A multiple sequence alignment program.

A program is described for simultaneously aligning two or more molecular sequences which is based on first finding common segments above a specified length and then piecing these together to maximize an alignment scoring function. Optimal as well as near-optimal alignments are found, and there is also provided a means for randomizing the given sequences for testing the statistical significance of an alignment. Alignments may be made in the original alphabets of the sequences or in user-specified alternate ones to take advantage of chemical similarities (such as hydrophobic-hydrophilic).     

Parallelized multiple alignment

SUMMARY: Multiple sequence alignment is a frequently used technique for analyzing sequence relationships. Compilation of large alignments is computationally expensive, but processing time can be considerably reduced when the computational load is distributed over many processors. Parallel processing functionality in the form of single-instruction multiple-data (SIMD) technology was implemented into the multiple alignment program Praline by using 'message passing interface' (MPI) routines. Over the alignments tested here, the parallelized program performed up to ten times faster on 25 processors compared to the single processor version. AVAILABILITY: Example program code for parallelizing pairwise alignment loops is available from http://mathbio.nimr.mrc.ac.uk/~jkleinj/tools/mpicode. The 'message passing interface' package (MPICH) is available from http:/www.unix.mcs.anl.gov/mpi/mpich. CONTACT: jhering@nimr.mrc.ac.uk SUPPLEMENTARY INFORMATION: Praline is accessible at http://mathbio.nimr.mrc.ac.uk/praline.     

基于Progressive多序列比对方法的求解多序列比对的启发式算法

在生物信息学研究中,生物序列比对问题占有重要的地位。多序列比对问题是一个NPC问题,由于时间和空间的限制不能够求出精确解。文中简要介绍了Feng和Doolittle提出的多序列比对算法的基本思想,并改进了该算法使之具有更好的比对精度。实验结果表明,新算法对解决一般的progressive多序列比对方法中遇到的局部最优问题有较好的效果。     

Approximate multiple protein structure alignment using the sum-of-pairs distance.

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.     

MAVID multiple alignment server

MAVID is a multiple alignment program suitable for many large genomic regions. The MAVID web server allows biomedical researchers to quickly obtain multiple alignments for genomic sequences and to subsequently analyse the alignments for conserved regions. MAVID has been successfully used for the alignment of closely related species such as primates and also for the alignment of more distant organisms such as human and fugu. The server is fast, capable of aligning hundreds of kilobases in less than a minute. The multiple alignment is used to build a phylogenetic tree for the sequences, which is subsequently used as a basis for identifying conserved regions in the alignment. The server can be accessed at http://baboon.math.berkeley.edu/mavid/.     

Efficient multiple genome alignment

MOTIVATION: To allow a direct comparison of the genomic DNA sequences of sufficiently similar organisms, there is an urgent need for software tools that can align more than two genomic sequences. RESULTS: We developed new algorithms and a software tool 'Multiple Genome Aligner' (MGA for short) that efficiently computes multiple genome alignments of large, closely related DNA sequences. For example, it can align 85% percent of the complete genomes of six human adenoviruses (average length 35305 bp.) in 159 seconds. An alignment of 74% of the complete genomes of three of strains of E. coli (lengths: 5528445; 5498450; 4639221 approximately bp.) is produced in 30 minutes.     

Computational complexity of multiple sequence alignment with SP-score.

It is shown that the multiple alignment problem with SP-score is NP-hard for each scoring matrix in a broad class M that includes most scoring matrices actually used in biological applications. The problem remains NP-hard even if sequences can only be shifted relative to each other and no internal gaps are allowed. It is also shown that there is a scoring matrix M(0) such that the multiple alignment problem for M(0) is MAX-SNP-hard, regardless of whether or not internal gaps are allowed.     

A comprehensive comparison of multiple sequence alignment programs.

