1:-Introduction ( Hoersch et al. 2000)( Hogeweg 2011)

1:-Introduction ( Hoersch et al. 2000)( Hogeweg  2011)

Bioinformatics is a
emerging and developing field in the modern age. It has given many benefits to
scientist in their research projects. It provides this ease by using different
methods to store, retrieve and analyses the data either it is of biology or mathematics.
Biological data may be of DNA, RNA and protein and all these biomolecules
contain unique sequences which are related to their functions. Bioinformatics
approaches are emerging day by day which allows scientist to measure the
changes and regulation in genome wide genes simultaneously. Scientist has
devised many bioinformatics tools to analyses sequence of different nature and
all these tools serve different purposes. Sequence analyses usually refer to
collect that information of the biomolecule (nucleic acid and protein) which
gives it to its unique function. Different tools are used for different purposes.
Nature of analyses determines which type of tool should be used. Sequences are
subjected to these tools which predict its function according to their nature
and features which are very much related to biomolecules function. The
objective of this review is to encompass all the tools being used for
biological sequence analysis.

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                                2:-Bioinformatics Tools For Biological Seq Analysis

2.1:-Basic Local Alignment
Search Tool (BLAST) (Johnson et al. 2008)

It is sequence similarity
search tool which can be use through web to compare users query to databases of
sequence. Different types of blast compare combination of nucleotide or protein
queries to databases of nucleotide and protein. It usually finds similarity
between two sequences and then align these sequence it tells about the
alignment score expect value or E value of alignment. BLAST usually calculates
statical significance by comparing nucleotide and protein sequences to database
sequence. It can be implemented in a number of ways to find out similarity
between different sequences

2.2:-Clustal
Omega ( Sievers et al. 2011)

Multiple sequence alignment is very important in bioinformatics because
it compares homologous sequences .we have an accurate tool for this purpose
known as Clustal Omega. It can align sequence of any size. It has generated
alignments of over 190,000 sequences in very few hours. So basically it can
align very large number of sequences accurately. It has a number of features of
adding new sequences to the existing alignments or adding existing alignments
into the new sequences which help to align new sequences of DNA/RNA or protein. It allows user to specify a profile “HMM” which
is basically obtained from an alignment of the sequences that are similar to
the input set. Then sequences are aligned to these ‘external
profiles’ which help them align to the rest of the input set. This tool has a
full confidence on the accuracy of the alignments which are made by using it.
That’s why it is most widely used tool for multiple seq alignment of nucleotide
or protein.

2.3:-GENSCAN (Burge et al. 1997)

It is a tool which is used to identify the gene structures in genomic
DNA.It predicts the location of exons and introns in the genomic DNA sequences
from a variety of organisms. It resolved the fundamental biochemical issues of
specifying the accurate sequence determinants of transcription translation and
RNA splicing. It also identifies the
whole structure of exons and introns in genomic DNA.This tool can also estimate
the multiple genes in a sequence to deal with partial plus complete genes and
to forecast the consistent set of genes occurring on either or both strands of
DNA. It has more accuracy than existing methods, with 70-80% exons identified accurately.
It is expected that the statistical analysis of genes may give some clues to
the biochemical processes such as transcription, translation and RNA splicing
which define genes biologically.

2.4:-“CARNA”—alignment of RNA structure ensembles (Sorescu et al. 2012)

Several approaches are use now a day for RNA analysis.
These approaches compare the RNA sequences with already predicted or single RNA
structure simultaneously. But we need another tool for multiple sequence
alignment of RNAs available, where these approaches are not good to use and
have limitations. We introduced another tool for the multiple sequence
alignment of RNA with riboswitch or pseudoknot structures and this tool is
named as “CARNA”this tool supports multiple RNAs with conserved structure and
aligns these pseudoknots originally. So CARNA is basically useful for aligning
RNA riboswitch, which have more than one stable structure. We have to put RNA
sequence as input and analyze base pair probability matrices and align the
sequences on the basis of full ensembles of structures. So this tool is
specialized to align RNA with conserved structures.

2.5:- “IPknot”:  IP-based prediction of RNA pseudoKNOTs (
Sato
et al. 2011)

It is used
for the prediction of RNA secondary structures with pseudokots based on the
accuracy of predicted structures. It breaks down the RNA structure with pseudoknot
into a set of pseudoknot free structures and predict base pairing
probability distribution that considers pseudoknot.It is an IP(integer
programming) based prediction tool for RNA having pseudoknot .it is more
accurate than previously used IP based methods. It computes the base pair
possibilities used in the IP objective function and solve the IP problem to
estimate the optimal pseudoknoted RNA secondary structure. It can take a single
sequence or aligned sequence as input. Prediction accuracy of IPknot depends on
its scoring functions as this method uses approximate possibilities
distribution of pseudoknoted structures. Another fact is that IPknot can run
quite fast long sequences less than one thousands bases. So it is a fast and
accurate computational prediction tool for both single sequence analysis and
comparative sequence analysis.

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