product edges, where the source and target nodes of a product edge define

the transition and a product of this transition, activator edges, where the source and

target nodes of an activator edge define the activating state and the transition that is

activated by this state, inhibitor edges where the source and target nodes of an

activator edge define the inhibiting state and the transition that is inhibited by this

state and substrate edges where the target and source nodes of a substrate edge define



the transition and a substrate of this transition respectively. It integrates data from

several sources, including Entrez Gene, Universal Protein Resource (UniProt), GO,

Human Protein Reference Database (HPRD) and Reactome pathway databases. Users

can query and access data using PATIKA's web query interface, and save their results

in XML format or export them as common picture formats. BioPAX and Systems

Biology Markup Language (SBML) exporters can be used as part of Patikas Web

service. The user can connect to the server and query the database to construct the

desired pathway. Pathways are created on the fly, and drawn automatically. The user

can manipulate a pathway through operations such as add new state or remove an

existing transition, edit its contents such as the description of a state or transition or

change the graphical view of a pathway component. PATIKA is a tool for data

integration and pathway analysis. It is an integrated software environment designed to

provide researchers a complete solution for modeling and analyzing cellular

processes. It is one of the few tools that allow visualizing transitions efficiently.

Though, there are various tools available to perform the sequence analysis, the

works related to find all the periodicities are very limited. Further the existing works

concentrate mainly on sequence alignment. Therefore there is a need for holistic

approach that computes all kinds of periodicities and their associations. In the current

work, we propose a tool called ‘Sequence Miner’ to classify the given sequence and

visualize the structure of the protein.

3. METHODOLOGY

The following steps are involved in the development of the bio-computational model

named ‘Sequence Miner’ for classifying the evolution of dengue virus serotypes. The

work flow of the proposed work is illustrated in Figure.1.

Figure 1 Work flow of the Sequence Miner

3.1. Data Collection

The primary step is to acquire the knowledge about dengue virus such as its serotypes,

replication cycle, symptoms caused and diagnosis methods available to detect dengue

and also identify the toxic protein in dengue virus. Each virus has toxic proteins that

cause diseases in human. Dengue virus has toxic proteins E and M. Then the online

composite database National Center for Biotechnology Information (NCBI) is used to

collect the gene sequences of all four dengue virus serotypes.

3.2. Data Preprocessing

The next step is to preprocess the collected data using sequence alignment algorithms

such as local alignment and global alignment [5]. The length variation and

inconsistent of the sequence are eliminated through the preprocessing. After

preprocessing, the aligned sequences are transferred to the next process.



3.3. Periodic Association Rules

A further step in this direction is the prediction of co-occurrence patterns among the

dengue gene sequences. This can be done by evaluating the rules that can reveal the

occurrence of an element or subsequence. Such rules are called Periodic Association

Rules, and the corresponding technique is called Periodic Association Rule Mining

(PARM). The PARM is similar to market basket analysis. In PARM terminology, the

nucleic or amino acids may be considered as items and the gene subsequences as the

baskets that contain the items. In the traditional association rules, only the number of

frequent items is calculated whereas PARM calculates the occurrence order of

frequent item sets along with its periodic position.

To obtain periodic association rule, the frequencies of nucleic or amino-acids are

computed in each dengue gene sequence. The rule can be expressed as A! C, where

A and C are the associated items. The rules state that if a nucleic acid A is present in a

given sequence with f1 periodicity then there will be another nucleic acid C that will

have similar periodicity with respect to their respective initial positions. The PARM

procedure enables to find the periodicity f1 along with their starting positions.

Let I = {i1, ...., ik} be a set of k elements, called items. Let Is = {b1, ...., bn} be a set

of n subsets of I. We call each bi as a set of transaction. In the market basket

application [6], the set I denotes the items stocked by a retail outlet and each basket bi

is the set of items of a transaction. Similarly, in case of gene sequence the set I denote

the elements of nucleic or amino acid and the basket bi is the orderly subsequences.

