Multiple Alignment Of Biological Sequences Research Articles

Enhanced Genetic Method for Optimizing Multiple Sequence Alignment

In the realm of bioinformatics, Multiple Sequence Alignment (MSA) is a pivotal technique used to optimize the alignment of multiple biological sequences, guided by specific scoring criteria. Existing approaches addressing the MSA challenge tend to specialize in distinct biological features, leading to variability in alignment outcomes for the same set of sequences. Consequently, this paper proposes an enhanced evolutionary-based approach that simplifies the sequence alignment problem without considering the sequences in the non-dominated solution. Our method employs a multi-objective optimization technique that uniquely excludes non-dominated solution sets, effectively mitigating computational complexities. Utilizing the Sum of Pairs and the Total Conserved Column as primary objective functions, our approach offers a novel perspective. We adopt an integer coding approach to enhance the computational efficiency, representing chromosomes with sets of integers during the alignment process. Using the SABmark and BAliBASE datasets, extensive experimentation is conducted to compare our method with existing ones. The results affirm the superior solution quality achieved by our approach compared to its predecessors. Furthermore, via the Wilcoxon signed-rank test, a statistical analysis underscores the statistical significance of our model’s improvement (p < 0.05). This comprehensive approach holds promise for advancing Multiple Sequence Alignment in bioinformatics.

HAlign-II: efficient ultra-large multiple sequence alignment and phylogenetic tree reconstruction with distributed and parallel computing

BackgroundMultiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction. Extreme increase in next-generation sequencing results in shortage of efficient ultra-large biological sequence alignment approaches for coping with different sequence types.MethodsDistributed and parallel computing represents a crucial technique for accelerating ultra-large (e.g. files more than 1 GB) sequence analyses. Based on HAlign and Spark distributed computing system, we implement a highly cost-efficient and time-efficient HAlign-II tool to address ultra-large multiple biological sequence alignment and phylogenetic tree construction.ResultsThe experiments in the DNA and protein large scale data sets, which are more than 1GB files, showed that HAlign II could save time and space. It outperformed the current software tools. HAlign-II can efficiently carry out MSA and construct phylogenetic trees with ultra-large numbers of biological sequences. HAlign-II shows extremely high memory efficiency and scales well with increases in computing resource.ConclusionsTHAlign-II provides a user-friendly web server based on our distributed computing infrastructure. HAlign-II with open-source codes and datasets was established at http://lab.malab.cn/soft/halign.

Editorial: special issue on parallel nature-inspired optimization

Nature-inspired optimization techniques have proven to efficiently solve a wide range of problems related to parallel computing and more generally to computer science. In addition, the intrinsic parallelization and distribution capabilities of nature-inspired techniques can been exploited in order to provide powerful optimization solutions. However many research challenges remain to be addressed, such as the design and implementation of efficient nature-inspired optimization algorithms for massively parallel and distributed architectures and their application to real-world problems. This special issue is composed of extended versions of selected best papers presented at the 14th International Workshop on Nature Inspired Distributed Computing (NIDISC 2011). These articles provide a good theoretical and practical overview of nature-inspired optimization techniques and their application to metaheuristics and parallel/distributed computing. In the following, we propose a brief overview of the different contributions included in this special issue. In the first paper by E.A. Macedo et al., “Multiple Biological Sequence Alignment in Heterogeneous Multicore Clusters with User-Selectable Task Allocation Policies” a parallel version of a heuristic iterative algorithm, DIALIGN-TX, is proposed to tackle the Multiple Sequence Alignment (MSA) problem, a well-known NP-Hard bioinformatics problem. The authors empirically demonstrate the execution time reduction obtained with the proposed parallel implementation as well as the impact of the choice of the allocation policy. The second paper by P. Korosec et al., “Multi-core Implementation of the Differential Ant-Stigmergy Algorithm,” also proposes to parallelize a state-of-the-art optimization algorithm, the Differential Ant-Stigmergy algorithm (DASA) which is a

