Solving the multiple sequence alignment problem based on simulated annealing algorithm

The multiple sequence alignment (MSA) problem has become relevant to several areas in bioinformatics from finding sequences family, detecting structural homologies of protein/DNA sequences, determining functions of protein/DNA sequences to predict patients diseases by comparing DNAs of patients in disease discovery, etc. The MSA is a NP-hard problem. In this paper, two new methods based on a cultural algorithm, namely the method of musical composition, for the solution of the MSA problem are introduced. The performance of the first and second versions were evaluated and analyzed on 26 and 12 different benchmark alignments, respectively. Test instances were taken from BAliBASE 3.0. Alignment accuracies are computed using the QSCORE program, which is a quality scoring program that compares two multiple sequence alignments. Numerical results on the tackled instances indicate that the performance levels of the proposed versions of the MMC are promising. In particular, the experimental results show that the second version found the best alignment reported in the specialized literature in 25 $$\%$$% of the tested instances. Besides, for 50 $$\%$$% of the tested instances, the second version achieved the second best alignment. Finally, the significance of the numerical results were analyzed according to the Wilcoxon rank-sum test, which indicated that the second proposed version is statistically similar to some state-of-the-art techniques for the MSA problem.

The conventional multiple sequence alignment algorithms are classi ed into two categories: iterative improvement strategies (e.g., [1]) and simulated annealing methods (e.g., [6]). Recently, a genetic algorithm has been used in computational molecular biology as a powerful combinatorial optimizer. A genetic algorithm has been applied to the problem of multiple sequence alignment on a parallel computer (e.g., [7]). In a simple genetic algorithm [3], a solution of a given problem is represented as \chromosomes which consists of bit strings of 0's and 1's. The genetic operations, such as reproduction, crossover and mutation, are applied to a population of chromosomes to create a new population of chromosomes. This process is repeated many times so that we can obtain a nearly optimal alignment. Here, we propose a improved method to apply a genetic algorithm to the problem of multiple sequence aligment. The processing was performed on a Fujitsu SPARCstation 20 and NEC UP4800.

This paper presents a novel approach for solving the Multiple Sequence Alignment (MSA) problem. K-Means clustering is combined with the Rubber Band Technique (RBT) to introduce an iterative optimization algorithm, namely RBT-Km, to find the optimal alignment for a set of input protein sequences. In this technique, the MSA problem is modeled as a Rubber Band, while the solution space is modeled as plate with several poles corresponding locations in the input sequences that are most likely to be correlated and/or biologically related. K-Means clustering is then used to discriminate biologically related locations from those that may appear by chance. RBT-Km is tested with one of the well-known benchmarks in this field (BALiBASE 2.0). The results demonstrate the superiority of the proposed technique even in the case of formidable sequences.

In this study, the effects of the scoring functions, which are used in multiple sequence alignment problem, to find the biologically meaningful alignment has been investigated. BALiBASE version 3 has been used as benchmark set. Results have been obtained by calculating score values for alignments in benchmark set and investigating whether the higher score is possible for benchmark alignment or not.

One of the most challenging tasks in bioinformatics is the resolution of Multiple Sequence Alignment (MSA) problem. It consists in comparing a set of protein or DNA sequences, in aim of predicting their structure and function. This paper introduces a new bio-inspired approach to solve such problem. This approach named BA-MSA is based on Bat Algorithm. Bat Algorithm (BA) is a recent evolutionary algorithm inspired from Bats behavior seeking their prey. The proposed approach includes new mechanism to generate initial population. It consists in generating a guide tree for each solution with progressive approach by varying some parameters. The generated guide tree will be enhanced by Hill-Climbing algorithm. In addition, to deal with the premature convergence of BA, a new restart technique is proposed to introduce more diversification when detecting premature convergence. Balibase 2.0 datasets are used for experiments. The comparison with well-known methods as MSA-GA MSA-GA (w\prealign), ClustalW, and SAGA and recent method (BBOMP) shows the effectiveness of the proposed approach.

