Performance Improvement of BLAST with Use of MSA Techniques to Search Ancestor Relationship among Bioinformatics Sequences

Invention Journal of Research Technology in Engineering & Management (IJRTEM)
ISSN: 2455-3689
www.ijrtem.com Volume 2 Issue 5 ǁ May 2018 ǁ PP 13-21
|Volume 2| Issue 5 | www.ijrtem.com | 13 |
Performance Improvement of BLAST with Use of MSA Techniques to
Search Ancestor Relationship among Bioinformatics Sequences
1,
Dr. Kamal Shah, 2,
Mr. Nilesh N. Bane
1,
Professor & Dean- R & D TCET
2
ME PART-II TCET
ABSTRACT: BLAST is most popular sequence alignment tool used to align bioinformatics patterns. It uses
local alignment process in which instead comparing whole query sequence with database sequence it breaks
query sequence into small words and these words are used to align patterns. it uses heuristic method which
make it faster than earlier smith-waterman algorithm. But due small query sequence used for align in case of
very large database with complex queries it may perform poor. To remove this draw back we suggest by using
MSA tools which can filter database in by removing unnecessary sequences from data. This sorted data set then
applies to BLAST which can then indentify relationship among them i.e. HOMOLOGS, ORTHOLOGS,
PARALOGS. The proposed system can be further use to find relation among two persons or used to create
family tree. Ortholog is interesting for a wide range of bioinformatics analyses, including functional annotation,
phylogenetic inference, or genome evolution. This system describes and motivates the algorithm for predicting
orthologous relationships among complete genomes. The algorithm takes a pairwise approach, thus neither
requiring tree reconstruction nor reconciliation
KEYWORDS: BLAST, MSA, CLUSTALW, ORTHOLOGS, PARALOGS, RSD, CLIQUE
I. INTRODUCTION
Background: Bioinformatics is a field which is related to developing and improving methods for storing,
organizing. Retrieving and analyzing biological data. An important work in bioinformatics is to create a tool to
capture useful biological knowledge. Bioinformatics is a field which is related to developing and improving
methods for storing, organizing. Retrieving and analyzing biological data. An important work in bioinformatics
is to create tools to capture useful biological knowledge. It can be used in many areas of science and technology
to process biological data. High configuration machines are used to process bioinformatics data at a highest
rate. . Knowledge generation from biological data may involve study of areas like data mining, artificial
intelligence, simulation, Genetic Algorithms, Fuzzy Logic and image processing. DNA patterns, RNA patterns,
protein Patterns are some of the examples of Bioinformatics pattern. DNA patterns are of DNA sequences.
Various functional structures such as promoters and genes, or larger structures like bacterial or viral genomes,
can be analyzed using DNA patterns. Protein patterns are sequence of amino acid molecule connected to each
other. It contains almost 16 type of amino acid molecule.
Protein patterns example:-
“MQKSPLMEKASFISKLFFSWTTPILRKGYRHHLELSDIYQAPSADSADHLSEKLEREWDREQASKKNP
QLIHALRRCFFWRFLFYGILLYLGEVTKAVQPVLLGRIIASYDPENKVERSIAIYLGIGLCLLFIVRTLLLH
PAIFGLHRIGMQMRTAMFSLIYKKTLKLSSRVLDKISIGQLVS...”
It is the large sequence of characters which represent their respective amino acid molecule. To extract
information from these large patterns is necessary to match them with another pattern. Sequence alignment
method has been using to capture similarity between these patterns. In bioinformatics, a sequence alignment is a
way of matching the sequences of DNA, RNA, or protein to identify regions of similarity that may be a
consequence of functional, structural, or evolutionary relationships between the sequences [1]. The sequences
are represented in matrix form. Each row in matrix represents aligned sequences of amino acid residues. Spaces
are introduced between the residues so that identical characters are come under same column. The sequence
alignment process has two methods: based on completeness and based on number of sequences in used.
