Sequence Alignment Problem Research Articles

Alignment-free phylogenetic reconstruction: Sample complexity via a branching process analysis

We present an efficient phylogenetic reconstruction algorithm allowing insertions and deletions which provably achieves a sequence-length requirement (or sample complexity) growing polynomially in the number of taxa. Our algorithm is distance-based, that is, it relies on pairwise sequence comparisons. More importantly, our approach largely bypasses the difficult problem of multiple sequence alignment.

Implementing Hierarchical Clustering Method for Multiple Sequence Alignment and Phylogenetic Tree Construction

In the field of proteomics because of more data is added, the computational methods need to be more efficient. The part of molecular sequences is functionally more important to the molecule wis more resistant to change. To ensure the reliability of sequence alignment, comparative approaches are used. The problem of multiple sequence alignment is a proposition of evolutionary history. For each column in the alignment, the explicit homologous correspondence of each individual sequence position is established. The different pair-wise sequence alignment methods are elaborated in the present work. But these methods are only used for aligning the limited number of sequences having small sequencelength. For aligning sequences based on the local alignment with consensus sequences, a new method is introduced. From NCBI databank triticum wheat varieties are loaded. Phylogenetic trees are constructed for divided parts of dataset. A single new tree isconstructed from previous generated trees using advanced pruning technique. Then, the closely related sequences are extracted by applying threshold conditions and by using shift operations in the both directions optimal sequence alignment is obtained.

Improvements on bicriteria pairwise sequence alignment: algorithms and applications

In this article, we consider the bicriteria pairwise sequence alignment problem and propose extensions of dynamic programming algorithms for several problem variants with a novel pruning technique that efficiently reduces the number of states to be processed. Moreover, we present a method for the construction of phylogenetic trees based on this bicriteria framework. Two exemplary cases are discussed. Numerical results on a real dataset show that this approach is very fast in practice. The pruning technique saves up to 90% in memory usage and 80% in CPU time. Based on this method, phylogenetic trees are constructed from real-life data. In addition of providing complementary information, some of these trees match those obtained by the Maximum Likelihood method. Source code is freely available for download at URL http://eden.dei.uc.pt/paquete/MOSAL, implemented in C and supported on Linux, MAC OS and MS Windows.

A data parallel strategy for aligning multiple biological sequences on multi-core computers

In this paper, we address the large-scale biological sequence alignment problem, which has an increasing demand in computational biology. We employ data parallelism paradigm that is suitable for handling large-scale processing on multi-core computers to achieve a high degree of parallelism. Using the data parallelism paradigm, we propose a general strategy which can be used to speed up any multiple sequence alignment method. We applied five different clustering algorithms in our strategy and implemented rigorous tests on an 8-core computer using four traditional benchmarks and artificially generated sequences. The results show that our multi-core-based implementations can achieve up to 151-fold improvements in execution time while losing 2.19% accuracy on average. The source code of the proposed strategy, together with the test sets used in our analysis, is available on request.

Open-Phylo: a customizable crowd-computing platform for multiple sequence alignment

Citizen science games such as Galaxy Zoo, Foldit, and Phylo aim to harness the intelligence and processing power generated by crowds of online gamers to solve scientific problems. However, the selection of the data to be analyzed through these games is under the exclusive control of the game designers, and so are the results produced by gamers. Here, we introduce Open-Phylo, a freely accessible crowd-computing platform that enables any scientist to enter our system and use crowds of gamers to assist computer programs in solving one of the most fundamental problems in genomics: the multiple sequence alignment problem.