Multiple biological sequence alignment in heterogeneous multicore clusters with user-selectable task allocation policies

Multiple Sequence Alignment (MSA) is an important problem in Bioinformatics that aims to align more than two sequences in order to emphasize similarity regions. This problem is known to be NP-Hard, so heuristic methods are used to solve it. DIALIGN-TX is an iterative heuristic method for MSA that generates alignments by concatenating ungapped regions with high similarity. Usually, the first phase of MSA algorithms is parallelized by distributing several independent tasks among the nodes. Even though heterogeneous multicore clusters are becoming very common nowadays, very few task allocation policies were proposed for this type of architecture. This paper proposes an MPI/OpenMP master/slave parallel strategy to run DIALIGN-TX in heterogeneous multicore clusters, with several allocation policies. We show that an appropriate choice of the master node has great impact on the overall system performance. Also, the results obtained in a heterogeneous multicore cluster composed of 4 nodes (30 cores), with real sequence sets show that the execution time can be drastically reduced when the appropriate allocation policy is used.

PicXAA-Web: a web-based platform for non-progressive maximum expected accuracy alignment of multiple biological sequences

In this article, we introduce PicXAA-Web, a web-based platform for accurate probabilistic alignment of multiple biological sequences. The core of PicXAA-Web consists of PicXAA, a multiple protein/DNA sequence alignment algorithm, and PicXAA-R, an extension of PicXAA for structural alignment of RNA sequences. Both PicXAA and PicXAA-R are probabilistic non-progressive alignment algorithms that aim to find the optimal alignment of multiple biological sequences by maximizing the expected accuracy. PicXAA and PicXAA-R greedily build up the alignment from sequence regions with high local similarity, thereby yielding an accurate global alignment that effectively captures local similarities among sequences. PicXAA-Web integrates these two algorithms in a user-friendly web platform for accurate alignment and analysis of multiple protein, DNA and RNA sequences. PicXAA-Web can be freely accessed at http://gsp.tamu.edu/picxaa/.

Fast Statistical Alignment

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment—previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches—yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/.

A domain decomposition strategy for alignment of multiple biological sequences on multiprocessor platforms

Multiple Sequences Alignment (MSA) of biological sequences is a fundamental problem in computational biology due to its critical significance in wide ranging applications including haplotype reconstruction, sequence homology, phylogenetic analysis, and prediction of evolutionary origins. The MSA problem is considered NP-hard and known heuristics for the problem do not scale well with increasing numbers of sequences. On the other hand, with the advent of a new breed of fast sequencing techniques it is now possible to generate thousands of sequences very quickly. For rapid sequence analysis, it is therefore desirable to develop fast MSA algorithms that scale well with an increase in the dataset size. In this paper, we present a novel domain decomposition based technique to solve the MSA problem on multiprocessing platforms. The domain decomposition based technique, in addition to yielding better quality, gives enormous advantages in terms of execution time and memory requirements. The proposed strategy allows one to decrease the time complexity of any known heuristic of O ( N ) x complexity by a factor of O ( 1 / p ) x , where N is the number of sequences, x depends on the underlying heuristic approach, and p is the number of processing nodes. In particular, we propose a highly scalable algorithm, Sample-Align-D, for aligning biological sequences using Muscle system as the underlying heuristic. The proposed algorithm has been implemented on a cluster of workstations using the MPI library. Experimental results for different problem sizes are analyzed in terms of quality of alignment, execution time and speed-up.

Hardness Results on Local Multiple Alignment of Biological Sequences

This paper studies the local multiple alignment problem, which is, given protein or DNA sequences, to locate a region (i.e., a substring) of fixed length from each sequence so that the score determined from the set of regions is optimized. We consider the following scoring schemes: the relative entropy score (i.e., average information content), the sum-of-pairs score and a relative entropy-like score introduced by Li, et al. We prove that multiple local alignment is NP-hard under each of these scoring schemes. In particular, we prove that multiple local alignment is APX-hard under relative entropy scoring. It implies that unless P =NP there is no polynomial time algorithm whose worst case approximation error can be arbitrarily specified(precisely, a polynomial time approximation scheme). Several related theoretical results are also provided.

