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.

MANGO: MULTIPLE ALIGNMENT WITH N GAPPED OLIGOS

2008 ◽  
Vol 06 (03) ◽  
pp. 521-541 ◽  
Author(s):  
ZEFENG ZHANG ◽  
HAO LIN ◽  
MING LI
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
State Of The Art ◽  
Multiple Alignment ◽  
Progressive Alignment ◽  
Multiple Sequence ◽  
New Approach ◽  
Repeat Elements ◽  
Spaced Seeds ◽  
16S Rna

Multiple sequence alignment is a classical and challenging task. The problem is NP-hard. The full dynamic programming takes too much time. The progressive alignment heuristics adopted by most state-of-the-art works suffer from the "once a gap, always a gap" phenomenon. Is there a radically new way to do multiple sequence alignment? In this paper, we introduce a novel and orthogonal multiple sequence alignment method, using both multiple optimized spaced seeds and new algorithms to handle these seeds efficiently. Our new algorithm processes information of all sequences as a whole and tries to build the alignment vertically, avoiding problems caused by the popular progressive approaches. Because the optimized spaced seeds have proved significantly more sensitive than the consecutive k-mers, the new approach promises to be more accurate and reliable. To validate our new approach, we have implemented MANGO: Multiple Alignment with N Gapped Oligos. Experiments were carried out on large 16S RNA benchmarks, showing that MANGO compares favorably, in both accuracy and speed, against state-of-the-art multiple sequence alignment methods, including ClustalW 1.83, MUSCLE 3.6, MAFFT 5.861, ProbConsRNA 1.11, Dialign 2.2.1, DIALIGN-T 0.2.1, T-Coffee 4.85, POA 2.0, and Kalign 2.0. We have further demonstrated the scalability of MANGO on very large datasets of repeat elements. MANGO can be downloaded at and is free for academic usage.

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 Protein multiple alignments: sequence-based versus structure-based programs

2019 ◽  
Vol 35 (20) ◽  
pp. 3970-3980 ◽  
Author(s):  
Mathilde Carpentier ◽  
Jacques Chomilier
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Added Value ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequence Structure ◽  
Multiple Sequence ◽  
Sequence Identity ◽  
Multiple Alignments ◽  
Low Levels

Abstract Motivation Multiple sequence alignment programs have proved to be very useful and have already been evaluated in the literature yet not alignment programs based on structure or both sequence and structure. In the present article we wish to evaluate the added value provided through considering structures. Results We compared the multiple alignments resulting from 25 programs either based on sequence, structure or both, to reference alignments deposited in five databases (BALIBASE 2 and 3, HOMSTRAD, OXBENCH and SISYPHUS). On the whole, the structure-based methods compute more reliable alignments than the sequence-based ones, and even than the sequence+structure-based programs whatever the databases. Two programs lead, MAMMOTH and MATRAS, nevertheless the performances of MUSTANG, MATT, 3DCOMB, TCOFFEE+TM_ALIGN and TCOFFEE+SAP are better for some alignments. The advantage of structure-based methods increases at low levels of sequence identity, or for residues in regular secondary structures or buried ones. Concerning gap management, sequence-based programs set less gaps than structure-based programs. Concerning the databases, the alignments of the manually built databases are more challenging for the programs. Availability and implementation All data and results presented in this study are available at: http://wwwabi.snv.jussieu.fr/people/mathilde/download/AliMulComp/. Supplementary information Supplementary data are available at Bioinformatics online.

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.

scholarly journals A Simple Genetic Algorithm for Optimizing Multiple Sequence Alignment on the Spread of the SARS Epidemic

2019 ◽  
Vol 12 (1) ◽  
pp. 30-39
Author(s):  
Siti Amiroch ◽  
M. Syaiful Pradana ◽  
M. Isa Irawan ◽  
Imam Mukhlash
Keyword(s):  
Genetic Algorithms ◽  
Phylogenetic Tree ◽  
Sequence Alignment ◽  
Dna Sequences ◽  
Multiple Sequence Alignment ◽  
Network System ◽  
Multiple Sequence ◽  
Network Analyses ◽  
Multiple Alignments ◽  
Mutation Region

Background:Multiple sequence alignment is a method of getting genomic relationships between 3 sequences or more. In multiple alignments, there are 3 mutation network analyses, namely topological network system, mutation region network and network system of mutation mode. In general, the three analyses show stable and unstable regions that map mutation regions. This area of ​​mutation is described further in a phylogenetic tree which simultaneously illustrates the path of the spread of an epidemic, the Severe Acute Respiratory Syndrome (SARS) epidemic. The process of spreading the SARS viruses, in this case, is described as the process of phylogenetic tree formation, and as a novelty of this research, multiple alignments in the process are analyzed in detail and then optimized with genetic algorithms.Methods:The data used to form the phylogenetic tree for the spread of the SARS epidemic are 14 DNA sequences which are then optimized by using genetic algorithms. The phylogenetic tree is constructed by using the neighbor-joining algorithm with a distance matrix that the intended distance is the genetic distance obtained from sequence alignment by using the Needleman Wunsch Algorithm.Results & Conclusion:The results of the analysis obtained 3649 stable areas and 19 unstable areas. The results of phylogenetic tree from the network system analysis indicated that the spread of the SARS epidemic extended from Guangzhou 16/12/02 to Zhongshan 27/12/02, then spread simultaneously to Guangzhou 18/02/03 and Guangzhou hospital. After that, the virus reached Metropole, Zhongshan, Hongkong, Singapore, Taiwan, Hong kong, and Hanoi which then continued to Guangzhou 01/01/03 and Toronto at once. The results of the mutation region network system demonstrate decomposition of orthogonal mutations in the 1st order arc.