Sequence alignment based on completeness of sequences deals with way of matching sequence i.e. whether
whole sequences are matched to achieve the alignment of sequences or will it be mapped partially. Depending
upon nature it can be categorized as GLOBAL and LOCAL mapping. Here are examples of global and local
alignments. The global alignment looks for comparison over the entire range of the two sequences involved.

Performance Improvement of BLAST with Use of MSA…
GCATTACTAATATATTAGTAAATCAGAGTAGTA
| | | | |
AAGCGAATAATATATTTATACTCAGATTATTGCGCG
As you can see only a portion of this two sequences can be aligned. By contrast, when a local alignment is
performed, a small seed is uncovered that can be used to quickly extend the alignment. The initial seed for the
alignment:
TAT
| | |
And now the extended alignment:
TATA T ATTAGTA
| | | | | |
Sequence alignment based on number of sequences in used deals with total number sequences involving
alignment processive. it checks whether two sequences are aligned at a time or ‘n’ sequences are used to for
sequence alignment. Depending upon No of sequences it can be categorized as ‘Pairwise sequence alignment’
and ‘multiple sequence alignment ‘These aligned sequences are matched based their match score which can
calculate using scoring scheme. The scoring scheme is consisting on character substitution score (i.e. score for
each possible character replacement) plus penalties for gaps. The alignment score is sum of substitution scores
and gap penalties. The alignment score reflects “GOODNESS OF ALIGNMENT”
Protein substitution matrices are significantly more complex than DNA scoring matrices. PAM, i.e. “Point
Accepted Mutation" family AND BLOSUM, i.e. "Blocks Substitution Matrix" family (BLOSUM62,
BLOSUM50, etc.) are most commonly used substitution matrix for protein substitutions. BLOSUM62 is the
matrix calculated by using the observed substitutions between proteins which have at most 62% sequence
identity. Various algorithms have been used to align sequence but most popularly known algorithm is BLAST
(Basic Local Alignment Search Tool). The BLAST accepts query sequence and weighted matrix as input and
give output in various format such as HTML, Plain text etc. It follows local paired matching of sequences. It
breaks input query sequence into small words (e.g. default 3 characters/per word). It will scan whole database
for word matches. Extends all matches to seek high scoring alignments due to which it is highly sensitive to
speed.
The classification of genes according to evolutionary relations is essential for many aspects of comparative and
functional genomics. Evolutionary relations are often described as pairwise relations. Two genes that share a
common ancestor are defined as homologs, while genes that are similar in sequence without a common origin
are termed analogs. Homologs can be divided into several classes [1]:
orthologs, which originate from a speciation event
paralogs, which originate from gene duplication
xenologs, which originate from horizontal gene transfer.
Orthologs are valuable in numerous analyses, including reconstruction of species phylogenies, protein function
inference, database annotation, and genomic context analysis.
Motivation: Suppose organization contains bioinformatics database containing ‘n’ number of sequences where
‘n’ is very large number. And they want to create ortholog clusters which highly random in nature using
BLAST. Even though BLAST is most popular algorithm in terms of and even though it is fastest than Smith–
Waterman algorithm it is less accurate in nature as well as it uses word by word local alignment matching
process it may take unnecessary extra time which can be minimized. Using BLAST bioinformatics sequences
can categorize into ORTHOLOGS AND PARALOGS which can be used to construct inheritance tree in given
context.

Scope of the system: This system can be used to identify related sequences in bioinformatics database which
can be used to identify homology among them. Along with that system aims towards increasing speed and
accuracy of sequence alignment process. It can be used to predict relationship among two persons.
II. RESEARCH GAPS AND PROBLEM DEFINITION
Research Gaps: A number of techniques have been developed for Bioinformatics Sequence Alignment such as
BLAST, MSA, smith-waterman, Needleman-Wunsch algorithm.