The order and frequency of the elements can be evaluated using the suffix tree. The

PAR is intended to capture the orderly dependence among the elements of dengue

virus dataset and the rule can be represented as i1 ! i2 along with the period and

starting position of i1 and i2, provided the following conditions hold good:

1. i1 and i2 occur at regular intervals in the sequence for at least s% of the n baskets

where s is the support and n is the number of subsequences.

2. For all the subsequences containing i1, at least c% of subsequences contains i2 where

c is the confidence.

The above definition can be extended to form multidimensional periodic

association rule such as AC ! GT, where AC and GT are element of nucleic acid

with periodic dependence. The association rules are considered interesting if they

satisfy both minimum support and confidence thresholds. The threshold values are set

by users based on their domain expertise.

To evaluate the PAR we propose the RECurrence FINder (RECFIN) algorithm.

The following steps are involved in the RECFIN algorithm:

1. Based on the occurrence positions the elements are mapped into integers.

2. Based on the support threshold the element periodicity is found. The set of elements

that satisfies the minimum support threshold is called the frequent item set.

3. The frequent item sets are used to generate association rules. For example, consider

the item set {A, C, G}. The following rules can be evaluated using the given

item set:

Rule 1: A ^ C ! G

Rule 2: C ^ G ! A

Rule 3: A ^ G ! C



Rule 4: G ^ A ! C

Rule 5: C ^ A ! G

Rule 6: G ^ C ! A

In the above rules the element that appears in left hand side is called antecedent and

that of the right hand side is called consequent. The confidence is computed using the

conditional probability of antecedent. For example, the confidence of the rule 1 is

computed as follows:

Confidence = support {A, C, G}/support{A,C}]

If the confidence is equal to or greater than a given confidence threshold, the rule is

considered as interesting rule.

4. Based on the support and confidence the PAR is generated.

3. 4. Amino Acid Component based Classification (AACC)

The AACC algorithm is based on the ID3 classifier. Based on PAR the given

sequence may be classified into six components such as Sulfur, Aromatic, Alphatic,

Acidic, Basic and Neutral. The classifier model has two phases viz. i) model

construction ii) model usage as illustrated in Figure.2. (a) and (b)

Neutral component produces the Asparagine, Serine, Threonine and Glutamine.

Sulfur component produces the Cytosine and Methoine. Alphatic component produces

the Leucine, Isoleucine, Glycine, Valine and Alanine. The Basic component produces

the amino acids Arginine and Lysine. Acidic component produces Glutamic and

Aspartic acids. Aromatic component produces the Phenylalanine, Tryptophan and

Tyrosine.

Figure 2(a) Model Construction



Figure 2(b) Model Usage

The classification model was trained by 10,735 sequences and the testing phase

was conducted by 10,198 sequences through this model.

Apart from these, a new dengue virus serotype was recently found by the

scientists that also creates the DH fever. DEN5 is the Non-Structural (NS) Protein

which indicates the new type of serotypes emerged from the existing serotypes.

Therefore, the proposed bio-computational model aims to predict the future evolution

of dengue virus serotype by analyzing the existing sequence of dengue virus

serotypes. Also this model is helpful to analyze the different kinds of gene sequences

like Nucleic acid (DNA, RNA) and Amino acid (Protein) sequences and visualize

them.

In fact, dengue provides to the drug designer significant difficulties that may not

be found for other virus infections like malaria, yellow fever, bird flu fever, etc. The

proposed tool aims to detect all other infections which are caused by various virus

serotypes.

3.5. Visualization / Graphical Representation

Bio-computational tools are the software programs for analyzing the biological data

and extracting the patterns from them. In addition to that tools must be user friendly,

even beginners can also be benefited by using them. The tool “Sequence Miner” will

be suitable for both the experts and the beginners to get knowledge about different

organisms via sequence analysis.

