Multiple Sequence Alignment Problem Research Articles

Solving multiple sequence alignment problems by using a swarm intelligent optimization based approach

<span lang="EN-US">In this article, the alignment of multiple sequences is examined through swarm intelligence based an improved particle swarm optimization (PSO). A random heuristic technique for solving discrete optimization problems and realistic estimation was recently discovered in PSO. The PSO approach is a nature-inspired technique based on intelligence and swarm movement. Thus, each solution is encoded as “chromosomes” in the genetic algorithm (GA). Based on the optimization of the objective function, the fitness function is designed to maximize the suitable components of the sequence and reduce the unsuitable components of the sequence. The availability of a public benchmark data set such as the Bali base is seen as an assessment of the proposed system performance, with the potential for PSO to reveal problems in adapting to better performance. This proposed system is compared with few existing approaches such as deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) alignment (DIALIGN), PILEUP8, hidden Markov model training (HMMT), rubber band technique-genetic algorithm (RBT-GA) and ML-PIMA. In many cases, the experimental results are well implemented in the proposed system compared to other existing approaches.</span>

Exploring Reinforcement Learning Methods for Multiple Sequence Alignment: A Brief Review

Multiple sequence alignment (MSA) plays a vital role in uncovering similarities among biological sequences such as DNA, RNA, or proteins, providing valuable information about their structural, functional, and evolutionary relationships. However, MSA is a computationally challenging problem, with complexity growing exponentially as the number and length of sequences increase. Currently, standard MSA tools like ClustalW, T-Coffee, and MAFFT, which are based on heuristic algorithms, are widely used but still face many challenges due to the combinatorial explosion. Recent advancements in MSA algorithms have employed reinforcement learning (RL), particularly deep reinforcement learning (DRL), and demonstrated optimized execution time and accuracy with promising results. This is because deep reinforcement learning algorithms update their search policies using gradient descent, instead of exploring the entire solution space making it significantly faster and efficient. In this article, we provide an overview of the recent historical advancements in MSA algorithms, highlighting RL models used to tackle the MSA problem and main challenges and opportunities in this regard.

Growth Optimizer: A powerful metaheuristic algorithm for solving continuous and discrete global optimization problems

In this paper, a novel and powerful metaheuristic optimizer, named the growth optimizer (GO), is proposed. Its main design inspiration originates from the learning and reflection mechanisms of individuals in their growth processes in society. Learning is the process of individuals growing up by acquiring knowledge from the outside world. Reflection is the process of checking the individual’s own deficiencies and adjusting the individual’s learning strategies to help the individual’s growth. This work simulates this growth behavior mathematically and benchmarks the proposed algorithm on a total of 30 international test functions of the 2017 IEEE Congress on Evolutionary Computation real-parameter boundary constraint benchmark (CEC 2017 test suite). A total of 50 state-of-the-art metaheuristic algorithms participated in the comparison process. The results of the convergence accuracy comparison and the two nonparametric statistics based on the Friedman test and the Wilcoxon signed-rank test showed that GO provides competitive results compared to the 50 state-of-the-art metaheuristic algorithms tested. In addition, to verify that GO has the ability to solve different real-world optimization problems, GO was applied to two different types of real-world optimization problems: the multiple sequence alignment (MSA) problem based on the hidden Markov model (HMM) and the multithresholding image segmentation problem based on Kapur’s entropy method. GO provided more promising results than other metaheuristic techniques, especially in terms of solution quality and avoidance of local optima. The source code of the GO algorithm is publicly available at https://github.com/tsingke/Growth-Optimizer.

