Sampling Based Meta-algorithms for Accurate Multiple Sequence Alignment

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.

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.

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.

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 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.

Background: Due to the growing amount of biological data, it is often necessary to select the most optimal estimation method for DNA sequence alignment across livestock species. One of the most important benches of genomics is to modelling homology between considered DNA sequences. A multiple sequence alignment is a potent tool for molecular and evolutionary biology, and there are several programs and algorithms applicable for this purpose. The purpose of this paper was to study the most commonly used DNA alignment algorithms to select the optimal tool dedicated for short sequences.Methods: Four steps of bioinformatics pipelines were considered to benchmark the algorithms for multiple DNA sequence alignment across livestock species: 1) selection of reference genome sequences of ARS1.2 for cattle, EquCab3.0 for horse and vicPac2 for alpaca with a low E-value using TBLASTn 2) removing gaps for these sequences 3) alignment of obtained sequences using examined algorithms 4) matching the quality of aligned sequences with sequences of reference genomes by more software. The time of computation was archived for the whole analysis. The seven programs were utilized, each based on different alignment algorithms, namely: ClustalO, ClustalW, Kalign, MAFFT, MUSCLE, Probcons and T-Coffee.Results: The result obtained in this study showed that the fastest is progressive algorithms such as Kalign or MUSCLE-FAST. Moreover, the iterative algorithms like MAFFT and MUSCLE revealed a higher quality of the alignment. The T-Coffee and Probcons programs were computational cost-effective; simultaneously, they were generating a medium-quality calculation in a relatively long time. The best quality of alignment was shown by iterative variants of the MAFFT program; however, the speed of the calculations was relatively low. The fastest algorithm was Kalign, making alignment much faster than the competitors, but achieving average results in the quality of the alignment. The average speed ratio concerning the quality of the analyzed algorithms was obtained by the progressive version of MAFFT, NS1.Conclusions: We conclude that the results of this study can be used to re-alignment of variant primers in new livestock genome releases.

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.

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.

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.

A large number of methods for multiple sequence alignment are currently available. Recent benchmarking tests demonstrated that strengths and drawbacks of these methods differ substantially. Global strategies can be outperformed by approaches based on local similarities and vice versa, depending on the characteristics of the input sequences. In recent years, mixed approaches that include both global and local features have shown promising results. Herein, we introduce a new algorithm for multiple sequence alignment that integrates the global divide-and-conquer approach with the local segment-based approach, thereby combining the strengths of those two strategies.

We consider a branch-and-cut approach for solving the multiple sequence alignment problem, which is a central problem in computational biology. We propose a general model for this problem in which arbitrary gap costs are allowed. An interesting aspect of our approach is that the three (exponentially large) classes of natural valid inequalities that we consider turn out to be both facet-defining for the convex hull of integer solutions and separable in polynomial time. Both the proofs that these classes of valid inequalities are facet-defining and the description of the separation algorithms are far from trivial. Experimental results on several benchmark instances show that our method outperforms the best tools developed so far, in that it produces alignments that are better from a biological point of view. A noteworthy outcome of the results is the effectiveness of using branch-and-cut with only a carefully-selected subset of the variables as a heuristic.

This chapter describes the Multiple Carbohydrate Alignment with Weights (MCAW) tool, which is available as a part of the RINGS (Resource for INformatics of Glycomes at Soka) website. It implements a combination of KCaM (Aoki, Yamaguchi, Ueda, et al., Nucl Acids Res 32:W267-W272, 2004), a pairwise glycan alignment algorithm, and ClustalW (Thompson, Higgins, Gibson, Nucleic Acids Res 22:4673-80, 1994), a weighted multiple protein sequence alignment algorithm. This tool computes the multiple glycan alignment by first computing a guide tree to determine the order by which to progressively add glycans to the multiple alignment. The dynamic programming algorithm results in a glycan profile of the alignment glycans, containing "monosaccharide positions" indicating the ratio of monosaccharides and their glycosidic bonds that are aligned at the corresponding position. This tool has been used to analyze databases of glycan array experimental data, incorporating weights to reflect the biological significance of certain glycans over others. As a result, it has been shown that the alignments obtained are biologically relevant, matching the results as found in the literature.

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.

Constrained sequence alignment has been proposed as a way for incorporating biologists' knowledge about common structures or functions into the alignment process. For alignment of protein sequences, several studies have suggested taking into account the motifs (a restricted regular expression) from the PROSITE database to guide alignments. The regular expression constrained sequence alignment has been introduced for this purpose. An alignment satisfies the constraint if part of it matches a given regular expression in each dimension (i.e. in each sequence aligned). There is a method that rewards the alignments that include a region matching the given regular expression. This method does not always guarantee the satisfaction of the constraint. Another method constructs a weighted finite automaton from the given regular expression, and presents a dynamic programming solution that simulates copies of this automaton to find an alignment with maximum score satisfying the regular expression constraint. We propose a new algorithm for the regular expression constrained multiple sequence alignment problem. Our algorithm considers two layers each of which corresponds to part of the dynamic programming matrix for the alignment of the given sequences. We compute each layer differently using dynamic programming. We propose the following modification in the definition of the problem: the region satisfying the constraint does not contribute to the total score. This modification is not necessary for the correctness and the performance in certain cases such as the constraint involves only one motif or motif-matching regions span short distance in each sequence but we believe that with this modification we achieve the same goal by doing less work in practice. Our algorithm is much more efficient than a previously proposed algorithm that uses weighted automata, and its performance in practice is comparable to (and under certain conditions even better than) that of the ordinary (unconstrained) multiple sequence alignment algorithm. Our experiments on real biological sequences, and regular expressions each composed of a sequence of motifs verify this

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.