Needleman-Wunsch algorithm : This algorithm first take the two sequences and create a 2-dimensional array
with the length of Multiply of the two sequence’s length, each cell can be evaluated from the maximum of the
three cells around it and at the same time keep a pointer of the maximum value to make the trace-back to get
optimal solution the problem of these dynamic algorithms is that it take more time to fill all the matrix of the
two sequences, although there are unused data but it must be found to help in filling all the cells in the matrix, so
these algorithm take many times to make this computation
Smith-Waterman algorithm: The Smith-Waterman algorithm is a method of database similarity searching
which considers the best local alignment between a query sequence and sequences in the database being
searched. The Smith-Waterman algorithm allows consideration of indels (insertions/deletions) and compares
fragments of arbitrary lengths between two sequences and this way the optimal local alignments are identified.
Sequence similarity searches performed using the Smith-Waterman algorithm guarantees you the optimal local
alignments between query and database sequences. Thus, you are ensured the best performance on accuracy and
the most precise results - aspects of significant importance when you cannot afford to miss any information
gained from the similarity search as e.g. when searching for remote homology. The Smith-Waterman algorithm
being the most sensitive algorithm for detection of sequence similarity has however some costs. Time is a
considerable disadvantage and performing a Smith-Waterman search is both time consuming and computer
power intensive.
Basic Local Alignment Search Tool: BLAST (Basic Local Alignment Search Tool) also identifies homologous
sequences by database searching. BLAST identifies the local alignments between sequences by finding short
matches and from these initial matches (local) alignments are created. The BLAST algorithm is a development
of the Smith-Waterman algorithm suggesting a time-optimized model contrary to the more accurate but time-
consuming calculations of the Smith-Waterman algorithm BLAST (Basic Local Alignment Search Tool) also
identifies homologous sequences by database searching. BLAST identifies the local alignments between
sequences by finding short matches and from these initial matches (local) alignments are created. The BLAST
algorithm is a development of the Smith-Waterman algorithm suggesting a time-optimized model contrary to
the more accurate but time-consuming calculations of the Smith-Waterman algorithm
Problem Definition: BLAST is very popular tool to align protein sequence in Local Pair wise alignment
method. The problem with BLAST is it can work best only for pair of single queries. IT may fail with complex
queries as well as in large database while using isolated.
“if ‘S’ is candidate bioinformatics sequence set contains ‘n’ bioinformatics sequences find the optimal
way of creating clusters of orthologs sequences and find parlogs among them” .
Research Objectives
• To find the efficient method or techniques for sequence alignment
• Methods should be such that it improves performance and accuracy of BLAST
• System can be able to identify orthologs and paralogs given query sequence from available datasets
III. PROPOSED METHODOLOGY
Materials and methods Technology
Software etc.
BLOSUM62: In bioinformatics, for sequence alignment of proteins BLOSUM matrix is used as substitution
matrix. BLOSUM matrices are used to score alignments between evolutionarily divergent protein sequences.
They are based on local alignments. BLOSUM matrices were first introduced in a paper by Henikoff S. and
Henikoff J.G. They scanned the BLOCKS database for very conserved regions of protein families and then
counted the relative frequencies of them and their substitution probabilities. Then, they calculated a log-
odds score for each of the 210 possible substitution pairs of the 20 standard amino acids.

Multiple sequence alignment tools: Multiple Sequence Alignment (MSA) is generally the alignment of three
or more biological sequences (protein or nucleic acid) of similar length. From the output, homology can be
inferred and the evolutionary relationships between the sequences studied. Used to generate a concise,
information-rich summary of sequence data. Sometimes used to illustrate the dissimilarity between groups of
sequences. Alignments can be treated as models that can be used to test hypotheses. It uses two method dynamic
and progressive alignment methods. Crustal has become the most popular algorithm for multiple sequence
alignment. This program implements a progressive method for multiple sequence alignment. As a progressive
algorithm, Crustal adds sequences one by one to the existing alignment to build a new alignment. The order of
the sequences to be added to the new alignment is indicated by a precomputed phylogenetic tree, which is called
a guide tree. The guide tree is constructed using the similarity of all possible pairs of sequences. The algorithm
consists of 3 phases that are described below:
Stage 1 —Distance Matrix: All pairs of sequences are aligned separately in order to calculate a distance matrix
based on the percentage of mismatches of each pair of sequences.