In recent years improvements to existing programs and the introduction of new iterative algorithms have changed the state-of-the-art in protein sequence alignment. This paper presents the first systematic study of the most commonly used alignment programs using BAliBASE benchmark alignments as test cases. Even below the 'twilight zone' at 10-20% residue identity, the best programs were capable of correctly aligning on average 47% of the residues. We show that iterative algorithms often offer improved alignment accuracy though at the expense of computation time. A notable exception was the effect of introducing a single divergent sequence into a set of closely related sequences, causing the iteration to diverge away from the best alignment. Global alignment programs generally performed better than local methods, except in the presence of large N/C-terminal extensions and internal insertions. In these cases, a local algorithm was more successful in identifying the most conserved motifs. This study enables us to propose appropriate alignment strategies, depending on the nature of a particular set of sequences. The employment of more than one program based on different alignment techniques should significantly improve the quality of automatic protein sequence alignment methods. The results also indicate guidelines for improvement of alignment algorithms.     

Strategies for multiple sequence alignment

We present an overview of multiple sequence alignments to outline the practical consequences for the choices among different techniques and parameters. We begin with a discussion of the scoring methods for quantifying the quality of a multiple sequence alignment, followed by a discussion of the algorithms implemented within a variety of multiple sequence alignment programs. We also discuss additional alignment details such as gap penalty and distance metrics. The paper concludes with a discussion on how to improve alignment quality and the limitations of the techniques described in this paper     

BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs

SUMMARY: BAliBASE is a database of manually refined multiple sequence alignments categorized by core blocks of conservation sequence length, similarity, and the presence of insertions and N/C-terminal extensions. AVAILABILITY: From http://www-igbmc. u-strasbg.fr/BioInfo/BAliBASE/index.html     

A polynomial time solvable formulation of multiple sequence alignment.

Since traditional multiple alignment formulations are NP-hard, heuristics are commonly employed to find acceptable alignments with no guaranteed performance bound. This causes a substantial difficulty in understanding what the resulting alignment means and in assessing the quality of these alignments. We propose an alternative formulation of multiple alignment based on the idea of finding a multiple alignment of k sequences which preserves k - 1 pairwise alignments as specified by edges of a given tree. Although it is well known that such a preserving alignment always exists, it did not become a mainstream method for multiple alignment since it seems that a lot of information is lost from ignoring pairwise similarities outside the tree. In contrast, by using pairwise alignments that incorporate consistency information from other sequences, we show that it is possible to obtain very good accuracy with the preserving alignment formulation. We show that a reasonable objective function to use is to find the shortest preserving alignment, and, by a reduction to a graph-theoretic problem, that the problem of finding the shortest preserving multiple alignment can be solved in polynomial time. We demonstrate the success of this approach on three sets of benchmark multiple alignments by using consistency-based pairwise alignments from the first stage of two of the best performing progressive alignment algorithms TCoffee and ProbCons and replace the second heuristic progressive step of these algorithms by the exact preserving alignment step. We apply this strategy to TCoffee and show that our approach outperforms TCoffee on two of the three test sets. We apply the strategy to a variant of ProbCons with no iterative refinements and show that our approach achieves similar or better accuracy except on one test set. We also compare our performance to ProbCons with iterative refinements and show that our approach achieves similar or better accuracy on many subcategories even without further refinements. The most important advantage of the preserving alignment formulation is that we are certain that we can solve the problem in polynomial time without using a heuristic. A software program implementing this approach (PSAlign) is available at http://faculty.cs.tamu.edu/shsze/psalign.     

Quality assessment of multiple alignment programs

A renewed interest in the multiple sequence alignment problem has given rise to several new algorithms. In contrast to traditional progressive methods, computationally expensive score optimization strategies are now predominantly employed. We systematically tested four methods (Poa, Dialign, T-Coffee and ClustalW) for the speed and quality of their alignments. As test sequences we used structurally derived alignments from BAliBASE and synthetic alignments generated by Rose. The tests included alignments of variable numbers of domains embedded in random spacer sequences. Overall, Dialign was the most accurate in cases with low sequence identity, while T-Coffee won in cases with high sequence identity. The fast Poa algorithm was almost as accurate, while ClustalW could compete only in strictly global cases with high sequence similarity.     