All the processes are graphically visualized in the sequence miner tool. It has the

interactive feature to classify the given sequence along with its structure. The

following are the effective features of the proposed tool: i) data collection through the

online ii) sequence alignment iii) generation of periodic association rules iv)

classification based on the amino acids and v) visualize the structure of the protein.

The layout of the sequence miner tool is illustrated in Figure.3.

The input sequences are collected from various online data repositories such as

NCBI, GenBank and related web sites as illustrated in Figure.4. The file format of the

input sequence may be text file or access number of the whole sequence.



Figure 3 Sequence Miner - Layout

Figure 4 Online Database

3.5.1. Sequence Comparison

The compare menu performs two tasks on dengue Serotypes: (i) Hit Rate (ii) Longest

Common Subsequence (LCS). Hit Rate compares two DNA or Protein sequences and

displays the number of matches between the two sequences and its matching

percentage as shown in Figure.5. LCS compares two DNA or Protein sequences then

predicts and displays the all common subsequence and longest common subsequences

using edit distance method. It also displays the execution time of the algorithm to

produce the result as shown in Figure.6. The comparison ratio also differs from

DEN1, DEN2, DEN3 and DEN4. However, DEN3 and DEN4 shows the similar hit

rate ranging from 70% to 86 %.

3.5.2. Sequence Alignment

The aim of the sequence alignment is to match the most similar elements of two

sequences. In comparing sequences, one should account for the influence of



molecular evolution. The probability of acceptably replacing an amino acid with a

similar amino acid is greater than replacement by a very different one. Substitution

matrices evaluate potential replacements for protein and nucleic acid sequences.

Figure 5 Comparison of Two Sequences

Figure 6 Finding the LCS in the given sequences

An optimal pairwise alignment is an alignment which has the maximum amount

of similarity with the minimum number of residue 'substitutions'. There are two types

of substitution matrix available:

1. Point Accepted Mutation (PAM)

2. BLOck SUbstitution Matrix (BLOSUM)

PAM is constructed by examining the kind of mutation that occurs in closely

related protein sequences i.e. mutation of one residue accepted by evolution.

BLOSUM is derived based on direct observation for every possible amino acid

substitution in multiple sequence alignment and it depends only on the identity of

protein sequences. BLOSUM matrices effectively represent more distant sequence

relationships, and BLOSUM62 has become a standard matrix. So in the pairwise

alignment module, BLOSUM 62 is used to find out both local and



global alignments between sequences.

Figure 7 Needleman – Wunsch Global Alignment Algorithm

The pairwise alignment module performs two tasks on dengue serotypes: (i)

Needleman- Wunsch (NW) Align with Blosum62 (ii) Smith- Waterman (SW) Align

with Blosum62. NW Align with Blosum62 performs global alignment. Global

alignment optimizes the alignment over the full-length of the sequences to find out the

similarity between two closely related sequences. In order to carry out the global

alignments on DNA or Protein sequences, Sequence Miner implemented the dynamic

algorithms NW algorithm and uses the Blosum62 substitution matrix as shown in

Figure.7. SW algorithm with Blosum62 performs local alignment. Local alignment is

for determining similar regions between two distinctly related DNA and Protein

sequences. In order to achieve the local alignments on DNA or Protein sequences,

Sequence Miner implemented the dynamic SW algorithm and uses the Blosum62

substitution matrix. It also displays the execution time of the algorithm for the given

input as shown in Figure.8.

Figure 8 Smith-Waterman Local Alignment Algorithm

3.5.3. Pattern Matching

Pattern matching is an important task in bioinformatics algorithms that try to find a

place where one or several patterns are found within a larger sequence or text.



Sequence Miner implements the Boyer Moore and Suffix tree algorithm in order to

highlight the user specified protein or nucleotide pattern in the inputted dengue

sequence. It also displays the periodical patterns along with periodic association rules

and execution time of the algorithm needed to find and highlight the searching

patterns as shown in Figure.9. The periodic association rules are mined with latent

periodicity that also exhibits the evolutionary relationship among DEN3 and DEN4.