Stage 2 —Neighbor joining: The guide tree is calculated from the distance matrix using a neighbor joining
algorithm. The guide tree defines the order which the sequences are aligned in the next stage.
Stage 3 —Progressive alignment: The sequences are progressively aligned following the guide tree.
Now we discuss the complexity for all stages. Give N sequences and sequence length L, calculating the distance
matrix in stage 1 takes O(N2L2) time. A neighbor joining algorithm is O(N4) time for constructing the guide
tree in stage 2. And for the last stage, progressive alignment is O(N3+NL2) time. The summary is shown in
Table 1 [17].
Table 1. Complexity of the sequential Crustal. The big-O asymptotic complexity of the elements of
Crustal as a function of L, the sequence length, and N, the number of sequences, retaining the highest-
order terms in N with L fixed and vice versa.
Stage Time Complexity
Distance Matrix O(N2
L2
)
Neighbor joining O(N4
)
Progressive alignment O(N3
+NL2
)
Total O(N4
+L2
)
Basic Local Alignment Search Tool
(BLASTP)
BLAST is very popular tool to align protein sequence in Local Pair wise alignment method. The problem with
BLAST is it can work best only for pair of single queries. IT may fail with complex queries as well as in large
database
Working of Basic Local Alignment Search Tool:
▪ A locally maximal segment pair (LMSP) is any segment pair (s, t) whose score cannot be improved by
shortening or extending the segment pair. Align all words with sequence in database
▪ A maximum segment pair (MSP) is any segment pair (s, t) of maximal alignment score σ(s, t).
▪ Given a cutoff score S, a segment pair (s, t) is called a high-scoring segment pair (HSP), if it is locally
maximal and σ(s, t) ≥ S.

Finally, a word is simply a short substring of ﬁxed length w.
▪ Localization of the hits: The database sequence d is scanned for all hits t of w-mers s in the list, and the
position of the hit is saved.
▪ Detection of hits: First all pairs of hits are searched that have a distance of at most A (think of them lying
on the same diagonal in the matrix of the SW-algorithm).
▪ Extension to HSPs: Each such seed (s, t) is extended in both directions until its score σ(s, t) cannot be
enlarged (LMSP). Then all best extensions are reported that have score ≥ S, these are the HSPs. Originally
the extension did not include gaps, the modern BLAST2 algorithm allows insertion of gaps.
▪ The list L of all words of length w that have similarity > T to some word in the query sequence q can be
produced in O(|L|) time.
▪ These are placed in a “keyword tree” and then, for each word in the tree, all exact locations of the word in
the database d are detected in time linear to the length of d.
▪ As an alternative to storing the words in a tree, a ﬁnite-state machine can be used.found to have the faster
implementation.
The reciprocal smallest distance algorithm: One such computationally intensive comparative genomics
method, the reciprocal smallest distance algorithm (RSD), is particularly representative of the scaling problems
faced by comparative genomics applications. RSD is a whole-genomic comparative method designed to detect
orthologous sequences between pairs of genomes. The algorithm employs BLAST [2] as a first step, starting
with a subject genome, J, and a protein query sequence, i, belonging to genome I. A set of hits, H, exceeding a
predefined significance threshold (e.g., E < 10-10, though this is adjustable) is obtained. Then, using crustal,
each protein sequence in H is aligned separately with the original query sequence i. If the alienable region of the
two sequences exceeds a threshold fraction of the alignment's total length (e.g., 0.8, although this is also
adjustable), the BLOSUM62 is used to obtain a maximum likelihood estimate of the number of amino acid
substitutions separating the two protein sequences, given an empirical amino acid substitution rate matrix. The
model under which a maximum likelihood estimate is obtained in RSD may include variation in evolutionary
rate among protein sites, by assuming a gamma distribution of rate across sites and setting the shape parameter
of this distribution, α, to a level appropriate for the phylogenetic distance of the species being compared. Of all
sequences in H for which an evolutionary distance is estimated, only j, the sequence yielding the shortest
distance, is retained. This sequence j is then used for a reciprocal BLAST against genome I, retrieving a set of
high scoring hits, L. If any hit from L is the original query sequence, i, the distance between i and j is retrieved
from the set of smallest distances calculated previously. The remaining hits from L are then separately aligned
with j and maximum likelihood distance estimates are calculated for these pairs as described above. If the
protein sequence from L producing the shortest distance to j is the original query sequence, i, it is assumed that a
true orthologous pair has been found and their evolutionary distance is retained.