Analysis of Population Based Metaheuristic Used for Gene Clustering

Gene Clustering is one of the most popular application in the field of Bioinformatics. It is a method of grouping gene into clusters, such that each cluster must have similar gene expression levels. The two most popular population based globalized search algorithms Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for clustering gene expression data but the main drawback of these two algorithm's is that they got trapped in local optima while exploring across the problem space. So the PSO and GA has been hybridized with several other methods, to overcome the local optima problem. This paper presents a literature survey of both GA and PSO's application in Gene Clustering. PSO and GA variants and their hybridization with other metaheuristics are also described in this paper. An attempt is made to provide a guide for the researchers who are working in the area of population based gene clustering. Bioinformatics field deals with the analysis of the biological data, which stored information in the form of DNA, protein sequence in various biological databases. The Bioinformatics field involves various problem such as Gene Clustering Problem, Molecular Docking Problem, Multiple Sequence Alignment Problems, Phylogenetic tree construction , RNA Secondary Structure Prediction, Protein Secondary Structure Prediction, Fragment Assembly Problem. In Gene Clustering the gene is defined as a sequence of nucleotide bases, which carry the required information for the purpose of protein formation, which helps in the construction of the structural components. Clustering is a process of combining input patterns into groups based on the similar functionality. So, Gene clustering is a process of grouping genes in the same clusters based on similar gene expression levels. Gene expression refers to a process through which the coded information from a gene is converted into structures operating in the cell. In order to cluster the Gene expression data , various population based algorithms were used by a number of researchers. The two most popular algorithms among these are PSO (Particle Swarm Optimization) and

The tree alignment problem.

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.

Simulated Annealing with Previous Solutions Applied to DNA Sequence Alignment

A new algorithm for solving sequence alignment problem is proposed, which is named SAPS (Simulated Annealing with Previous Solutions). This algorithm is based on the classical Simulated Annealing (SA). SAPS is implemented in order to obtain results of pair and multiple sequence alignment. SA is a simulation of heating and cooling of a metal to solve an optimization problem. In order to select randomly a current solution, SAPS algorithm chooses a solution from solutions that have been previously generated within the Metropolis Cycle. This simple change has led to increase the quality of the solution to the problem of aligning genomic sequences with respect to the classical Simulated Annealing algorithm. The parameters of SAPS, for certain instances, are tuned by an analytical method, and some parameters have experimentally been tuned. SAPS has generated high-quality results in comparison with the classical SA. The instances used are specific genes of the AIDS virus.

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

Global alignment of molecular sequences via ancestral state reconstruction

We consider the trace reconstruction problem on a tree (TRPT): a binary sequence is broadcast through a tree channel where we allow substitutions, deletions, and insertions; we seek to reconstruct the original sequence from the sequences received at the leaves. The TRPT is motivated by the multiple sequence alignment problem in computational biology. We give a simple recursive procedure giving strong reconstruction guarantees at low mutation rates. To our knowledge, this is the first rigorous trace reconstruction result on a tree in the presence of indels.

A PARALLEL ALGORITHM FOR THE CONSTRAINED MULTIPLE SEQUENCE ALIGNMENT PROBLEM DESIGNED FOR GPUs

Multiple sequence alignment (MSA) is one of the most important problems in computational biology. As availability of genomic and proteomic data constantly increases, new tools for processing this data in reasonable time are needed. One method of addressing this issue is parallelization. Nowadays, graphical processing units offer much more computational power than central processors, hence GPUs become more and more popular in computational-intensive tasks, including sequence alignment. We investigate the constrained multiple sequence alignment problem (CMSA) which allows some prior knowledge to be introduced to a final alignment. As a result we propose a GPU-parallel version of the Center Star algorithm which overtakes vastly its CPU-serial equivalent as well as the parallel version run on the quad-core processor. The speedups over CPU-serial algorithm were from 30 to 110 in the case of synthetic sets and from 55 to 75 for the real sequences obtained from the Pfam database.

Cluster Analysis Method for Multiple Sequence Alignment

the addition of more data in the field of proteomics, the computational methods need to be more efficient. The fraction or the part of molecular sequence that is more resistant to change is functionally more important to the molecule. Comparative approaches are used to ensure the reliability of sequence alignment. The problem of multiple sequence alignment (MSA) is a proposition of evolutionary history. The explicit homologous correspondence of each individual sequence position is established for each column in the alignment. In the present work, the different pair-wise sequence alignment methods are discussed. The limitation of these methods is that they are capable for aligning the limited number of sequences having small sequence length. A new method is proposed for sequence alignment based on the local alignment with consensus sequence. The triticum wheat varieties sequences are considered which are loaded from the NCBI databank. The dataset is divided into two parts and two phylogenetic trees are constructed for each dataset. Using advanced pruning techniques, a single tree is constructed from the two trees generated. Then by applying the threshold condition, the closely related sequences are extracted and optimal MSA is obtained using shift operations in both directions.