Correction to: Nature Inspired Algorithms for Solving Multiple Sequence Alignment Problem: A Review

Correction to: Nature Inspired Algorithms for Solving Multiple Sequence Alignment Problem: A Review

Correction to: Nature Inspired Algorithms for Solving Multiple Sequence Alignment Problem: A Review

Correction to: Nature Inspired Algorithms for Solving Multiple Sequence Alignment Problem: A Review

Nature Inspired Algorithms for Solving Multiple Sequence Alignment Problem: A Review

Nature Inspired Algorithms for Solving Multiple Sequence Alignment Problem: A Review

Brain Storm Optimization for Multiple Sequence Alignment problem

Brain Storm Optimization for Multiple Sequence Alignment problem

A Decomposition and Dominance-Based Multiobjective Artificial Bee Colony Algorithm for Multiple Sequence Alignment

The multiple sequence alignment (MSA) problem is essential in biological research for finding a specific relationship between the biologic sequences and their function. This study proposes a decomposition and dominance-based multiobjective artificial bee colony optimization algorithm for MSA (MOABC/D-MSA). MOABC/D-MSA uses three kinds of searching strategies to obtain a group of nondominated solutions with high quality and diversity of an MSA problem. A decomposition-based employed bee strategy is proposed to search for high-performance solutions of the MSA, while insuring their diversity. A nondominated sorting-based onlooker strategy searches for the solutions near the Pareto front (PF) to guide the subsequent searching. The scout bee strategy facilitates the algorithm to get out of the local optimal. A comparative experiment is implemented on BAliBASE 3.0, a benchmark for MSA algorithms. Experimental results show that the proposed algorithm has competitive performance with state-of-the-art metaheuristic algorithms. Furthermore, nondominated solutions of MOABC/D-MSA have a more uniform distribution in the objective space.

The Brain Storm Dynamic Technique DBSO-MSA for Efficiently Resolving Multiple Sequence Alignment

Multiple Sequence Alignment (MSA) is a critical step in molecular biology. Different techniques are having been proposed for obtaining optimal alignments, still, there is a need of developing accurate and efficient techniques for optimal sequence alignment. One of the efficient techniques among the swarm optimization families is Brain Storm Optimization Technique based on human social behavior has achieved success in numerous applications. However, population divergence plays a major role in obtaining better solutions for optimization problems. Therefore, high diverged populations obtain optimal results. The multiple sequences alignment is an efficient optimization for dataset analysis but hidden samples do not get tracked by MSA. Therefore DBSO_MSA model requirement is there to crossover limitations of the above model. This paper proposed a dynamic clustered and populated Brain Storm Optimization Algorithm for obtaining more optimal alignment solutions for the Multiple Sequence Alignment problem (DBSO-MSA). The dynamic nature with respect to the number of clusters and population generation at every iteration is incorporated into BSO. The number of solutions and cluster size at each iteration is controlled by the probability variable either it increases or decreases the solution space to explore more diversification in obtaining alignments for the MSA problem. The experiments show DBSO-MSA effectively improves the alignment score on the benchmark sequence datasets compared to the Classical BSO and other evolutionary algorithms.

Generic Tabu Search

The Multiple Sequence Alignment problem is considered to be an NP-Hard problem, requiring initially a specific encoding schema and design, as for any other of its siblings, to implement and run any of the main categories of heuristic. This paper intends to discuss our proposed generic implementation of the Tabu Search algorithm, a heuristic procedure proposed by Fred Glover to solve discrete combinatorial optimization problems. In this research, we try to coordinate and synchronize different designs/implementations discussed in many literatures, with some of the references mentioned in this paper. The basic idea is to avoid that the search for best solutions stops when a local optimum is found, by maintaining a list of non-acceptable or forbidden (taboo) solutions/costs, called Tabu list or Short-Term Memory (STM). In our algorithm, we attempt to add some executions tracing functionalities in order to help later analysis for initial parameters tuning. On the other hand, we propose to include the concept of a list called Long-Term Memory (LTM), so that some of the best solutions found so far can be saved, for search diversification.

Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics

The alignment operation between many protein sequences or DNA sequences related to the scientific bioinformatics application is very complex. There is a trade-off in the objectives in the existing techniques of Multiple Sequence Alignment (MSA). The techniques that concern with speed ignore accuracy, whereas techniques that concern with accuracy ignore speed. The term alignment means to get the similarity in different sequences with high accuracy. The more growing number of sequences leads to a very complex and complicated problem. Because of the emergence; rapid development; and dependence on gene sequencing, sequence alignment has become important in every biological relationship analysis process. Calculating the number of similar amino acids is the primary method for proving that there is a relationship between two sequences. The time is a main issue in any alignment technique. In this paper, a more effective MSA method for handling the massive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method depends on Artificial Fish Swarm (AFS) algorithm that can break down the most challenges of MSA problems. The AFS is exploited to obtain high accuracy in adequate time. ASF has been increasing popularly in various applications such as artificial intelligence, computer vision, machine learning, and data-intensive application. It basically mimics the behavior of fish trying to get the food in nature. The proposed mechanisms of AFS that is like preying, swarming, following, moving, and leaping help in increasing the accuracy and concerning the speed by decreasing execution time. The sense organs that aid the artificial fishes to collect information and vision from the environment help in concerning the accuracy. These features of the proposed AFS make the alignment operation more efficient and are suitable especially for large-scale data. The implementation and experimental results put the proposed AFS as a first choice in the queue of alignment compared to the well-known algorithms in multiple sequence alignment.

A Greedy Clustering Algorithm for Multiple Sequence Alignment

This paper presents a strategy to tackle the Multiple Sequence Alignment (MSA) problem, which is one of the most important tasks in the biological sequence analysis. Its role is to align the sequences in their entirety to derive relationships and common characteristics between a set of protein or nucleotide sequences. The MSA problem was proved to be an NP-Hard problem. The proposed strategy incorporates a new idea based on the well-known divide and conquer paradigm. This paper presents a novel method of clustering sequences as a preliminary step to improve the final alignment; this decomposition can be used as an optimization procedure with any MSA aligner to explore promising alignments of the search space. In their solution, authors proposed to align the clusters in a parallel and distributed way in order to benefit from parallel architectures. The strategy was tested using classical benchmarks like BAliBASE, Sabre, Prefab4 and Oxm, and the experimental results show that it gives good results by comparing to the other aligners.

Multiple sequence alignment using enhanced bird swarm align algorithm

Discovering structural, functional and evolutionary information in biological sequences have been considered as a core research area in Bioinformatics. Multiple Sequence Alignment (MSA) tries to align all sequences in a given query set to provide us ease in annotation of new sequences. Traditional methods to find the optimal alignment are computationally expensive in real time. This research presents an enhanced version of Bird Swarm Algorithm (BSA), based on bio inspired optimization. Enhanced Bird Swarm Align Algorithm (EBSAA) is proposed for multiple sequence alignment problem to determine the optimal alignment among different sequences. Twenty-one different datasets have been used in order to compare performance of EBSAA with Genetic Algorithm (GA) and Particle Swarm Align Algorithm (PSAA). The proposed technique results in better alignment as compared to GA and PSAA in most of the cases.

Harris Hawks Optimizer for Solving Multiple Sequence Alignment

In the field of bioinformatics, sequence alignment is crucial for the genes study and drug development. Multiple Sequence Alignment (MSA) is an NP-hard problem in the sequence alignment. In the last three decades, the MSA problem has drawn a great deal of interest from biologists and scientists. It is an essential method to identify the structural and behavioural attributes of biomolecules present in organisms and vital to biological processes. Nature-inspired algorithms are one of the approaches to solve the MSA problem of aligning multiple sequences at a time. They construct powerful tools to overpower traditional optimization techniques to find an accurate solution. This investigative work (HHO-MSA) brings a new nature-inspired algorithm known as the Harris Hawks Optimizer (HHO) Algorithm to solve the MSA problem. HHO’s key influence is the cooperative behaviour and hunting style of the Harris hawks in nature called the surprise pounce. The computational results across five references of the BAliBASE dataset proven the proposed HHO-MSA method’s effectiveness by reaching great results (up to 83.5% optimum solutions). Findings were statistically supported while comparing it with other established literature works to solve the MSA problem.

