Multiple sequence alignment by parallel simulated annealing

Ishikawa, Masato; Toya, Tomoyuki; Hoshida, Masaki; Nitta, Katsumi; Ogiwara, Atushi; Kanehisa, Minoru

doi:10.1093/bioinformatics/9.3.267

Cited by 40 publications

(26 citation statements)

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“…These include tabu search [200], Monte Carlo optimizaton [201], methods based on genetic algorithms [202], relaxation methods [203], simulated annealing [204], iterative algorithms [205] and parallel simulated annealing [206].…”

Section: Optimization In Bioinformaticsmentioning

confidence: 99%

Machine Learning in Bioinformatics

Mohammed¹,

Mohammed²

2008

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Section: Optimization In Bioinformaticsmentioning

confidence: 99%

Machine Learning in Bioinformatics

Mohammed¹,

Mohammed²

2008

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“…Another algorithm is the (SW) Algorithm, which applies more sensitive approach to the alignment of strings with different lengths [22]. Simulated Annealing (SA) was one of the first heuristics applied to sequence alignment [23][24][25], over time, GA and many variants of it have been applied to the sequence alignment problem. Shyu et al reviewed the strengths and disadvantage of their recent application for sequence alignment using evolutionary computation [26], which combined ant colony optimization (ACO) with GA to overcome the problem of becoming trapped in local optimum.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Self-Organizing Map Neural Network for DNA Sequence Classification

Mohamed¹,

Al-Mehdhar²,

Bamatraf³

et al. 2013

IIM

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The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, powerful, distributed, fault tolerant computing and capability to learn in a data-rich environment. ANNs has been used in several fields, showing high performance as classifiers. The problem of dealing with non numerical data is one major obstacle prevents using them with various data sets and several domains. Another problem is their complex structure and how hands to interprets. Self-Organizing Map (SOM) is type of neural systems that can be easily interpreted, but still can't be used with non numerical data directly. This paper presents an enhanced SOM structure to cope with non numerical data. It used DNA sequences as the training dataset. Results show very good performance compared to other classifiers. For better evaluation both micro-array structure and their sequential representation as proteins were targeted as dataset accuracy is measured accordingly.

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“…Other examples for alignment approaches include the use of distributed architectures (Ebedes and Datta, 2004;Strumpen, 1995), simulated annealing (Ishikawa et al, 1993;Zola et al, 2006), or Markov chain decomposition (Bhandarkar et al, 1998;Keibler et al, 2007). (Strumpen, 1995) presented a geographically distributed parallel computing approach over the internet (MIMD) which improves the biological sequence analysis.…”

Section: Introductionmentioning

confidence: 99%

“…It is described a parallel implementation of the method that utilizes simulated annealing metaheuristic to find locally optimal phylogenetic trees in reasonable time. In a different approach (Ishikawa et al, 1993) used simulated annealing heuristics in a parallel system to solve the problem of multiple sequence alignment. Basically, they solve the problem of multiple sequence alignments calculating the annealing temperature in parallel, in order to improve the simulated annealing algorithm result at a reasonable execution time.…”

Section: Introductionmentioning

confidence: 99%

Using Threads to Overcome Synchronization Delays in Parallel Multiple Progressive Alignment Algorithms

Adnan¹

2012

American Journal of Bioinformatics

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Problem statement:The parallelization of multiple progressive alignment algorithms is a difficult task. All known methods have strong bottlenecks resulting from synchronization delays. This is even more constraining in distributed memory systems, where message passing also delays the interprocess communication. Despite these drawbacks, parallel computing is becoming increasingly necessary to perform multiple sequence alignment. Approach: In this study, it is introduced a solution for parallelizing multiple progressive alignments in distributed memory systems that overcomes such delays. Results: The proposed approach uses threads to separate actual alignment from synchronization and communication. It also uses a different approach to schedule independent tasks. Conclusion/Recommendations: The approach was intensively tested, producing a performance remarkably better than a largely used algorithm. It is suggested that it can be applied to improve the performance of some multiple alignment tools, as CLUSTALW and MUSCLE.

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Multiple sequence alignment by parallel simulated annealing

Cited by 40 publications

References 0 publications

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics

Enhanced Self-Organizing Map Neural Network for DNA Sequence Classification

Using Threads to Overcome Synchronization Delays in Parallel Multiple Progressive Alignment Algorithms

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