On the alignment of multiple time series fragments

We consider a local least-squares criterion for aligning multipletime series fragments differing by locations and show the consistencyof the time-lag estimator and the asymptotic normality of thelocation estimator. We apply the criterion to the problem ofaligning 50 glacial varve fragments and construct a 3000-yearsurrogate for global temperature.     

Sequence alignment of citrate synthase proteins using a multiple sequence alignment algorithm and multiple scoring matrices

The alignment of Escherichia coli citrate synthase to pig heart citrate synthase and the multiple alignment of the known sequences of the citrate synthase family of enzymes have been performed using six different amino acid similarity scoring matrices and a large range of gap penalty ratios for insertions and deletions of amino acids. The alignment studies have been performed as the first step in a project aimed at homology modelling E. coli citrate synthase (a hexamer) from pig heart citrate synthase (a dimer) in a molecular modelling approach to the study of multi-subunit enzymes. The effects of several important variables in producing realistic alignments have been investigated. The difference between multiple alignment of the family of enzymes versus simple pairwise alignment of the pig heart and E. coli proteins was explored. The effects of initial separate multiple alignments of the most highly related or most homologous species of the family of enzymes upon a subsequent pairwise alignment between species was evaluated. The value of 'fingerprinting' certain residues to bias the alignment in favour of matching those residues, as well as the worth of the computerized approach compared to an intuitive alignment technique, were assessed.     

Protein multiple alignment incorporating primary and secondary structure information.

Identifying common local segments, also called motifs, in multiple protein sequences plays an important role for establishing homology between proteins. Homology is easy to establish when sequences are similar (sharing an identity > 25%). However, for distant proteins, it is much more difficult to align motifs that are not similar in sequences but still share common structures or functions. This paper is a first attempt to align multiple protein sequences using both primary and secondary structure information. A new sequence model is proposed so that the model assigns high probabilities not only to motifs that contain conserved amino acids but also to motifs that present common secondary structures. The proposed method is tested in a structural alignment database BAliBASE. We show that information brought by the predicted secondary structures greatly improves motif identification. A website of this program is available at www.stat.purdue.edu/~junxie/2ndmodel/sov.html.     

Reticular alignment: A progressive corner-cutting method for multiple sequence alignment

Background  

In 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.     

Gap costs for multiple sequence alignment

Standard methods for aligning pairs of biological sequences charge for the most common mutations, which are substitutions, deletions and insertions. Because a single mutation may insert or delete several nucleotides, gap costs that are not directly proportional to gap length are usually the most effective. How to extend such gap costs to alignments of three or more sequences is not immediately obvious, and a variety of approaches have been taken. This paper argues that, since gap and substitution costs together specify optimal alignments, they should be defined using a common rationale. Specifically, a new definition of gap costs for multiple alignments is proposed and compared with previous ones. Since the new definition links a multiple alignment's cost to that of its pairwise projections, it allows knowledge gained about two-sequence alignments to bear on the multiple alignment problem. Also, such linkage is a key element of recent algorithms that have rendered practical the simultaneous alignment of as many as six sequences.     

Lower bounds on multiple sequence alignment using exact 3-way alignment

Background  

Multiple sequence alignment is fundamental. Exponential growth in computation time appears to be inevitable when an optimal alignment is required for many sequences. Exact costs of optimum alignments are therefore rarely computed. Consequently much effort has been invested in algorithms for alignment that are heuristic, or explore a restricted class of solutions. These give an upper bound on the alignment cost, but it is equally important to determine the quality of the solution obtained. In the absence of an optimal alignment with which to compare, lower bounds may be calculated to assess the quality of the alignment. As more effort is invested in improving upper bounds (alignment algorithms), it is therefore important to improve lower bounds as well. Although numerous cost metrics can be used to determine the quality of an alignment, many are based on sum-of-pairs (SP) measures and their generalizations.