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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 175-185 © IAEME
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COMPRESSIVE ASSESSMENT OF BIOINFORMATICS IN BIOMEDICAL
IMAGING AND IMAGE PROCESSING
Shashikant S. Patil Sachin A Sonawane
Associate Professor Assistant Professor
SVKM’s NMIMS SVKM’s NMIMS
Nischay Upadhyay Aanchal Srivastava
SVKM’s NMIMS SVKM’s NMIMS
ABSTRACT
Bioinformatics is basically the method of storing, organizing, retrieving and analyzing the
biological data. The protein sequences of the organisms are stored in databases and can be retrieved
as per requirement. This paper reviews the bioinformatics and its importance in detail with the
description of various analyzing methods. Whenever a new virus is found, its genetic material is
compared with the existing genetic information. Based on the comparison of results, the protein
sequence that is required for curing can be identified. Based on this observation the appropriate
medicine is prescribed. The data stored in the databases is updated as per the discovery of new
protein sequences. Hence, it is essential to keep the information in the database in the most organized
and optimized form for its quick retrieval.
Keywords: Bioinformatics, Biomedical Imaging, Compressive assessment, Genomics analysis,
Protein sequence.
1. INTRODUCTION
Biological data are being produced at a phenomenal rate. For instance till the year 2000 there
were 88,166 sequences of protein database in SWISS-PROT and 8,214,000 sequences of neuclic acid
contained in the GenBank. Typically observing these scenario the databases are expanding by twice
every 15 months []. Moreover, complete sequences for database for more than 4 organisms have
been released that ranges from 50 genes to as much as 100,000. Appending to the data from the
uncountable number of related projects that is based on gene expressions tells us the protein structure
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ISSN 0976 – 6367(Print)
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Volume 5, Issue 4, April (2014), pp. 175-185
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encoded by the genes and also gives information about how these product will interact with each
other. This indeed gives our imagination a wider sense about the variety and amount of information
that is being processed [15, 19]. This overflowing of data leads to the necessity of computers in the
biological research. Introducing the computer in the system is an ideal approach as computer has
capability of dealing with a large amount of data and solving the complex problems with an ease.
Bioinformatics, the subject of the current review, is often defined as an interdisciplinary field of
science that has develop various techniques to store, retrieve, organize and analyse the biological
data. Life itself revolves around information technology [2, 23]. An individual’s psychology is
largely correlated with its genes which at its most primitive form can be looked as a digitalized
information. Simultaneously with the emergence in the bioinformatics there has been advanced
enhancements in the technologies that supply the initial raw data. Recently in the studies of Anthony
Kerlavage of Celera has stated that an experimental laboratory is able to produce data over 100
Gigabytes within a day easily [9, 14, 32]. To match the incredibly large data the computer employed
should be able to deal with the data with incredible processing powers. The more the data processing
is there more is the challenges in the computing field. The computer employed should be of high
efficiency, larger disk storage, should allow faster computation and should be optimized to its
highest possible limit.
2. AIMS OF BIOINFORMATICS
The bioinformatics has some simple aim that can be explained in three-folds. Firstly, the
bioinformatics should organize and optimize the data in such a way that it can be easily accessible by
the researchers and new entries in the database can be made easily [6, 7, 11]. While curing the data is
one of the important tasks, hence, the information that is stored in the databases are not fruitful until
and unless they are analysed. Therefore, the functionality of bioinformatics is further extended.
Secondly, the aim of bioinformatics is to develop techniques and resources that will help in the
analysing phase of data. For instance, if some sequence of protein is found, then it is of researcher’s
interest to compare it with the previously characterized sequences of protein. This search of the
previously matching sequence is not a mere search of data from a stack of data rather it requires
programs like FASTA and PSI-BLAST that does the biological significant matching of the
sequences. The development in the resources is therefore demanding the expertise in the
computational field and thorough understanding of the biology and its related terms. Thirdly, the aim
of bioinformatics is to use the provided tools to analyse the given data and interpret the outcomes in
a biologically understandable manner [23]. Customarily the biological studies goes on by examining
the subject in detail and comparing the gathered data with the related previously characterized data.
In bioinformatics now we can compare the database globally which has led to uncovering general
principles that may apply around various present system and helps to highlight the novel features
present. The biological data gathered in the examining process goes through many processing stages
which includes areas such as computer science, mathematical analysis, statistics and engineering.
3. HISTORY
On the basis of importance of data transmission, accumulation and processing in biological
systems, PaulienHogeweg, discovered the term “Bioinformatics” in 1978. The term implies to the
study of data processes in biotic systems [3]. Because of its definition this field is similar to
biochemistry or biophysics. The data accumulation and maintenance in models of prebiotic evolution
and social interaction structures by simple behavioural rules are the relevant examples of biological
information processes which were studied earlier under bioinformatics [5].

