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The
pace, by which scientific knowledge is being produced and shared today, was
never been so fast in the past. Different areas of science are getting closer
to each other to give rise new disciplines. Bioinformatics is one of such newly
emerging fields, which makes use of computer, mathematics and statistics in
molecular biology to archive, retrieve, and analyse biological data. Although
yet at infancy, it has become one of the fastest growing fields, and quickly
established itself as an integral component of any biological research
activity. It is getting popular due to its ability to analyze huge amount of
biological data quickly and cost-effectively. Bioinformatics can assist a
biologist to extract valuable information from biological data providing
various web- and/or computer-based tools, the majority of which are freely
available. The present review gives a comprehensive summary of some of these
tools available to a life scientist to analyse biological data. Exclusively
this review will focus on those areas of biological research, which can be
greatly assisted by such tools like analyzing a DNA and protein sequence to
identify various features, prediction of 3D structure of protein molecules, to
study molecular interactions, and to perform simulations to mimic a biological
phenomenon to extract useful information from the biological data. The functioning
and specificity of the tools like ENTREZ, iTasser, GENSCAN, ORF finder;
Modeller is discussed in the following review

Introduction

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Bioinformatics is an
interdisciplinary science, emerged by the combination of various other
disciplines like biology, mathematics, computer science, and statistics, to
develop methods for storage, retrieval and analyses of biological data.Paulien
Hogeweg, a Dutch system-biologist, was the firstperson who used the term
“Bioinformatics” in 1970, referring to the use of information technology for studying
biological systems. The launch of userfriendly interactive automated modeling
along with the creation of SWISS-MODEL server around 18 years ago resulted in
massive growth of this discipline. Since then, it has become an essential part
of biological sciences to process biological data at a much faster rate with
the databases and informatics working at the backend.

Computational tools are routinely
used for characterization of genes, determining structural and physiochemical
properties of proteins, phylogenetic analyses, and performing simulations to
study how biomolecule interact in a living cell. Although these tools cannot
generate information as reliable as experimentation, which is expensive, time
consuming and tedious, however, the in silico analyses can still
facilitate to reach an informed decision for conducting a costly experiment.
For example, a druggable molecule must have certain ADMET (absorption,
distribution, metabolism, excretion, and toxicity) properties to pass through
clinical trials. If a compound does not have required ADMETs, it is likely to
be rejected. To avoid such failures, different bioinformatics tools have been
developed to predict ADMET properties, which allow researchers to screen a
large number of compounds to select most druggable molecule before launching of
clinical trials. Earlier, a number of reviews on various specialized aspects of
bioinformatics have been written. However, none of these articles makes it
suitable for a scientist who does not belong to computational biology. Here, we
take the opportunity to introduce various tools of bioinformatics to a
non-specialist reader to help extract useful information regarding his/her
project. Therefore, we have selected only those areas where these tools could
be highly useful to obtain useful information from biological data. These areas
include analyses of DNA/protein sequences, phylogenetic studies, predicting 3D
structures of protein molecules, molecular interactions and simulations as well
as drug designing. The organization of text in each section starts from a
simplistic overview of each area followed by key reports from literature and a
tabulated summary of related tools, where necessary, towards the end of each section.

`•
Genbank
• Expasy
• ENSAMBLE
• READSEQ
• ENTREZ
• Magpie
• GenQuiz
• GENSCAN
• ORF finder
• Modeller
• iTASSER

i.                  
iTassar

                           Iterative Threading ASSEmbly Refinementis
a bioinformatics method for predicting three-dimensionalstructure model of
protein molecules from amino acid sequences.

Specificity

                                    It detects
structure templates from the Protein Data Bank by a technique called
fold recognition or threading. The full-length structure models are
constructed by reassembling structural fragments from threading templates using
Replica Exchange Monte Carlo Simulation. I-TASSER is one of the most
successful protein structure prediction methods in the community-wide CASP experiments.
I-TASSER has been extended for structure-based protein function predictions,
which provides annotations on ligand binding site, gene ontology and enzyme commission by structurally
matching structural models of the target protein to the known proteins in protein
function databases. It has an on-line server built in the Yang Zhang Lab at
the University of Michigan, Ann Arbor, allowing users to submit sequences and obtain
structure and function predictions. A standalone package of  I-TASSER
is available for download at the I-TASSER website.