BackgroundThe inference of homologies among DNA sequences, that is, positions in multiple genomes that share a common evolutionary origin, is a crucial, yet difficult task facing biologists. Its computational counterpart is known as the multiple sequence alignment problem. There are various criteria and methods available to perform multiple sequence alignments, and among these, the minimization of the overall cost of the alignment on a phylogenetic tree is known in combinatorial optimization as the Tree Alignment Problem. This problem typically occurs as a subproblem of the Generalized Tree Alignment Problem, which looks for the tree with the lowest alignment cost among all possible trees. This is equivalent to the Maximum Parsimony problem when the input sequences are not aligned, that is, when phylogeny and alignments are simultaneously inferred.ResultsFor large data sets, a popular heuristic is Direct Optimization (DO). DO provides a good tradeoff between speed, scalability, and competitive scores, and is implemented in the computer program POY. All other (competitive) algorithms have greater time complexities compared to DO. Here, we introduce and present experiments a new algorithm Affine-DO to accommodate the indel (alignment gap) models commonly used in phylogenetic analysis of molecular sequence data. Affine-DO has the same time complexity as DO, but is correctly suited for the affine gap edit distance. We demonstrate its performance with more than 330,000 experimental tests. These experiments show that the solutions of Affine-DO are close to the lower bound inferred from a linear programming solution. Moreover, iterating over a solution produced using Affine-DO shows little improvement.ConclusionsOur results show that Affine-DO is likely producing near-optimal solutions, with approximations within 10% for sequences with small divergence, and within 30% for random sequences, for which Affine-DO produced the worst solutions. The Affine-DO algorithm has the necessary scalability and optimality to be a significant improvement in the real-world phylogenetic analysis of sequence data.

The problem of multiple sequence alignment (MSA) in biology has been studied extensively. In this paper we offer sampling based algorithms for MSA that are more accurate than existing algorithms. The performance of our algorithms has been evaluated using the standard BaliBase dataset [10]. We are able to improve the average alignment SP score for ClustalW [9] and MAFFT [6] by 16.39% and 12.2 %, respectively and the TC score by 55.4% and 46.9%, respectively using sampling. Given that ClustalW and MAFFT are some of the most accurate algorithms for MSA currently and are widely used, our algorithms would be of interest to biologists.

Quality assessment of multiple alignment programs

The alignment of more than two biological sequences is a widely used technique in a number of areas of computational biology. However, finding an optimal alignment has been shown to be an NP-complete optimization problem. Furthermore, Multiple Sequence Alignment (MSA) can be formulated according to more than one score function, leading to multi-objective formulations of this problem. Due to these reasons, metaheuristics have been proposed to deal with MSA problems. In this paper, we present jMetalMSA, an Open Source software tool for solving MSA problems with multi-objective metaheuristics. Our motivation here is to offer to the scientific community in computational biology, a platform including state-of-the-art optimization algorithms aimed at solving different formulations of the MSA. We describe the main features of jMetalMSA, including the metaheuristics and scores that are currently available. In addition, we show a working example for illustration purposes.

Gene Clustering is one of the most popular application in the field of Bioinformatics. It is a method of grouping gene into clusters, such that each cluster must have similar gene expression levels. The two most popular population based globalized search algorithms Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for clustering gene expression data but the main drawback of these two algorithm's is that they got trapped in local optima while exploring across the problem space. So the PSO and GA has been hybridized with several other methods, to overcome the local optima problem. This paper presents a literature survey of both GA and PSO's application in Gene Clustering. PSO and GA variants and their hybridization with other metaheuristics are also described in this paper. An attempt is made to provide a guide for the researchers who are working in the area of population based gene clustering. Bioinformatics field deals with the analysis of the biological data, which stored information in the form of DNA, protein sequence in various biological databases. The Bioinformatics field involves various problem such as Gene Clustering Problem, Molecular Docking Problem, Multiple Sequence Alignment Problems, Phylogenetic tree construction , RNA Secondary Structure Prediction, Protein Secondary Structure Prediction, Fragment Assembly Problem. In Gene Clustering the gene is defined as a sequence of nucleotide bases, which carry the required information for the purpose of protein formation, which helps in the construction of the structural components. Clustering is a process of combining input patterns into groups based on the similar functionality. So, Gene clustering is a process of grouping genes in the same clusters based on similar gene expression levels. Gene expression refers to a process through which the coded information from a gene is converted into structures operating in the cell. In order to cluster the Gene expression data , various population based algorithms were used by a number of researchers. The two most popular algorithms among these are PSO (Particle Swarm Optimization) and