SpliVert: A Protein Multiple Sequence Alignment Refinement Method Based on Splitting-Splicing Vertically

2020 ◽  
Vol 27 (4) ◽  
pp. 295-302 ◽  
Author(s):  
Qing Zhan ◽  
Yilei Fu ◽  
Qinghua Jiang ◽  
Bo Liu ◽  
Jiajie Peng ◽  
...  
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Middle Part ◽  
The Other ◽  
Initial Alignment ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Refinement Method ◽  
Refinement Strategy

Background: Multiple Sequence Alignment (MSA) is a fundamental task in bioinformatics and is required for many biological analysis tasks. The more accurate the alignments are, the more credible the downstream analyses. Most protein MSA algorithms realign an alignment to refine it by dividing it into two groups horizontally and then realign the two groups. However, this strategy does not consider that different regions of the sequences have different conservation; this property may lead to incorrect residue-residue or residue-gap pairs, which cannot be corrected by this strategy. Objective: In this article, our motivation is to develop a novel refinement method based on splitting- splicing vertically. Method: Here, we present a novel refinement method based on splitting-splicing vertically, called SpliVert. For an alignment, we split it vertically into 3 parts, remove the gap characters in the middle, realign the middle part alone, and splice the realigned middle parts with the other two initial pieces to obtain a refined alignment. In the realign procedure of our method, the aligner will only focus on a certain part, ignoring the disturbance of the other parts, which could help fix the incorrect pairs. Results: We tested our refinement strategy for 2 leading MSA tools on 3 standard benchmarks, according to the commonly used average SP (and TC) score. The results show that given appropriate proportions to split the initial alignment, the average scores are increased comparably or slightly after using our method. We also compared the alignments refined by our method with alignments directly refined by the original alignment tools. The results suggest that using our SpliVert method to refine alignments can also outperform direct use of the original alignment tools. Conclusion: The results reveal that splitting vertically and realigning part of the alignment is a good strategy for the refinement of protein multiple sequence alignments.

scholarly journals Multiple sequence alignment for phylogenetic purposes

2006 ◽  
Vol 19 (6) ◽  
pp. 479 ◽  
Author(s):  
David A. Morrison
Keyword(s):  
Phylogenetic Analysis ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Alignment ◽  
Evolutionary Processes ◽  
Multiple Sequence ◽  
Molecular Sequence ◽  
The Many ◽  
Tree Building ◽  
Automated Procedures

I have addressed the biological rather than bioinformatics aspects of molecular sequence alignment by covering a series of topics that have been under-valued, particularly within the context of phylogenetic analysis. First, phylogenetic analysis is only one of the many objectives of sequence alignment, and the most appropriate multiple alignment may not be the same for all of these purposes. Phylogenetic alignment thus occupies a specific place within a broader context. Second, homology assessment plays an intricate role in phylogenetic analysis, with sequence alignment consisting of primary homology assessment and tree building being secondary homology assessment. The objective of phylogenetic alignment thus distinguishes it from other sorts of alignment. Third, I summarise what is known about the serious limitations of using phenetic similarity as a criterion for automated multiple alignment, and provide an overview of what is currently being done to improve these computerised procedures. This synthesises information that is apparently not widely known among phylogeneticists. Fourth, I then consider the recent development of automated procedures for combining alignment and tree building, thus integrating primary and secondary homology assessment. Finally, I outline various strategies for increasing the biological content of sequence alignment procedures, which consists of taking into account known evolutionary processes when making alignment decisions. These procedures can be objective and repeatable, and can involve computerised algorithms to automate much of the work. Perhaps the most important suggestion is that alignment should be seen as a process where new sequences are added to a pre-existing alignment that has been manually curated by the biologist.

scholarly journals Tailor-made multiple sequence alignments using the PRALINE 2 alignment toolkit

2019 ◽  
Vol 35 (24) ◽  
pp. 5315-5317 ◽  
Author(s):  
Maurits J J Dijkstra ◽  
Atze J van der Ploeg ◽  
K Anton Feenstra ◽  
Wan J Fokkink ◽  
Sanne Abeln ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Open Source ◽  
Sequence Alignment ◽  
Open Source Software ◽  
Multiple Sequence Alignment ◽  
Multiple Alignment ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Dna Motifs ◽  
Multiple Sequence Alignments

Abstract Summary PRALINE 2 is a toolkit for custom multiple sequence alignment workflows. It can be used to incorporate sequence annotations, such as secondary structure or (DNA) motifs, into the alignment scoring, as well as to customize many other aspects of a progressive multiple alignment workflow. Availability and implementation PRALINE 2 is implemented in Python and available as open source software on GitHub: https://github.com/ibivu/PRALINE/.

Sign in / Sign up

Export Citation Format

Share Document