Clique Algorithm: To search for maximal, completely connected subgraphs in a graph, where the vertices are
genes and the edges are verified pairs. To compute cliques, algorithms exist to maximize either the size of the
clique (number of vertices) or the total weight of cliques (sum of edge weights). Figure A shows a graph with
edges between all vertices except (z1, z2) and (z1, y2), which are paralogous relations. The highest scoring
partition is {w1, x1, z1}, {y2, z2}, with the total sum of edge weights of 700 + 800 + 900 + 1000 = 3600. The
score is higher than the highest scoring maximum size clique {w1, x1, y2, z2}, {z1}, where the sum of the
scores is 200+300+400+500+800+1000 = 3200. Hence, a smaller clique is chosen due to higher edge weights,
which correctly assigns orthologs according to the hypothetical evolutionary scenario in Figure B where the
duplication gives subscripts that correspond to functionality. Finding cliques is a NP-complete problem and the
implementations used here are based on an approximation of the vertex cover problem [19]. Each clique
constitutes an orthologous group, where the sequence pairs in an orthologous group are denoted group pairs
(GP), corresponding to close orthologs.
Figure 1:A.An example graph containing one 4-clique, four 3-cliques, and eight 2-cliques is provided. The
highest scoring partition of the graph is {w1, x1, z1}, {y2, z2}. B. A possible evolutionary scenario
corresponding to the graph.

Proposed model: Core objective of this system is to combine method of multiple sequence alignment and Basic
linear alignment search so as to increase speed and efficiency of target system.
Ortholog Cluster Creator: It accept a set of complete genomes and gives pairs of orthologous genes that will
be clustered into orthologous groups. The algorithm follows four steps
ALL X ALL Comparison: - To find homology, system will compute pairwise alignments between all pairs of
sequences for all genes in all genomes. Pairs with good alignment scores are selected as candidate pairs. The
goal of the first step is homology detection. This includes following steps
a. Given dataset has been applied to the multiple sequence alignment tool which will introduce gap for
alignment which will allow short sequence to compare with long one or allow partial sequence to compare
with the whole. With help of BLOSUM62, all pairs of sequences are aligned separately in order to calculate
a distance matrix based on the percentage of mismatches of each pair of sequences and calculate score.
Depending upon score filter the database. proposed system used CLUSTALW algorithm for this process.
Crustal has become the most popular algorithm for multiple sequence alignment. This program implements
a progressive method for multiple sequence alignment. As a progressive algorithm, ClustalW adds
sequences one by one to the existing alignment to build a new alignment.
b. This selected dataset has been applied to Basic Linear Alignment Search Tool which is very popular tool to
align protein sequence in Local Pair wise alignment method.
