Divide-and-conquer multiple alignment with segment-based constraints.

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.

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.

We have developed a novel method for multiple sequence alignment based on combinatorial selection of similar block candidates. Our method resembles manual multiple alignment performed by biologists. The method is more feasible for nding functional motifs than previous multiple alignment algorithms that are extensions of pairwise alignments. We employed a Hop eld neural network technique so that the method can cope with the combinatorial explosion in examining a large number of \incomplete block candidates.

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.

The reliability of multiple sequence alignment (MSA) results directly determines the credibility of the conclusions drawn from biological research. However, MSA is inherently an NP-hard problem, making it theoretically impossible to guarantee a globally optimal solution. Consequently, in addition to developing more efficient alignment algorithms, improving the quality of initial alignments through post-processing optimization has become an important strategy for enhancing the overall alignment accuracy. Although post-processing methods have shown potential in improving alignment accuracy, currently, there is a lack of systematic reviews and summaries. In this review, we provide a systematic overview of the development and key research ideas of MSA post-processing methods over the past 3 decades and outline potential directions for future research.

Multiple protein sequence alignment using double-dynamic programming

Multiple protein sequence alignment using double-dynamic programming.

Multiple alignment of biological sequences is an interesting area of research for application of parallel processing algorithms. For multiple alignment of sequences, parallelism can be introduced at different levels to reduce the overall time. This paper is concerned with the comparison of an MPI-based multiple sequence alignment algorithm by using single and multiple guide trees. In this work, we will look at the application of Divide-and-Conquer algorithm for multiple sequence alignment and discuss two different implementations on MPI. We compare the speed up of the two approaches and we contrast the quality of the alignments for these two approaches. These two approaches differ in using one single tree for alignment vs. multiple trees for alignment that we further discuss in this paper.

An algorithm is presented to compute a multiple structure alignment for a set of proteins and to generate a consensus (pseudo) protein for the set. The algorithm is a heuristic in that it computes an approximation to the optimal multiple structure alignment that minimizes the sum of the pairwise distances between the protein structures. The algorithm chooses an input protein as the initial consensus and computes a correspondence between the protein structures (which are represented as sets of unit vectors) using an approach analogous to the center-star method for multiple sequence alignment. From this correspondence, a set of rotation matrices (optimal for the given correspondence) is derived to align the structures and derive the new consensus. The process is iterated until the sum of pairwise distances converges. The computation of the optimal rotations is itself an iterative process that both makes use of the current consensus and generates simultaneously a new one. This approach is based on an interesting result that allows the sum of all pairwise distances to be represented compactly as distances to the consensus. Experimental results on several protein families are presented, showing that the algorithm converges quite rapidly.

Summary: JABAWS is a web services framework that simplifies the deployment of web services for bioinformatics. JABAWS:MSA provides services for five multiple sequence alignment (MSA) methods (Probcons, T-coffee, Muscle, Mafft and ClustalW), and is the system employed by the Jalview multiple sequence analysis workbench since version 2.6. A fully functional, easy to set up server is provided as a Virtual Appliance (VA), which can be run on most operating systems that support a virtualization environment such as VMware or Oracle VirtualBox. JABAWS is also distributed as a Web Application aRchive (WAR) and can be configured to run on a single computer and/or a cluster managed by Grid Engine, LSF or other queuing systems that support DRMAA. JABAWS:MSA provides clients full access to each application's parameters, allows administrators to specify named parameter preset combinations and execution limits for each application through simple configuration files. The JABAWS command-line client allows integration of JABAWS services into conventional scripts.Availability and Implementation: JABAWS is made freely available under the Apache 2 license and can be obtained from: http://www.compbio.dundee.ac.uk/jabaws.Contact: g.j.barton@dundee.ac.uk

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.

BackgroundIn this paper, we introduce a progressive corner cutting method called Reticular Alignment for multiple sequence alignment. Unlike previous corner-cutting methods, our approach does not define a compact part of the dynamic programming table. Instead, it defines a set of optimal and suboptimal alignments at each step during the progressive alignment. The set of alignments are represented with a network to store them and use them during the progressive alignment in an efficient way. The program contains a threshold parameter on which the size of the network depends. The larger the threshold parameter and thus the network, the deeper the search in the alignment space for better scored alignments.ResultsWe implemented the program in the Java programming language, and tested it on the BAliBASE database. Reticular Alignment can outperform ClustalW even if a very simple scoring scheme (BLOSUM62 and affine gap penalty) is implemented and merely the threshold value is increased. However, this set-up is not sufficient for outperforming other cutting-edge alignment methods. On the other hand, the reticular alignment search strategy together with sophisticated scoring schemes (for example, differentiating gap penalties for hydrophobic and hydrophylic amino acids) overcome FSA and in some accuracy measurement, even MAFFT. The program is available from http://phylogeny-cafe.elte.hu/RetAlign/ConclusionsReticular alignment is an efficient search strategy for finding accurate multiple alignments. The highest accuracy achieved when this searching strategy is combined with sophisticated scoring schemes.

T-Coffee (Tree-based consistency objective function for alignment evaluation) is a versatile multiple sequence alignment (MSA) method suitable for aligning most types of biological sequences. The main strength of T-Coffee is its ability to combine third party aligners and to integrate structural (or homology) information when building MSAs. The series of protocols presented here show how the package can be used to multiply align proteins, RNA and DNA sequences. The protein section shows how users can select the most suitable T-Coffee mode for their data set. Detailed protocols include T-Coffee, the default mode, M-Coffee, a meta version able to combine several third party aligners into one, PSI (position-specific iterated)-Coffee, the homology extended mode suitable for remote homologs and Expresso, the structure-based multiple aligner. We then also show how the T-RMSD (tree based on root mean square deviation) option can be used to produce a functionally informative structure-based clustering. RNA alignment procedures are described for using R-Coffee, a mode able to use predicted RNA secondary structures when aligning RNA sequences. DNA alignments are illustrated with Pro-Coffee, a multiple aligner specific of promoter regions. We also present some of the many reformatting utilities bundled with T-Coffee. The package is an open-source freeware available from http://www.tcoffee.org/.

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.