Multiple Sequence Alignment (MSA) is one of the most fundamental problems in computational molecular biology. The running time of the best known scheme for finding an optimal alignment, based on dynamic programming, increases exponentially with the number of input sequences. Hence, many heuristics were suggested for the problem. We consider a version of the MSA problem where the goal is to find an optimal alignment in which matches are restricted to positions in predefined matching segments. We present several techniques for making the dynamic programming algorithm more efficient, while still finding an optimal solution under these restrictions. We prove that it suffices to find an optimal alignment of the predefined sequence segments, rather than single letters, thereby reducing the input size and thus improving the running time. We also identify "shortcuts" that expedite the dynamic programming scheme. Empirical study shows that, taken together, these observations lead to an improved running time over the basic dynamic programming algorithm by 4 to 12 orders of magnitude, while still obtaining an optimal solution. Under the additional assumption that matches between segments are transitive, we further improve the running time for finding the optimal solution by restricting the search space of the dynamic programming algorithm.

The intensity modulated radiation therapy (IMRT) treatment planning problem consists of several subproblems which are typically solved sequentially. We seek to combine two of the subproblems: the beam orientation optimization (BOO) problem and the fluence map optimization (FMO) problem. The BOO problem is the problem of selecting the beam orientations to deliver radiation to the patient. The FMO problem is the problem of determining the amount of radiation intensity, or fluence, of each beamlet in each beam. The solution to the FMO problem measures the quality of a beam set, but the majority of previous BOO studies rely on heuristics and approximations to gauge the quality of the beam set. In contrast with these studies, we use an exact measure of the treatment plan quality attainable using a given beam set, which ensures convergence to a global optimum in the case of our simulated annealing algorithm and a local optimum in the case of our local search algorithm. We have also developed a new neighborhood structure that allows for faster convergence using our simulated annealing and local search algorithms, thus reducing the amount of time required to obtain a good solution. Finally, we show empirically that we can generate clinically acceptable treatment plans that require fewer beams than in current practice. This may reduce the length of treatment time, which is an important clinical consideration in IMRT.

A combined strategy of an improved simulated annealing (SA) algorithm and genetic algorithm is presented, with the goal of reducing the computational expenses. The improvements made on the SA algorithm include two parts, i.e., the adaptive regulating for the step vector, and the dynamic testing for the equilibrium of the Metropolis process. The proposed strategy has both the advantage of SA algorithm, the ability to avoid being trapped in a local optimum, and that of genetic algorithm, the ability to use the information about the searched states for the next iteration. A practical application on geometry optimization of pole shoes in large salient pole synchronous generators is effectively implemented using the strategy. The numerical results show that the number of iterations used by executing the combined strategy are only about 75% of those by executing basic SA algorithm.

Multiple sequence alignment (MSA) is a traditional and still challenging task for time-series analyses. The MSA problem is intrinsically a discrete optimization and, in principle, dynamic programming is available for solving MSA. However, the computation complexity of such algorithms increases exponentially with the number of sequences to be aligned. In this paper, we propose neural time warping (NTW) that relaxes the original MSA to a continuous optimization, in which a neural network is used to model the alignment. We show that the solution of NTW is guaranteed to be a feasible solution of the original discrete problem under mild conditions. Our experimental results suggest that NTW successfully aligns a hundred sequences and significantly outperforms existing methods for solving the MSA problem.

This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA) problem. MSA is one of the most important tasks in biological sequence analysis. Although this paper focuses on protein alignments, most of the discussion and methodology may also be applied to DNA alignments. The problem of finding the multiple alignment was investigated in the study by Bonizzoni and Vedova and Wang and Jiang, and proved to be a NP-hard (non-deterministic polynomial-time hard) problem. The presented algorithm, called Immunological Multiple Sequence Alignment Algorithm (IMSA), incorporates two new strategies to create the initial population and specific ad hoc mutation operators. It is based on the ‘weighted sum of pairs’ as objective function, to evaluate a given candidate alignment. IMSA was tested using both classical benchmarks of BAliBASE (versions 1.0, 2.0 and 3.0), and experimental results indicate that it is comparable with state-of-the-art multiple alignment algorithms, in terms of quality of alignments, weighted Sums-of-Pairs (SP) and Column Score (CS) values. The main novelty of IMSA is its ability to generate more than a single suboptimal alignment, for every MSA instance; this behaviour is due to the stochastic nature of the algorithm and of the populations evolved during the convergence process. This feature will help the decision maker to assess and select a biologically relevant multiple sequence alignment. Finally, the designed algorithm can be used as a local search procedure to properly explore promising alignments of the search space.