c. Since consider entire proteins as the basic evolutionary unit, why then not use global alignments? Protein
ends are often variable, and thus, it is reasonable to ignore them by using local alignments. To guarantee
that a significant fraction of a sequence is aligned, use a length tolerance criterion. The length of the shorter
aligned sequence must be at least the fraction ℓ of the longest sequence. That is
min (|a1|, |a2|) > ℓ·max (|s1|, |s2|)
where a1 and a2 are the lengths of the aligned subsequences of s1 and s2. a "plateau" is observed for 0.6 < ℓ <
0.9. Alignments that pass both the length and score criteria are upgraded to candidate pairs (CP).
The all-against-all step is computationally expensive, and the run time increases quadratically with the size of a
protein sequence. The use of a heuristic-based algorithm such as BLAST could potentially increase the speed of
the search
Formation of Stable Pairs: - Orthologs are usually the nearest genes in two genomes, because they started
diverging at speciation, whereas paralogs started diverging at a duplication prior to speciation. Genes across
genomes that are mutually the most closely related sequences, taking into account inference uncertainty, are
upgraded to stable pairs. In the second step of the algorithm, potential orthologs are detected by finding
sequences in two genomes that are near to each other than to any other sequence in the other sequence. These
sequences called as stable pairs (SP). This name was chosen due to its close association with the stable
marriage problem in computer science. The rsd algorithm is used to find candidate pairs.
Verification of Stable pair: - Although the construction of stable pairs is likely to identify the corresponding
ortholog of each sequence, at least one special case exists in which systematic failure will occur: differential
gene loss. This problem affects all pairwise approaches, and is shown in Fig A. An ancient duplication event is
followed by two speciation events resulting in three species X, Y, and Z. In two of these species, each of the
duplicates is lost (e.g. x2 and y1), and as a result, when comparing species X and Y, x1 and y2 are the highest
scoring match. In such a case, (x1, y2), although paralogs, form a stable pair.
Figure 2:Identification of potential paralogs

The purpose of the third step is to detect such stable pairs corresponding to non-ortholog. The presence of a
third genome Z, which has retained both copies z1 and z2 of the duplication event, acts as a witness of non-
ortholog. We previously described the details of this procedure [21], and the idea is illustrated in Figure B.
If dx1z1 is significantly shorter than dx1z2 and dy2z2 is significantly shorter than dy2z1, there is evidence that
x1 and y2 may not be orthologs. Figure C depicts the most likely quartet predicted from the data provided in
Figure A. This approach can also be viewed as a tree-reconciliation that is based on quartets without assuming
any species tree topology.
Each stable pair is verified by comparison to all other genomes. Stable pairs for which no witness of non-
ortholog could be found are termed verified pairs (VP) and are likely to be orthologs. Furthermore, stable pairs
that are not verified were defined as broken pairs (BP) and are likely to correspond to paralogs.
Clustering of orthologs: - The final step of the algorithm creates groups of orthologs. Such grouping is non-
trivial, because orthology is defined over pairs of sequences and is not necessarily a transitive relation. For
instance, a sequence in one genome may form several verified pairs with sequences in another genomes,
corresponding to several orthologous relations (co-orthologs). These in turn cannot be orthologous to each other.
This problem is addressed by making available both pairwise orthologous relations (the verified pairs) and
groups of genes in which all pairs are orthologs. A clique algorithm is used to search for maximal, completely
connected subgraphs in a graph, where the vertices are genes and the edges are verified pairs. Each clique
constitutes an orthologous group, where the sequence pairs in an orthologous group are denoted group
pairs (GP), corresponding to close orthologs.
Table 1:Sequence pairs and their corresponding evolutionary relationships
Pairs Evolutionary Relation
All pairs (AP) Any
Candidate pairs (CP) Homologs
Stable pairs (SP) Orthologs, Pseudo-
orthologs
Broken pairs (BP) Paralogs
Verified pairs (VP) Orthologs
Group pairs (GP) Close orthologs
IV. EXPECTED OUTCOMES/
DELIVERABLES
Expected Outcome Using MSA: The MSA tools will provide efficient way to filter out given database with
‘n’ sequences. which can be reduce size of dataset. which is help to remove unmatched sequences reference to
the given query string. Which in turns reduce load on BLAST tool
Expected Outcome Using BLAST: The BLAST tool is used to find out relevant sequence from filtered dataset
outputted from MSA tools. It can be use to find out their relationship score so as to we can able to categorized
them.