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Elvin A. Kabat, contributed to bioinformatics with his comprehensive volumes of antibody
sequences which was released with Tai Te Wu between 1980 and 1991. Margaret Oakley Dayhoff is
another significant pioneer in this field who has been hailed by David Lipman, director of the
National Centre for Biotechnology Information, as the "mother and father of bioinformatics [16, 20,
27].
The term bioinformatics was re-invented at the beginning of the "genomic revolution", to
refer to the creation and maintenance of a database to store biological data such as amino acid
sequences and nucleotide sequences [8, 10]. This type of database included not only design issues
but also the development of complex interfaces so that the researchers could easily access the
existing data and also submit the revised or new data.
4. GOALS
The biological data are combined together to study the alterations of cellular activities in
various disease states [14, 25]. The field of bioinformatics includes protein domains, amino acid and
nucleotide sequences and protein structures which emerged this field in such a way that now it
includes the interpretation and analysis of several types of data. The actual term for analyzing and
interpreting data is known as computational biology [31, 33]. The vital sub-disciplines within
computational biology and bioinformatics include:
• The implementation and the development of tools that enables effective access for use and
management of several kind of data.
• The improvement in statistics and new mathematical formula to access the interaction among
members of large data sets. For instance, the protein structure and/or function and cluster
protein sequences are predicted into the families of related sequences in order to locate a gene
within a sequence.
The initial goal of bioinformatics is to escalate the understanding of biological processes. Its
focus on improving and applying computationally intensive methods to achieve this goal, makes this
field different from other approaches. Samples include: data mining, machine learning algorithms,
pattern recognition and visualization [21]. Key research efforts in the field include gene finding, drug
design, sequence alignment, drug discovery, genome assembly, protein structure prediction, protein
structure alignment, genome-wide association studies, prediction of gene expression and protein-
protein interactions, and the modeling of evolution [18].
In order to solve practical and formal problems that rise from the analysis and the
management of biological information, bioinformatics [29] requires the creation and development of
algorithms, databases, statistical and computational methods.
The combination of continuous development in information technologies and genomic and
other molecular research [14] technologies has given rise to a large amount of data related to
molecular biology. Thus, the name given to these computing and mathematical approaches is
“bioinformatics” for the understanding of biological processes.
5. APPROACHES
Mapping and analysing DNA and several protein sequences and aligning them with different
DNA [13] and protein sequences in order to compare them and then creating the 3-D models of these
sequences and viewing it, these are the common activates in bioinformatics.

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The two ways of modelling a biological system for instance, living cell are as follows:
1) Static : sequences- Peptides and Nucleic acids and Proteins Relationship among the above
entities including metabolites, microarray information and Networks of proteins
2) Dynamic: Structures- Nucleic acids, Proteins, Peptides and Ligands System biology, under
this category, includes variable concentrations of metabolites and reaction fluxes
6. MAJOR RESEARCH AREAS
The major research areas of bioinformatics is bioinformatics and computational biology,
genetics and genomics and lastly system biology. Bioinformatics has emerged as an essential part of
numerous areas of biology [33]. In starting level of experimental biology many image processing
techniques and signal processing techniques are combined and used to encapsulate the important
outcomes from a huge quantity of raw data. Bioinformatics has been working in the field of genetics
and genomes in which it helps in sequencing and interpreting genomes[14] and simultaneously
observes their transformations. It plays an essential role in making a note of the recorded data and
arranging it sequentially in an organized manner in the databases. It has a role in the analyzing stages
of genes and protein sequences. In the area of system biology it aids in the modelling and simulation
of several microscopic level things like the RNA, DNA[23,25] and the protein structures and also
their interaction at molecular level of studies.
7. SEQUENCE ANALYSIS
Sequence analysis is basically a process in which the protein sequence, DNA or RNA are
subjected to the broader range of methods that analytically analyzes the subjects in order to
understand its characteristics in detail. When we talk about a characteristics of the peptide sequences
we talk about its features, its functions, its structure and its evolution. The analytical methods used
consist of the alignment of the sequences[12,32] and comparison of the sequence with the previous
biological databases. The sequence analysis of the functions include a wide range of similar topics:
• The comparison of the sequences with the biological databases to find the similarities
between them and also infer whether they are homologous [14, 22].
• Intrinsic features of the sequence is identified using the sequence analysis.
• Genetic marking based upon the differences and variation from the biological database
sequences is used to identify the signals.