Performance analysis of computational approaches to solve Multiple Sequence Alignment

In the biotechnology field, the deployment of the Multiple Sequence Alignment (MSA) problem, which is a high performance computing demanding process, is one of the new challenges to address on the new parallel systems. The aim of this problem is to find similar regions on biological sequences. Furthermore, the goal of MSA applications is to align as much sequences as possible with a level of quality that makes the alignment biologically meaningful. An efficiency study of different MSA implementations, based on T-Coffee (one of the most used MSA aligners), has been performed in order to find new optimizations that may improve the average execution time on multi-core systems. We found that the current parallel implementations have some performance issues, affecting negatively the scalability of the process. Finally, the proposed implementation based on the usage of threads in conjunction with a message-passing library is presented, with the aim to optimize the execution of the MSA problem in multi-core-based clusters.

Phylo: A Citizen Science Approach for Improving Multiple Sequence Alignment

BackgroundComparative genomics, or the study of the relationships of genome structure and function across different species, offers a powerful tool for studying evolution, annotating genomes, and understanding the causes of various genetic disorders. However, aligning multiple sequences of DNA, an essential intermediate step for most types of analyses, is a difficult computational task. In parallel, citizen science, an approach that takes advantage of the fact that the human brain is exquisitely tuned to solving specific types of problems, is becoming increasingly popular. There, instances of hard computational problems are dispatched to a crowd of non-expert human game players and solutions are sent back to a central server.Methodology/Principal FindingsWe introduce Phylo, a human-based computing framework applying “crowd sourcing” techniques to solve the Multiple Sequence Alignment (MSA) problem. The key idea of Phylo is to convert the MSA problem into a casual game that can be played by ordinary web users with a minimal prior knowledge of the biological context. We applied this strategy to improve the alignment of the promoters of disease-related genes from up to 44 vertebrate species. Since the launch in November 2010, we received more than 350,000 solutions submitted from more than 12,000 registered users. Our results show that solutions submitted contributed to improving the accuracy of up to 70% of the alignment blocks considered.Conclusions/SignificanceWe demonstrate that, combined with classical algorithms, crowd computing techniques can be successfully used to help improving the accuracy of MSA. More importantly, we show that an NP-hard computational problem can be embedded in casual game that can be easily played by people without significant scientific training. This suggests that citizen science approaches can be used to exploit the billions of “human-brain peta-flops” of computation that are spent every day playing games. Phylo is available at: http://phylo.cs.mcgill.ca.

A Lossy Compression Technique Enabling Duplication-Aware Sequence Alignment

In spite of the recognized importance of tandem duplications in genome evolution, commonly adopted sequence comparison algorithms do not take into account complex mutation events involving more than one residue at the time, since they are not compliant with the underlying assumption of statistical independence of adjacent residues. As a consequence, the presence of tandem repeats in sequences under comparison may impair the biological significance of the resulting alignment. Although solutions have been proposed, repeat-aware sequence alignment is still considered to be an open problem and new efficient and effective methods have been advocated. The present paper describes an alternative lossy compression scheme for genomic sequences which iteratively collapses repeats of increasing length. The resulting approximate representations do not contain tandem duplications, while retaining enough information for making their comparison even more significant than the edit distance between the original sequences. This allows us to exploit traditional alignment algorithms directly on the compressed sequences. Results confirm the validity of the proposed approach for the problem of duplication-aware sequence alignment.

