A Multi-objective Optimization Framework for Multiple Sequence Alignment with Metaheuristics

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.

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.

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 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.

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.

With the development of new sequencing techniques, whole genomes of many species have become available. This huge amount of data gives rise to new opportunities and challenges. These new sequences provide valuable information on relationships among species, e.g. genome recombination and conservation. One of the principal ways to investigate such information is multiple sequence alignment (MSA). Currently, there is large amount of MSA data on the internet, such as the UCSC genome database, but how to effectively use this information to solve classical and new problems is still an area lacking of exploration. In this thesis, we explored how to use this information in four problems, i.e. sequence orthology search problem, multiple alignment improvement problem, short read mapping problem, and genome rearrangement inference problem. For the first problem, we developed a EM algorithm to iteratively align a query with a multiple alignment database with the information from a phylogeny relating the query species and the species in the multiple alignment. We also infer the query's location in the phylogeny. We showed that by doing alignment and phylogeny inference together, we can improve the accuracies for both problems. For the second problem, we developed an optimization algorithm to iteratively refine the multiple alignment quality. Experiment results showed our algorithm is very stable in term of resulting alignments. The results showed that our method is more accurate than existing methods, i.e. Mafft, Clustal-O, and Mavid, on test data from three sets of species from the UCSC genome database. For the third problem, we developed a model, PhyMap, to align a read to a multiple alignment allowing mismatches and indels. PhyMap computes local alignments of a query sequence against a fixed multiple-genome alignment of closely related species. PhyMap uses a known phylogenetic tree on the species in the multiple alignment to improve the quality of its computed alignments while also estimating the placement of the query on this tree. Both theoretical computation and experiment results show that our model can differentiate between orthologous and paralogous alignments better than other popular short read mapping tools (BWA, BOWTIE and BLAST). For the fourth problem, we gave a simple genome recombination model which can express insertions, deletions, inversions, translocations and inverted translocations on aligned genome segments. We also developed an MCMC algorithm to infer the order of the query segments. We proved that using any Euclidian metrics to measure distance between two sequence orders in the tree optimization goal function will lead to a degenerated solution where the inferred order will be the order of one of the leaf nodes. We also gave a graph-based formulation of the problem which can represent the probability distribution of the order of the query sequences.

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.

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.

Multiple Sequence Alignment (MSA) is a fundamental problem in computational biology that is used to understand the evolutionary history of protein, DNA, or RNA sequences. An optimal alignment for two sequences can efficiently be found using dynamic programming, but computing optimal alignments for more sequences continues to be a hard problem. A common method to solve MSA problems is A* search with admissible heuristics, computed from subsets of the input sequences. In this paper, we consider MSA from the perspective of cost partitioning and relate the existing heuristics for MSA to uniform cost partitioning and post-hoc optimization, two well-known techniques from the automated planning literature. We show that the MSA heuristics are bounded by uniform cost partitioning and that post-hoc optimization yields strictly dominating heuristics. For a common benchmark set of protein sequences and a set of DNA sequences, we show that the theoretical dominance relations between the heuristics carry over to practical instances.

The multiple sequence alignment problem (MSAP) is one of the most difficult problems in computational molecular biology. In this paper, we describe the optimization model and the neighborhood structure on the MSAP, then propose a scheme to solve the MSAP using Simulated Annealing Algorithm. Experiment shows that the scheme is effcient.

Multiple sequence alignment (MSA) is an optimization problem consisting in finding the best alignment of more than two biological sequences according to a number of scores or objectives. In this paper, we consider a three-objective formulation of MSA, which includes the STRIKE score, the percentage of aligned columns, and the percentage of non-gap symbols. The two last objectives introduce many plateaus in the search space, thus increasing the complexity of the problem. By taking as benchmark the BAliBASE data set, we carry out a rigorous comparative study by using four multi-objective metaheuristics, including the classical NSGA-II evolutionary algorithm and the more recent ones MOCell, GWASF-GA, and NSGA-III. Our study concludes that NSGA-II provides the best overall performance.

BackgroundStructural alignment of RNAs is becoming important, since the discovery of functional non-coding RNAs (ncRNAs). Recent studies, mainly based on various approximations of the Sankoff algorithm, have resulted in considerable improvement in the accuracy of pairwise structural alignment. In contrast, for the cases with more than two sequences, the practical merit of structural alignment remains unclear as compared to traditional sequence-based methods, although the importance of multiple structural alignment is widely recognized.ResultsWe took a different approach from a straightforward extension of the Sankoff algorithm to the multiple alignments from the viewpoints of accuracy and time complexity. As a new option of the MAFFT alignment program, we developed a multiple RNA alignment framework, X-INS-i, which builds a multiple alignment with an iterative method incorporating structural information through two components: (1) pairwise structural alignments by an external pairwise alignment method such as SCARNA or LaRA and (2) a new objective function, Four-way Consistency, derived from the base-pairing probability of every sub-aligned group at every multiple alignment stage.ConclusionThe BRAliBASE benchmark showed that X-INS-i outperforms other methods currently available in the sum-of-pairs score (SPS) criterion. As a basis for predicting common secondary structure, the accuracy of the present method is comparable to or rather higher than those of the current leading methods such as RNA Sampler. The X-INS-i framework can be used for building a multiple RNA alignment from any combination of algorithms for pairwise RNA alignment and base-pairing probability. The source code is available at the webpage found in the Availability and requirements section.

Multiple sequence alignment involves alignment of more than two sequences and is an NP-complete problem. Therefore, heuristic algorithms that use different criteria to find an approximation to the optimal solution are employed. At the heart of these approaches lie the scoring and objective functions that a given algorithm uses to compare competing solutions in constructing a multiple sequence alignment. These objective functions are often motivated by the biological paradigms that govern functional similarities and evolutionary relations. Most existing approaches utilize a progressive process where the final alignment is constructed sequentially by adding new sequences into an existing multiple sequence alignment matrix, which is dynamically updated. In doing this, the core scoring function to assess accuracies of pairwise alignments generally remains the same, while the objective functions used in intermediary steps differ. Nevertheless, the overall assessment of the final multiple sequence alignment is generally calculated by an extension of pairwise scorings. In this chapter, we explore different scoring and objective functions used in calculating the accuracy and optimization of a multiple sequence alignment and provide utilization of these criteria in popularly used multiple sequence alignment algorithms.

Multiple sequence alignment (MSA) methods infer the homologous regions within DNA and protein sequences. Among the other algorithms for MSA, the center star method (CSM) is useful when processing a large number of sequences. In this method, the partially conserved regions among the non-center sequences tend to hide in the alignment. Therefore, existence of subsets of similar sequences in the input set leads to a significant accuracy loss. As a solution to the above problem, this research introduces an algorithm for MSA based on a solution proposed by D. Gusfield in 1991. In this research, the sequences are first grouped into subsets, aligned separately using CSM and finally these alignments are merged by applying progressive alignment procedure. The detailed solution for this problem is presented in the following section.

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.