• Finally after comparing the new sequence with the old database we reveal the genetic
diversity and evolution of the new sequence or organism.
• Sequence alone is sufficient to identify the molecular structure of the organism.
Intrinsic features mentioned in one of the points above refer to the features like post
translation modification sites, active sites, gene structures, reading frames, distribution of exons,
introns [5, 9] and other regulatory elements.
Sequence analysis is followed in a particular fashion. The main contents in the analysis are
the sequence alignment which is followed by profile comparison. The profile comparison is done by
aligning the new sequence with the old database. After the profile comparison the next stage is of the
sequence assemble and gene prediction after which the protein structure prediction takes place.

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8. GENOME ANNOTATION
Genome can be defined as a complete set of DNA within a single cell of organism. The very
first software system that can explain the genome was designed by Owen White in 1995. He with his
team analyzed and sequenced the very first genome of the bacterium haemophilus influenza. Genes
are defined as the fragments of genomic sequence [6, 8] that encode the protein within. Owen White
then build a software that was capable of finding the genes, transferring RNA’s and moreover can
assign initial functions to the genes. Since the development in the computer field is tremendous
hence the programs now available are more advanced, changing and improved in its own ways.
9. COMPUTATIONAL EVOLUTIONARY BIOLOGY
The study of the origin of a species, its descent as well as the change over time is known as
Evolution biology [3]. In simple words the meaning of evolution biology can be judged from its
name itself i.e. it is the study of an evolution of species. The invention of bioinformatics in
evolutionary biology has enabled the researchers to trace the descent of an uncountable number of
species. Also bioinformatics has aided in the study of the evolution of more complex species that
include the cases of gene[2,10] duplication, gene transfer horizontally, etc. It has also helped in
estimating the result of any population over time. Bioinformatics has main usage in tracking and
sharing the information of a huge number species and organism.
10. LITERATURE ANALYSIS
As the number of researcher are growing, in the same manner the number of papers published
are increasing at a tremendous rate [1, 5]. Hence to get into the expanding library of the textual
resources, literature analysis is required. Literature analysis targets to make use of various
computational and statistical linguistics for this analysis. It uses abbreviation recognition, named
entity recognition and protein- protein interaction for the same. In abbreviation recognition it finds
out the full form of the abbreviated biological [18, 20] terms. In named entity recognition it
recognizes the other biological terms like the names of the genes etc. whereas in the protein-protein
interaction it identifies that which protein is interacting with which protein from the given text.
11. ANALYSIS OF GENE EXPRESSION
To analyze the gene expression we have to measure its mRNA level. There are various
techniques to measure the mRNA level, some of which are microarrays, serial analysis of gene
expression, expressed cDNA sequence[17,32] tag (EST) sequencing, massively parallel signature
sequencing (MPSS), RNA-Seq. that is also called as "Whole Transcriptome Shotgun Sequencing"
(WTSS). The techniques mentioned above are all noise prone. Most of the efficiency and time in the
computational biology is absorbed in developing various statistical tools that are capable of
separating the noise from signal.
12. ANALYSIS OF REGULATION
Before analysis of regulation we should first understand what regulation is all about.
Regulation can be defined as a complex arrangement of the events[12,21] initiating with a hormone
or any extracellular signal that eventually leads to the increment or the decrement in one or more
activity of any number of proteins. For the in-depth study of the process bioinformatics is required.
To infer about a gene regulation expression data can be used [14]. If the analysis is restricted to a

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single celled species then the various stages of the cell cycle can be compared under different
conditions of stress i.e. under heat, shock, etc. After getting the expression data we can apply the
clustering algorithm in order to tell that which of the genes are co-expressed. Clustering algorithm
used for gene clustering are like k-means clustering, SOMs (self-organizing maps), consensus
clustering and hierarchical clustering.
13. ANALYSIS OF PROTEIN EXPRESSION
The proteins present in the biological sample can be judged by providing a snapshot using the
protein microarrays and high throughout (HT) mass spectrometry (MS). These snapshots can be
turned into sensible data using bioinformatics [9, 12]. It faced a problem of matching large quantity
of data against the previously available protein database and also there were multiple complicated
statistical analysis of samples whereas it detected the incomplete peptides from each protein.
14. COMPARATIVE GENOMICS
Comparative genomics is a biological research field that is used for the comparison of the
genomic features of different organisms. The features that are compared between organisms are
generally the DNA sequences, the genes, the regulatory sequences, the gene order and various other
genomic structural landmarks[41]. The comparative genomics has the ability to find out the
similarities as well as differences in biological terms between different species. Evolutionary
relationships between the alike organisms can also be found out by using comparative genomics. The
principle of the comparative genomics is that each and every organism has its features encoded
within its DNA and these features are evolutionary conserved within them. Hence, the common
features of two organisms are also encoded within their DNAs [4, 6]. Therefore, comparative
genomics makes some sort of alignment of the genome sequences and then finds the common
sequence in the aligned sequences. Finally it verifies that to which extend are the sequences
conserved.
