INTRODUCTION this review we describe the use of


Bioinformatics is the one of the emerging
science discipline of inter relation of computer, biology mathematics and
statistics. Now days it become necessary for the research and establishment of
data in biological science. (Muhammad Aamer Mehmood, 2017)i

It develop difference methods and tools
that helpful in sequencing, retrieving and analysing the biological data. (Muhammad
Aamer Mehmood, 2017)

Bioinformatics tools uses in determining
molecular interaction, phylogenetic, properties of different properties of gene
and protein and prediction of physiological and structural expectation about
experiment. Thus it play importance role to proceed our experimental work in correct
direction and save our money and time. (Muhammad Aamer Mehmood, 2017)1

After completion of human genome project,
researcher all over the world begin to sequencing the other organism resulting
enormous biological data and handling large amount of data getting difficult. So
bioinformatics came into exist to storage, analysis and prediction result of
research works. Now it become necessary in the research of all the field of
biological science. With the increasing popularity of bioinformatics different
bioinformatics tools are introduced to compete the research in efficient manner
and lesser time. (Sharma, 2015) ii.The
later segment of this review we describe the use of different bioinformatics
tools in sequences alignment.




local alignment search tool (BLAST), estimates alignments directly that
optimize an of local similarity measurement, the maximal segment pair (MSP)
score. The basic algorithm is simple and robust; it can be used in a different
ways and applied in a diversity of contexts including DNA and protein sequence
database searches, motif searches, gene identification searches, and in the
analysis of multiple regions of similarity in long DNA sequences. (Altschul SF,

Clustal Omega

It is a fast multiple sequence alignment programme that is use align any
numbers of DNA or protein sequences with accurate result. 190, 000 alignment
can generated in a single processes within few hours by it. Facilitate the user
to reuse their alignment and save time to realignment their entire sequence in
every time, for example once the sequence, we can add a new sequence or the
available sequence can use to align new sequence. The key feature that make it
progressive alignment approach is method of guide tree making. Usually, include
number of sequence (N) to compare to required time and memory requirement of O
(N2). (Altschul SF, 1990)iv


It is a computer program that is used to
determine the complete gene sequence of can predicted the structure of
intron and exon in in gene. It have capacity to determine the more than one
gene in a given sequence   of partial as well as complete can
also be uses to predict the specific gene set either one or both strand of DNA.
The higher accuracy of GENSCANE than other existing method is revealed at what
time tested on standardized sets of vertebrate and human genes, with 75 to 80%
of exons recognized exactly. It applicable of determining fairly accurately the
consistency of every identified exon. For sequences of differing C + G content
and for different detected with high levels of accuracy (Burge C, 1997)v

Protein sequence analysis


HMMER, (Hidden Markov Model for Local Sequence-Structure) is a hidden
Markov model that is use to predict the protein structure. This program take an
amino acid probability distribution (or profile) as input for each residue
position. It has the programs needed for secondary structure prediction,
beginning with a sequence profile. HMMSTR enable to identify recurrent confined
features of protein sequences and structures that exceed boundaries of protein
family. The HMM can predicts secondary structure with 74.3 % accuracy. It
involves higher probability to coding sequence as compare to others dipeptide
model. It describe the angle of backbone torsion better former used method thus
it useful for construct the accurate tertiary structure. (Bystroff C,


Molecular evolutionary genetics analysis (MEGA) is a bioinformatics tool
for measuring evolutionary distances and phylogenetic trees construction, and
computing basic statistical quantities from molecular data such as nucleotide
and amino acid frequencies, transition/ transversion biases, codon frequencies
(codon usage tables), and the number of variable sites in specified segments in
nucleotide and amino acid sequences. It is written in C++ computer language and
it can be easily processed by IBM and IBM-compatible personal computers. This
program include three different methods of phylogenetic inference (UPGMA,
neighbor-joining and maximum parsimony) and two statistical tests of
topological differences. (Kumar S, 1994)vii. Different
versions of MEGA were introduce with the passage with improved graphical user
interface and better topographies and the latest version is MEGA7. (NT, 2017)viii


It is the computer program that is to predict comparative
protein tree-dimensional (3-D) structure by using the sequence alignment and
template structure. The prediction process involve fold assignment, target-template
alignment, model modelling and model elevation. MODELLER can calculate all non-hydrogen
atom by modelling the aligned sequence with atomic coordinate of template, and script can also calculate phylogenetic tree and de novo modelling in protein structure (Eswar N, 2006)ix



ii file:///E:/assidnment%20and%20presentation/sir%20shahid/role-of-bioinformatics-in-various-aspects-of-biological-research-a-mini-review.pdf


iv  Sievers F, Wilm A, Dineen D,
Gibson TJ, Karplus K, et al. (2011) Fast, scalable generation of high-quality
protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7: 539.

v .  Burge C, Karolin S (1997)
Prediction of complete gene structures in human genomic DNA. J Mol Biol 268:
78-94 .  Burge C, Karlin S (1997)
Prediction of complete gene structures in human genomic DNA. J Mol Biol 268:

vi .  Bystroff C, Thorsson V,
Baker D (2000) HMMSTR: a hidden Markov model for local sequence-structure
correlations in proteins. J Mol Biol 301: 173-190

vii   Kumar S, Tamura K, Nei M
(1994) MEGA: Molecular Evolutionary Genetics Analysis software for
microcomputers. Comput Appl Biosci 10: 189-191.

viii NT Khan MEGA – Core of Phylogenetic Analysis in Molecular
Evolutionary Genetics Journal // Journal of Phylogenetics &
Evolutionary Biology

ix  Eswar N, Webb B,
Martin-Renom MA, Shen MY, Pieper U, et al. (2006) Comparative protein structure
    modeling using Modeller. Curr Protoc