Deliverables: - An application which accepts bioinformatics textual sequence and give HOMOLOGS sequences
available in Database. this application can further predict relationship among two humans.
V. CONCLUSION
We have proposed new hybrid method for aligning bioinformatics sequences to understand the nature of
relationship among them. The proposed method will use MSA tools to deduct irrelevant data from available
database and decrease size of data. then BLAST tool will be used to identify matches among them and try to

identify ancestor relationship among query string and clusters of ortholog from available data in bioinformatics
database. The core intention of this proposed method is to decrease unnecessary time utilized by BLAST in
comparing word by word and increase accuracy of clustering. Orthology is useful for a wide range of
bioinformatics study, including functional annotation, phylogenetic inference, or genome evolution. This system
describes and motivates the algorithm for predicting orthologous relationships among complete genomes. The
algorithm takes a pairwise approach, thus neither requiring tree reconstruction nor reconciliation, and offers the
following improvements over the standard bidirectional best hit approach: i) the use of evolutionary distance, ii)
a tolerance that allows the inclusion of one-to-many and many-to-many orthologs, iii) consideration of
uncertainty in distance estimations, iv) detection of potential differential gene losses.
REFERENCES
[1] Min-Sung Kim, Choong-Hyun Sun, Jin-Ki Kim, Gwan-Su Yi (2006) Whole Genome Alignment with
BLAST on Grid Environment, Proceedings of The Sixth IEEE International Conference on Computer
and Information Technology (CIT'06)
[2] Sing-Hui To, Hoon-Jae Lee, Kyeong-Hoon D0 (2009), Basic Sequence Search by Hashing Algorithm
in DNA Sequence Databases, ISBN 978-89-5519-139-4, Feb 15-18,2009, ICACT
[3] Rodolfo Bezerra Batista and Alba Cristina Magalhaes Alves de Melo (2006), Z-align: An Exact and
Parallel Strategy for Local Biological Sequence Alignment in User-Restricted Memory Space
[4] Sangha Mitra Bandyopadhyay, Senior Member, IEEE, and Ramakrishna Mitra (2009), A Parallel Pair
Wise Local Sequence Alignment Algorithm
[5] Amlan Kundu, Suvasini Panigrahi, Shamika Sural and Arun K. Majumdar in (2009)BLAST-SSAHA
Hybridization for Credit Card Fraud Detection, Dependable and Secure Computing, IEEE
Transactions on Volume: 6 , Issue: 4
[6] Stephen Pellicer∗, Guihai Chen, Member, IEEE, Keith C. C. Chan, and Yi Pan, (2008) Distributed
Sequence Alignment Applications for the Public Computing Architecture, IEEE TRANSACTIONS
ON NANOBIOSCIENCE, VOL. 7, NO. 1, MARCH 2008
[7] Ken D. Nguyen, Member, IEEE, and Yi Pan, Senior Member, IEEE in (2011) An Improved Scoring
Method for Protein Residue Conservation and Multiple Sequence Alignment
[8] Farhana Naznin, Member, IEEE, Ruhul Sarker, Member, IEEE, and Daryl Essam (2012) Progressive
Alignment Method Using Genetic Algorithm for Multiple Sequence Alignment, IEEE
TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 16, NO. 5, OCTOBER 2012
[9] Ching Zhang and Andrew K. C. Wong. (1998). A Technique of Genetic Algorithm and Sequence
Synthesis for Multiple Molecular Sequence Alignment 0-7803-4778-1 198 @ 1998 IEEE
[10] A. Agarwal and s.k. khaitan (2008) A new heuristic for multiple sequence alignment,
Electro/Information Technology, 2008. EIT 2008. IEEE International Conference, 978-1-4244-2029-2
[11] Jagadamba, P.V.S.L. ; DST Project, JNTUK, Kakinada, India ; Babu, M.S.P. ; Rao, A.A.; Rao, P.K.