Hardness Results on Local Multiple Alignment of Biological Sequences

Hardness Results on Local Multiple Alignment of Biological Sequences

A novel method for multiple alignment of sequences with repeated and shuffled elements

We describe ABA (A-Bruijn alignment), a new method for multiple alignment of biological sequences. The major difference between ABA and existing multiple alignment methods is that ABA represents an alignment as a directed graph, possibly containing cycles. This representation provides more flexibility than does a traditional alignment matrix or the recently introduced partial order alignment (POA) graph by allowing a larger class of evolutionary relationships between the aligned sequences. Our graph representation is particularly well-suited to the alignment of protein sequences with shuffled and/or repeated domain structure, and allows one to construct multiple alignments of proteins containing (1) domains that are not present in all proteins, (2) domains that are present in different orders in different proteins, and (3) domains that are present in multiple copies in some proteins. In addition, ABA is useful in the alignment of genomic sequences that contain duplications and inversions. We provide several examples illustrating the applications of ABA.

Fast and Sound Two-Step Algorithms for Multiple Alignment of Nucleic Sequences

We report here on our work on multiple alignment of biological sequences. The observation of actual alignments has lead us to formulate heuristics from which we have derived new and efficient algorithms. These two algorithms are very fast and give reasonably good results. They work in two steps: blocks are found in the sequences, and then the sequences are aligned between the blocks. Strong and sound hypotheses made at every step, as well as a new representation of blocks, yield high efficiency of the resulting algorithms.

Parametric Alignment of Multiple Biological Sequences

Parametric Alignment of Multiple Biological Sequences

A novel method of multiple alignment of biopolymer sequences

A novel algorithm for multiple alignment of biological sequences is suggested. At the first step the DotHelix procedure is employed for construction of motifs, i.e. continuous fragments of local similarity of various “thickness” and strength, and then these motifs are concatenated into chains consistent with the order of letters in the sequences. The algorithm is implemented in the MA-Tools program of the GeneBee package. An example illustrating the effectivity of the algorithm is presented.

‘Size leap’ algorithm: an efficient extraction of the longest common motifs from a molecular sequence set. Application to the DNA sequence reconstruction

We propose a new method, called 'size leap' algorithm, of search for motifs of maximum size and common to two fragments at least. It allows the creation of a reduced database of motifs from a set of sequences whose size obeys the series of Fibonacci numbers. The convenience lies in the efficiency of the motif extraction. It can be applied in the establishment of overlap regions for DNA sequence reconstruction and multiple alignment of biological sequences. The method of complete DNA sequence reconstruction by extraction of the longest motifs ('anchor motifs') is presented as an application of the size leap algorithm. The details of a reconstruction from three sequenced fragments are given as an example.

Trees, Stars, and Multiple Biological Sequence Alignment

One important problem in biological sequence comparison is how to simultaneously align several nucleic acid or protein sequences. A multiple alignment avoids possible inconsistencies among several pairwise alignments and can elucidate relationships not evident from pairwise comparisons. The basic dynamic programming algorithm for optimal multiple sequence alignment requires too much time to be practical for more than three sequences, the length of an average protein. Recently, Carrillo and Lipman (SIAMJ. Appl. Math., 48 (1988), pp. 1073–1082) have rendered feasible the optimal simultaneous alignment of as many as six sequences by showing that a consideration of minimal pairwise alignment costs can vastly decrease the number of cells a dynamic programming algorithm need consider. Their argument, however, requires the cost of a multiple alignment to be a weighted sum of the costs of its projected pairwise alignments.This paper presents an extension of Carrillo and Lipman's algorithm to the definition of mul...