15. NETWORK AND SYSTEMS BIOLOGY
15.1. Network Biology
The relationships between biological networks such as the metabolic network or the protein-
protein network are understood by doing the network analysis. Network biology seldom contains
different data types like small molecules, proteins[31], data of gene expression and many other types
that may be connected physically or/and functionally. A biological network can be formed from a
single type of genes or molecules but still the above mentioned data types are always attempted to be
integrated [19].
15.2. Systems biology
In order to visualize and analyze the complex connections of the cellular processes the
computer simulation of cellular subsystems are used. Cellular subsystem [23] comprises a network of
enzymes and metabolites which includes metabolism, gene regulatory networks and signal
transduction pathways. With the aid of the simple (artificial) life forms computer stimulations, the
virtual evolution also known as artificial life tries to comprehend the evolutionary processes [9].

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16. HIGH-THROUGHPUT IMAGE ANALYSIS
The biomedical imagery has a lot of information stored into it. For complete analysis of these
images computer technologies are deployed which in-turn accelerate or sometimes fully automate the
processing, analysis[22] and quantification of the large amount of data stored in the biological
images. The analysis systems in the modern times has increased the ability of the observer to take
measurements from larger distances and has also improved the accuracy, speed or objectivity. Some
of the modern fully developed analysis systems has completely removed the observer. These
enhanced systems[29] are not only related to the field of biomedical imaging. Nowadays biomedical
imaging is becoming useful for both the research and diagnostics purposes.
17. SOFTWARE AND TOOLS
Several software tools used for bioinformatics are varied from simple command-line tools to
more complex graphical programs. Further the software and tools in bioinformatics are classified as
follows:
17.1. Open-source bioinformatics software
Since 1980s, there have been many free and open source software tools that existed and are
further continuing to grow. Regardless of the funding arrangements, the association of several open
code bases that are freely available, innovation in silicoexperiments [6, 27] and necessity for new
algorithms for the analysis of evolving types of biological readouts have helped all research groups
to contribute to both the variety of open source software available and bioinformatics. These open
source tools often serve as the brooding place for ideas, or community supported plug-ins in
commercial applications [25, 28].
The variety of open-source software packages involves titles such as BioPerl, BioJava,
Taverna workbench, Bioconductor, UGENE [31], Biopython, BioRuby, EMBOSS, .NET Bio and
Bioclipse. The annual Bioinformatics Open Source Conference (BOSC) has been supported by the
non-profit Open Bioinformatics Foundation since 2000, to maintain the tradition create further
opportunities.
17.2. Web services in bioinformatics
For a wide range of applications in bioinformatics, several interfaces based on SOAP and
REST [16, 17] have been developed which allows an application to run on one computer in one part
of the world so as to use information, algorithms and computing resources on servers in other parts
of the world. The main advantage of using web services in bioinformatics is that there is no need for
the end users to deal with database maintenance overheads and software.
EBI further sub-divides the basic bioinformatics services into three categories which are as follows:
1) BSA (Biological Sequence Analysis)
2) SSS (Sequence Search Services)
3) MSA (Multiple Sequence Alignment)
The applicability of web-based bioinformatics solutions is illustrated through the availability
of these service oriented bioinformatics resources that varies from an assembly of standalone tools
having a common information pattern under a single web-based interface[28], to distributed,
integrative and extensible bioinformatics workflow management systems.

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17.3. Bioinformatics workflow management systems
Bioinformatics workflow management system is defined as a specialized kind of a workflow
management system. The main aim of this system is to constitute and execute a series of
computational or information manipulation steps, or a workflow in a bioinformatics application. This
system helps scientists to create their own workflows[11, 19] for individual application by providing
them an easy to use environment. It also provides them various interactive tools that enable them to
execute their workflows and view the results in real time. This system simplifies the process of
reusing and sharing workflows between the scientists. Thus, it helps the scientists to track the
workflow creation steps and the provenance[17] of the workflow execution results. Galaxy, Kepler,
Anyaya and TavernaAnduril are four such platforms that provide the above mentioned service
currently.
