scholarly journals Parallelization of Pairwise Alignment and Neighbor-Joining Algorithm in Progressive Multiple Sequence Alignment

2018 ◽  
Vol 9 (1) ◽  
pp. 234
Author(s):  
Agung Widyo Utomo
Keyword(s):  
Shared Memory ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Programming Model ◽  
Heuristic Method ◽  
Pairwise Alignment ◽  
Neighbor Joining ◽  
Progressive Alignment ◽  
Multiple Sequence ◽  
Progressive Multiple Sequence Alignment

Progressive multiple sequence alignment ClustalW is a widely used heuristic method for computing multiple sequence alignment (MSA). It has three stages: distance matrix computation using pairwise alignment, guide tree reconstruction using neighbor-joining and progressive alignment. To accelerate computing for large data, the progressive MSA algorithm needs to be parallelized. This research aims to identify, decompose and implement the pairwise alignment and neighbor-joining in progressive MSA using message passing, shared memory and hybrid programming model in the computer cluster. The experimental results obtained shared memory programming model as the best scenario implementation with speed up up to 12 times.

scholarly journals Progressive multiple sequence alignment with indel evolution

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Massimo Maiolo ◽  
Xiaolei Zhang ◽  
Manuel Gil ◽  
Maria Anisimova
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Sequence ◽  
Progressive Multiple Sequence Alignment

Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques

2020 ◽  
pp. 565-579 ◽  
Author(s):  
Mohamed Issa ◽  
Aboul Ella Hassanien
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Trees ◽  
Pairwise Alignment ◽  
Accurate Method ◽  
Alignment Algorithm ◽  
Bacterial Foraging Optimization ◽  
Multiple Sequence ◽  
Speed Up ◽  
Dna Fragment Assembly

Sequence alignment is a vital process in many biological applications such as Phylogenetic trees construction, DNA fragment assembly and structure/function prediction. Two kinds of alignment are pairwise alignment which align two sequences and Multiple Sequence alignment (MSA) that align sequences more than two. The accurate method of alignment is based on Dynamic Programming (DP) approach which suffering from increasing time exponentially with increasing the length and the number of the aligned sequences. Stochastic or meta-heuristics techniques speed up alignment algorithm but with near optimal alignment accuracy not as that of DP. Hence, This chapter aims to review the recent development of MSA using meta-heuristics algorithms. In addition, two recent techniques are focused in more deep: the first is Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO). The second is Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm (MO-BFO).

scholarly journals Efficient mapping of genomic sequences to optimize multiple pairwise alignment in hybrid cluster platforms

2014 ◽  
Vol 11 (3) ◽  
pp. 60-71
Author(s):  
Alberto Montañola ◽  
Concepció Roig ◽  
Porfidio Hernández
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Pairwise Alignment ◽  
Experimental Results ◽  
Genomic Sequences ◽  
Multiple Sequence ◽  
Optimal Amount ◽  
New Challenges ◽  
Best Parameters ◽  
Available Resources

Summary Multiple sequence alignment (MSA), used in biocomputing to study similarities between different genomic sequences, is known to require important memory and computation resources. Nowadays, researchers are aligning thousands of these sequences, creating new challenges in order to solve the problem using the available resources efficiently. Determining the efficient amount of resources to allocate is important to avoid waste of them, thus reducing the economical costs required in running for example a specific cloud instance. The pairwise alignment is the initial key step of the MSA problem, which will compute all pair alignments needed. We present a method to determine the optimal amount of memory and computation resources to allocate by the pairwise alignment, and we will validate it through a set of experimental results for different possible inputs. These allow us to determine the best parameters to configure the applications in order to use effectively the available resources of a given system.

MULTIPLE SEQUENCE ALIGNMENT USING AN EXHAUSTIVE AND GREEDY ALGORITHM

2005 ◽  
Vol 03 (02) ◽  
pp. 243-255 ◽  
Author(s):  
YI WANG ◽  
KUO-BIN LI
Keyword(s):  
Greedy Algorithm ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Alignment ◽  
Initial Alignment ◽  
Progressive Alignment ◽  
Multiple Sequence ◽  
Java Programming ◽  
Multiple Alignments ◽  
Objective Score

We describe an exhaustive and greedy algorithm for improving the accuracy of multiple sequence alignment. A simple progressive alignment approach is employed to provide initial alignments. The initial alignment is then iteratively optimized against an objective function. For any working alignment, the optimization involves three operations: insertions, deletions and shuffles of gaps. The optimization is exhaustive since the algorithm applies the above operations to all eligible positions of an alignment. It is also greedy since only the operation that gives the best improving objective score will be accepted. The algorithms have been implemented in the EGMA (Exhaustive and Greedy Multiple Alignment) package using Java programming language, and have been evaluated using the BAliBASE benchmark alignment database. Although EGMA is not guaranteed to produce globally optimized alignment, the tests indicate that EGMA is able to build alignments with high quality consistently, compared with other commonly used iterative and non-iterative alignment programs. It is also useful for refining multiple alignments obtained by other methods.

A New Quantum Cuckoo Search Algorithm for Multiple Sequence Alignment

2014 ◽  
Vol 23 (3) ◽  
pp. 261-275 ◽  
Author(s):  
Widad Kartous ◽  
Abdesslem Layeb ◽  
Salim Chikhi
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Initial Population ◽  
Biological Sequences ◽  
Alignment Method ◽  
Progressive Alignment ◽  
Multiple Sequence

AbstractMultiple sequence alignment (MSA) is one of the major problems that can be encountered in the bioinformatics field. MSA consists in aligning a set of biological sequences to extract the similarities between them. Unfortunately, this problem has been shown to be NP-hard. In this article, a new algorithm was proposed to deal with this problem; it is based on a quantum-inspired cuckoo search algorithm. The other feature of the proposed approach is the use of a randomized progressive alignment method based on a hybrid global/local pairwise algorithm to construct the initial population. The results obtained by this hybridization are very encouraging and show the feasibility and effectiveness of the proposed solution.

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