Functioning

The I-TASSER server allows users to
generate protein structure and function predictions.

There are in the form of inputs
and the outputs

These are inputs.

·        
Input

·        
Mandatory:

·        
Amino
acid sequence with length from 10 to 1,500 residues

·        
Optional

·        
Contact
restraints

·        
Distance
maps

·        
Inclusion
of special templates

·        
Exclusion
of special templates

·        
Secondary
structures

There are following outputs.

·        
Output

·        
Structure
prediction:

·        
Secondary
structure prediction

·        
Top
10 threading alignment from LOMETS

·        
Solvent
accessibility prediction.

·        
Top
10 proteins in PDB which are structurally closest to the predicted models

·        
Top
5 full-length atomic models (ranked based on cluster density)

·        
Estimated
accuracy of the predicted models B-factor estimation

·        
Function prediction:

·        
Enzyme
Classification and the confidence score

·        
Gene
Ontology terms and the confidence score

·        
Ligand-binding
sites and the confidence score

·        
An
image of the predicted ligand-binding sites

Conclusion and Future Prospects

                       
Bioinformatics is comparatively young disciplines and it has progressed
very fast in the last few years. It has made it possible to test our hypotheses
virtually and therefore allows to take a better and an informed decision before
launching costly experimentations. Although, more and more tools for analyzing
genomes, proteomes, predicting structures, rational drug designing and
molecular simulations are being developed; none of them is ‘perfect.One thing
is clear that the future research will be guided largely by the availability of
databases, which could be either generic or specific. It can also be safely
assumed, based on the developments in the field of bioinformatics, that the
bioinformatics tools and software packages would be able to give results that
are more accurate and thus more reliable interpretations. Prospects in the
field of bioinformatics include its future contribution to functional
understanding of the human genome, leading to enhanced discovery of drug
targets and individualized therapy. Thus, bioinformatics and other scientific
disciplines have to move hand in hand to flourish for the welfare of humanity.

There are some other tools and the softwares

1.     
Genbank

2.     
Expasy

3.     
Ensamble

4.     
Readseq

5.     
Enterez

6.     
Magpie

7.     
GenQuiz

8.     
Genscan

9.     
ORF finder

10. 
Modeller

11. 
DDBJ

12. 
PIR

13. 
AceDB

14. 
Bankit

15. 
Sequin

16. 
Spin

17. 
Panther

18. 
NCBI ORF finder

19. 
ORF Prediction

20. 
ORF Investigation

21. 
RNA Seq etc

REFERENCES

Mount DW (2004) Sequence and
genome analysis. New York: Cold Spring.

Hesper B, Hogeweg P (1970)
Bioinformatica:eenwerkconcept. Kameleon 1:28-9.

Hogeweg P (2011) The
roots of bioinformatics in theoretical biology. PLoS Comput Biol 7:
e1002021.

Peitsch MC (1996) ProMod
and Swiss-Model: Internet-based tools for automated comparative protein
modelling. Biochem Soc Trans 24: 274-279.

Dibyajyoti S, Bin ET, Swati P
(2013) Bioinformatics: The effects on the cost of drug discovery. Galle
Med J 18:44-50.

Ouzounis CA, Valencia A (2003) Early
bioinformatics: the birth of a discipline–a personal view. Bioinformatics
19: 2176-2190.

Molatudi M, Molotja N, Pouris A
(2009) Abibliometric study of bioinformatics research
in South Africa. Scientometrics 81:47-59.

Ouzounis CA (2012) Rise
and demise of bioinformatics? Promise and progress. PLoS Comput Biol 8:
e1002487.

Geer RC, Sayers EW (2003) Entrez:
making use of its power. Brief Bioinform 4: 179-184.

Parmigiani G, Garrett ES,
Irizarry RA, Zeger SL (2003) The analysis of gene expression data: an
overview of methods and software, Springer, New York.

 

 

 

 

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