18. PRACTICAL APPLICATIONS
18.1. Finding Homologues
The search for similarities between unlike biomolecules is one of the driving forces behind
bioinformatics. The identification of protein homologues has some direct practical uses in
bioinformatics, apart from the systematic organization [22, 24] of information. The key application
of this, is transferring of information between related proteins. For instance, it is possible to look for
homologues that are better understood and also applies some of the knowledge as compared to a
given poorly characterized protein. The theoretical models of proteins are usually built on
experimentally solved structures of close homologues specifically in the case of structural data. The
method used in fold recognition is same as the above mentioned technique in which tertiary structure
predictions rely on searching structures of remote homologues[26,30] and then further it is checked
that whether the prediction is energetically viable or not. Wherever there is lack in structural and
biochemical data, the studies could be made in low-level organisms such as yeast and the results of
these experiments could be applied to high level organisms like humans, where the experiments are
more demanding. A corresponding approach is also applied in genomics. Basically homologue
finding is used in newly sequenced genomes to confirm coding regions and the functional
information is recurrently transferred to annotate individual genes [31].
The problem of understanding complex genomes is simplified by first analysing simple
organisms and then applying the same techniques to high level complicated organisms. This is
applicable on large scale and is one of the reasons why the projects of early structural genomics
focussed on Mycoplasma genitalium[17]. Ironically, this identical idea can also be applied vice
versa. By checking the missing microbial proteins in humans, one can quickly discover the potential
drug targets. Drug molecules may be designed on a small scale by harnessing the structural
differences between alike proteins, that specifically bind to one structure but not another.
18.2. Rational Drug Design
Assisting rational drug design has been one of the earliest medical applications of
bioinformatics. Using the MLH1 gene[14] product is the commonly cited approach as an example of
drug target. MLH1 is a human gene which is used to encode a mismatch repair protein (mmr) that is
situated on the shortarm of chromosome [1, 5]. After going through the linkage analysis, it has been
observed that MLH1 gene is similar to the mmr genes in mice, therefore the gene has now been
implicated in nonpolyposis colorectal cancer. Translation software can be used to determine the
probable amino acid sequence of the encoded protein when the nucleotide sequence is given. Firstly
homologues are found in model organisms by using sequence search techniques and then on the
basis of the similarity of the sequence, the structure of the human protein is modeled based on the

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experimentally characterized structures [22, 23]. Finally, to bind the model structure, molecules are
designed using docking algorithms.
18.3. Large-scale censuses
The bioinformatics database is capable of storing all the information and sequences of the
protein, genomes and their structure and datasets expression[29] but on the other hand it is very
important to condense that information in the understandable form by the user.
In order to identify the subjects and areas of interest for further analysis[2,4] and detailed study the
Broad generalization helps in doing so and also it aids in placing the new observations at appropriate
place. This sorting assists the user to judge whether the content is unusual in any pf the possible
ways.
The surveys held at a particularly large scale can give rise to a number of questions related to
bioinformatics. Questions such as whether the protein fold are related to a particular groups of
phylogenetic? In a particular organism how the different folds are common? What percentages of the
folds are commonly shared between similar organisms?
We have also integrated data on to the protein functions. We know that certain protein folds
are responsible for the particular biochemical functions. The sharing of the folds by an organism
indeed follows the routine phylogenetic classification [6, 11]. A query also arises that to which
extent of sharing parallel the deprived relatedness are derived from the traditional evolutionary trees?
Expression data is the most electrifying and latest source of genomic information. The question after
the combination of the expression information with the functional and the structural classification of
the protein the high presence of the protein fold in a genome is the revealing of the higher levels of
expression. The subcellular[22] localization of the protein and the interactions between them is
included in the surveys at larger scale to get the genomic data. Along with the union of the structural
data we can start by executing the maps of all the protein-protein interaction that takes place within
an organism.
18.4. Further applications in medical sciences
The current application in the biomedical sciences has been wrapped around the gene
expression analysis. In the biomedical disease detection the compiled expression data of the affected
cell due to any disease like cancer, etc. is compared with the normal expression levels of the
unaffected cells. The gene expressed by the affected cells provides us with the cause of the disease
and therefore indicates the various possible treatments for the same using specialized
drugs[23].Given a lead compound, microarray trials can then be helpful in evaluating the responses
to the pharmacological intervention, moreover it aids in predicting the toxicity [26, 29] of the applied
drugs and is used to facilitate with the early test results.
The combination of experimental genomics with the advancement in bioinformatics is
predicted to bring about a revolution in the future of healthcare for the individuals. For a patient a
distinctive scenario may arise with post natal genotyping in order to assess immunity from specific
pathogens and diseases. Therefore, keeping in mind this information, in order to reduce the costs
related to healthcare, a distinct combination of vaccines could be prescribed saving the unnecessary
treatments and expecting the onslaught of diseases later in life.
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