A Greedy Clustering Algorithm for Multiple Sequence Alignment

This paper presents a strategy to tackle the Multiple Sequence Alignment (MSA) problem, which is one of the most important tasks in the biological sequence analysis. Its role is to align the sequences in their entirety to derive relationships and common characteristics between a set of protein or nucleotide sequences. The MSA problem was proved to be an NP-Hard problem. The proposed strategy incorporates a new idea based on the well-known divide and conquer paradigm. This paper presents a novel method of clustering sequences as a preliminary step to improve the final alignment; this decomposition can be used as an optimization procedure with any MSA aligner to explore promising alignments of the search space. In their solution, authors proposed to align the clusters in a parallel and distributed way in order to benefit from parallel architectures. The strategy was tested using classical benchmarks like BAliBASE, Sabre, Prefab4 and Oxm, and the experimental results show that it gives good results by comparing to the other aligners.

The multiple alignments of very short sequences.

The multiple sequence alignment (MSA) is an increasingly important task in bioinformatics as we have to deal with the constantly increasing gene‐ and protein sequence databases. MSA is applied in phylogenetic analysis, in discovering conservative protein domains, in the assignment of secondary and tertiary structural features in proteins, or in the metagenomic sample analysis and gene discovery. Usually, the focus is on the MSA of long sequences, since in the practice these tasks appear most frequently. However, the strict analysis of the optimal MSA of short sequences is an area of negligence, and findings there may contribute to better and faster algorithms for the multiple alignment of long sequences. In the present contribution, we are examining length‐1 sequences using arbitrary metric and length‐2 sequences using unit metric, and we show that the optimum of the MSA problem can be achieved by the trivial alignment in both cases.

An effective cooperative aligner to resolve multiple-sequence alignment problem

In this research work, we propose a new cooperative aligner based on metaheuristics to find an approximate solution to the multiple-sequence alignment (MSA) problem. The developed approach named HPSOSA apply in the first stage the particle swarm optimisation (PSO) with a crossover operator as a move mechanism for each particle. In the second stage, simulated annealing is incorporated in order to improve worst solutions in the population and to help HPSOSA to escape from local optimum alignment. Simulation results on BaliBASE benchmarks have demonstrated the capability of the proposed method to obtain better results for the MSA problem compared to those produced by some literature works in the same field.

An enhanced cooperative method to solve multiple-sequence alignment problem

An enhanced cooperative method to solve multiple-sequence alignment problem

An effective cooperative aligner to resolve multiple-sequence alignment problem

An effective cooperative aligner to resolve multiple-sequence alignment problem

Exact Multiple Sequence Alignment by Synchronized Decision Diagrams

This paper develops an exact solution algorithm for the multiple sequence alignment (MSA) problem. In the first step, we design a dynamic programming model and use it to construct a novel multivalued decision diagram (MDD) representation of all pairwise sequence alignments (PSA). PSA MDDs are then synchronized using side constraints to model the MSA problem as a mixed-integer program (MIP), for the first time, in polynomial space complexity. Two bound-based filtering procedures are developed to reduce the size of the MDDs, and the resulting MIP is solved using logic-based Benders decomposition. For a more effective algorithm, we develop a two-phase solution approach. In the first phase, we use optimistic filtering to quickly obtain a near-optimal bound, which we then use for exact filtering in the second phase to prove or obtain an optimal solution. Numerical results on benchmark instances show that our algorithm solves several instances to optimality for the first time, and, in case optimality cannot be proven, considerably improves upon a state-of-the-art heuristic MSA solver. Comparison with an existing state-of-the-art exact MSA algorithm shows that our approach is more time efficient and yields significantly smaller optimality gaps.