Efficient solutions for the far from most string problem

Computational molecular biology has emerged as one of the most exciting interdisciplinary fields. It has currently benefited from concepts and theoretical results obtained by different scientific research communities, including genetics, biochemistry, and computer science. In the past few years it has been shown that a large number of molecular biology problems can be formulated as combinatorial optimization problems, including sequence alignment problems, genome rearrangement problems, string selection and comparison problems, and protein structure prediction and recognition. This paper provides a detailed description of string selection and string comparison problems. For finding good-quality solutions of a particular class of string comparison molecular biology problems, known as the far from most string problem, we propose new heuristics, including a Greedy Randomized Adaptive Search Procedure (GRASP) and a Genetic Algorithm (GA). Computational results indicate that these randomized heuristics find better quality solutions compared with results produced by the best state-of-the-art heuristic approach.

Multiple Sequence Alignment Using a Genetic Algorithm

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.

GpALIGNER: A Fast Algorithm for Global Pairwise Alignment of DNA Sequences

Bioinformatics, through the sequencing of the full genomes for many species, is increasingly relying on efficient global alignment tools exhibiting both high sensitivity and specificity. Many computational algorithms have been applied for solving the sequence alignment problem. Dynamic programming, statistical methods, approximation and heuristic algorithms are the most common methods applied to this problem. We introduce gpALIGNER, a fast pairwise DNA-DNA global alignment algorithm. gpALIGNER uses similar score schema with DIALIGN-T to produce the final alignment. It also uses the concept of spaced seeds to determine locally aligned subsequences which construct semi-global alignment as the preliminaries of global alignment computation. This enables gpALIGNER to have the precision provided by the DIALIGN-T algorithm in considerably less time and space complexities. We performed benchmarking of our approach based on numerous datasets from standard benchmarking databases and real sequences of NCBI database where gpALIGNER performed three times faster than DIALIGN-T. gpALIGNER is a new alternative for having sensitivity and selectivity of DIALIGN-T but with less computational cost.

Meta-alignment with crumble and prune: partitioning very large alignment problems for performance and parallelization.

BackgroundContinuing research into the global multiple sequence alignment problem has resulted in more sophisticated and principled alignment methods. Unfortunately these new algorithms often require large amounts of time and memory to run, making it nearly impossible to run these algorithms on large datasets. As a solution, we present two general methods, Crumble and Prune, for breaking a phylogenetic alignment problem into smaller, more tractable sub-problems. We call Crumble and Prune meta-alignment methods because they use existing alignment algorithms and can be used with many current alignment programs. Crumble breaks long alignment problems into shorter sub-problems. Prune divides the phylogenetic tree into a collection of smaller trees to reduce the number of sequences in each alignment problem. These methods are orthogonal: they can be applied together to provide better scaling in terms of sequence length and in sequence depth. Both methods partition the problem such that many of the sub-problems can be solved independently. The results are then combined to form a solution to the full alignment problem.ResultsCrumble and Prune each provide a significant performance improvement with little loss of accuracy. In some cases, a gain in accuracy was observed. Crumble and Prune were tested on real and simulated data. Furthermore, we have implemented a system called Job-tree that allows hierarchical sub-problems to be solved in parallel on a compute cluster, significantly shortening the run-time.ConclusionsThese methods enabled us to solve gigabase alignment problems. These methods could enable a new generation of biologically realistic alignment algorithms to be applied to real world, large scale alignment problems.

Regular Language Constrained Sequence Alignment Revisited

Imposing constraints in the form of a finite automaton or a regular expression is an effective way to incorporate additional a priori knowledge into sequence alignment procedures. With this motivation, the Regular Expression Constrained Sequence Alignment Problem was introduced, which proposed an O(n²t⁴) time and O(n²t²) space algorithm for solving it, where n is the length of the input strings and t is the number of states in the input non-deterministic automaton. A faster O(n²t³) time algorithm for the same problem was subsequently proposed. In this article, we further speed up the algorithms for Regular Language Constrained Sequence Alignment by reducing their worst case time complexity bound to O(n²t³)/log t). This is done by establishing an optimal bound on the size of Straight-Line Programs solving the maxima computation subproblem of the basic dynamic programming algorithm. We also study another solution based on a Steiner Tree computation. While it does not improve the worst case, our simulations show that both approaches are efficient in practice, especially when the input automata are dense.