(2011).An improved algorithm for Multiple Sequence Alignment using Particle Swarm Optimization, I
EEE TRANSACTIONS ON NANOBIOSCIENCE, VOL. 10, NO. 4, DECEMBER 2011
[12] Z. Liu, J. Bornean, and T. Jiang (2004) A Software System for Gene Sequence Database Construction
Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco, CA, USA
• September 1-5, 2004
[13] Ken D. Nguyen, Member, IEEE, Yi Pan, Member, IEEE (2013) A Knowledge-based Multiple
sequence Alignment Algorithm, IEEE TRANSACTIONS ON COMPUTATIONAL
BIOLOGY AND BIOINFORMATICS, 1545-5963/13 © 2013 IEEE
[14] Jun Wang and Young Sun (2012) A Sliding Window and Keyword Tree Based Algorithm for Multiple
Sequence Alignment, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery
(FSKD 2012),978-1-4673—0024-7/10, IEEE
[15] Bugra Ozer, Gizem Gezici, Cem Meydan, Ugur Sezerman (2009). Multiple Sequence Alignment Based
on Structural Properties IEEE-2009
[16] Joanne Bai, Siamak Rezaei (2005). Multithreaded Multiple Sequence Alignments. Proceedings of the
2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September
1-4, 2005
[17] R.C. Edgar, “Muscle: a multiple sequence alignment method with reduced time and space complexity,”
BMC Bioinformatics. 2004, vol. 5, 2004.
[18] Distinguishing homologous from analogous proteins. Fitch WM Syst Zool. 1970 Jun; 19(2):99-113.

[19] Balasubramanian R, Fellows M, Raman V. An improved fixed-parameter algorithm for vertex
cover. Information Processing Letters. 1998;65:163–168. doi: 10.1016/S0020-0190(97)00213-5.
[20] A dimensionless fit measure for phylogenetic distance trees.[J Bio inform Comput Biol. 2005]
[21] Detecting non-orthology in the COGs database and other approaches grouping orthologs using
genome-specific best hits. Decimos C, Beckmann B, Roth AC, Gannet GH, Nucleic Acids Res.
2006; 34(11):3309-16.
[22] Needleman, Saul B. & Wunsch, Christian D. (1970). "A general method applicable to the search for
similarities in the amino acid sequence of two proteins". Journal of Molecular Biology
[23] Hierarchical clustering algorithm for comprehensive orthologous-domain classification in multiple
genomes(https://www.ncbi.nlm.nih.gov)
[24] An Algorithm to Cluster Orthologous Proteins across Multiple Genomes, Sunshin Kim, Chung Sei
Rhee, Jung-Do Choi
[25] Automatic protein function annotation through candidate ortholog clusters from incomplete genomes
A. Vashist; C. Kulikowski; I. Muchnik 2005 IEEE Computational Systems Bioinformatics Conference
- Workshops (CSBW'05)
[26] Clustering orthologs based on sequence and domain similarities Fa Zhang; Sheng Zhong Feng; H.
Ozer; Bo Yuan Eighth International Conference on High-Performance Computing in Asia-Pacific Region
(HPCASIA'05)
[27] Ortholog Clustering on a Multipartite Graph Akshay Vashist; Casimir A. Kulikowski; Ilya Muchnik
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Dr. Kamal Shah, and Mr. Nilesh N. Bane. “Performance Improvement of BLAST with Use
of MSA Techniques to Search Ancestor Relationship among Bioinformatics
Sequences.” Invention Journal of Research Technology in Engineering & Management
(IJRTEM), vol. 2, no. 5, 11 May 2018, pp. 13–21., www.ijrtem.com.

Performance Improvement of BLAST with Use of MSA Techniques to Search Ancestor Relationship among Bioinformatics Sequences

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Performance Improvement of BLAST with Use of MSA Techniques to Search Ancestor Relationship among Bioinformatics Sequences