3.5.4. Periodic Association Rules

The periodic patterns are extracted from the aligned sequences using novel RECFIN

algorithm. The RECFIN algorithm finds element, subsequence and latent periodicities

using suffix tree. The sample input sequence is given as in Figure.10. The suffix tree

finds the subsequence periodicities present in the given sequence. The sample result

displayed in Figure.11. The periodic patterns including latent periodicities are

identified along with their position as shown in Figure.12. The resultant PAR mined

with the help of minimum support and confident thresholds as shown in Figure.13.

Figure 9 Pattern Matching Algorithms

Figure 10 Sample Input Sequence



Figure 11 Suffix Tree for the partial subsequence

Figure 12 Periodic patterns along with position

Figure 13 Mining Periodic Association Rules



3.5.5. Amino Acid Component based Classification

This classification works based on the novel AACC algorithm. The mined amino

acids are classified based on the amino acid components and the classification results

illustrated in Figure.13.

3.5.6. Visualization

The visualization module exhibits the 3-Dimensional structure of the proteins as

shown in Figure.15. The variation of the protein structure of all dengue serotypes are

visualized in this module. Some additional requirements are needed for the

visualization such as Rasmol (Graphics Visualization), Swiss Protein DataBase

Viewer (SPDBV).

Figure 14 Classification Results

4. EXPERIMENTAL RESULTS

The total number of dengue gene sequences available in the NCBI is 21, 026. From

this huge number of sequences 10, 735 sequences have been taken for the training set.

The proposed model classifies 10, 198 correctly. The accuracy of classification result

is 96.74%. The resultant sequence can be calculated by subtracting the non classified

sequence from the total sequences.

Result = 10,735 – 537 = 10, 198

The same set of data given into other bioinformatics tools. The comparison of the

classification results is illustrated in Figure 16.

The features of the existing tools are compared with our proposed sequence miner

tool. Table.1. shows the salient features of the proposed tool that are compared with

other existing tools.

Figure 15 Visualization of the Protein Structure



Figure 16 Accuracy of the Classification Results

Table.1. Comparative Analysis

Name of the

Tool

Input Type Compatibility Visualization Functionality

Medusa text file Not compatible

with other

visualization

tools

2- Dimensional

Representation

Text search

through regular

expression

Cytoscape text file Load graphs 2- Dimensional

Representation

Zoom in and

Zoom out

Osprey text file Different text

formats, grid

2- Dimensional

Representation

Gene Ontology

ProViz text file, Image Graphs 2- Dimensional

Representation

Sub graphs

Ondex text file Data supports

with other

formats

2- Dimensional

Representation

Filter

PATIKA XML Format Data supports

with other

formats

2- Dimensional

Representation

Data integration

Sequence

Miner

Text file,

Sequence data,

XML

Compatible with

other file formats

2- Dimensional

and 3-

Dimensional

Representation

Classification,

Protein structure

prediction, Gene

Ontology

5. CONCLUSION

The proposed tool “Sequence Miner” is a novel approach designed to perform

sequence analysis through the traditional methods such as LCS, Pairwise alignment

(Local, Global and Multiple), Pattern matching algorithms, and Phylogenetic tree

construction. The classification results of this work clearly exhibit the evolutionary

relationship of dengue virus serotypes from the existing serotypes. Therefore, there is

the chance to the presence of DEN3 or DEN4 in the recently discovered DEN5

serotype. There is no evidence to find the structure of DEN5, however the E and M

proteins of DEN5 may be associated with the existing serotypes. The proposed bio-

computational model will be helpful to make the confirmation of the toxic proteins

presence in the recently discovered virus serotype. On the whole, the relationship

between dengue serotypes predicted via the proposed tool will definitely help the



biotechnologists and drug designers to move one step forward in discovering an

effective vaccine for dengue.

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