Searching for evolutionary distant RNA homologs within genomic sequences using partition function posterior probabilities

Background  

Identification of RNA homologs within genomic stretches is difficult when pairwise sequence identity is low or unalignable flanking residues are present. In both cases structure-sequence or profile/family-sequence alignment programs become difficult to apply because of unreliable RNA structures or family alignments. As such, local sequence-sequence alignment programs are frequently used instead. We have recently demonstrated that maximal expected accuracy alignments using partition function match probabilities (implemented in Probalign) are significantly better than contemporary methods on heterogeneous length protein sequence datasets, thus suggesting an affinity for local alignment.     

Local multiple sequence alignment using dead-end elimination

MOTIVATION: Local multiple sequence alignment is a basic tool for extracting functionally important regions shared by a family of protein sequences. We present an effectively polynomial-time algorithm for rigorously solving the local multiple alignment problem. RESULTS: The algorithm is based on the dead-end elimination procedure that makes it possible to avoid an exhaustive search. In the framework of the sum-of-pairs scoring system, certain rejection criteria are derived in order to eliminate those sequence segments and segment pairs that can be mathematically shown to be inconsistent (dead-ending) with the globally optimal alignment. Iterative application of the elimination criteria results in a rapid reduction of combinatorial possibilities without considering them explicitly. In the vast majority of cases, the procedure converges to a unique globally optimal solution. In contrast to the exhaustive search, whose computational complexity is combinatorial, the algorithm is computationally feasible because the number of operations required to eliminate the dead-ending segments and segment pairs grows quadratically and cubically, respectively, with the total number of sequence elements. The method is illustrated on a set of protein families for which the globally optimal alignments are well recognized. AVAILABILITY: The source code of the program implementing the algorithm is available upon request from the authors. CONTACT: alex_lukashin@biogen.com.     

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.     

MALIGNED: a multiple sequence alignment editor

A multiple sequence alignment editor is described which runson a VAX/VMS system and can exchange data with a number of otherprograms, including those of the Genetics Computer Group (GCG).Up to 199 sequences can be aligned. The quality of the alignmentcan be easily judged during its development because the displayattributes to each character are determined by the way it matchesthe other sequences. Four methods are available for calculatingthe highlighting to emphasize different aspects of the relationshipsof the sequences and up to four styles of highlighting can beused at the same time. Laser printer output is suitable forpublication without modification.     

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.     

DIALIGN P: Fast pair-wise and multiple sequence alignment using parallel processors

Background  

Parallel computing is frequently used to speed up computationally expensive tasks in Bioinformatics.     

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     

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.     

Improvement in accuracy of multiple sequence alignment using novel group-to-group sequence alignment algorithm with piecewise linear gap cost

Background  

Multiple sequence alignment (MSA) is a useful tool in bioinformatics. Although many MSA algorithms have been developed, there is still room for improvement in accuracy and speed. In the alignment of a family of protein sequences, global MSA algorithms perform better than local ones in many cases, while local ones perform better than global ones when some sequences have long insertions or deletions (indels) relative to others. Many recent leading MSA algorithms have incorporated pairwise alignment information obtained from a mixture of sources into their scoring system to improve accuracy of alignment containing long indels.     

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。     

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.     

Progressive multiple alignment using sequence triplet optimizations and three-residue exchange costs

In this paper we demonstrate a practical approach to construct progressive multiple alignments using sequence triplet optimizations rather than a conventional pairwise approach. Using the sequence triplet alignments progressively provides a scope for the synthesis of a three-residue exchange amino acid substitution matrix. We develop such a 20 x 20 x 20 matrix for the first time and demonstrate how its use in optimal sequence triplet alignments increases the sensitivity of building multiple alignments. Various comparisons were made between alignments generated using the progressive triplet methods and the conventional progressive pairwise procedure. The assessment of these data reveal that, in general, the triplet based approaches generate more accurate sequence alignments than the traditional pairwise based procedures, especially between more divergent sets of sequences.     

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).     

Heuristics for multiobjective multiple sequence alignment

Background

Aligning multiple sequences arises in many tasks in Bioinformatics. However, the alignments produced by the current software packages are highly dependent on the parameters setting, such as the relative importance of opening gaps with respect to the increase of similarity. Choosing only one parameter setting may provide an undesirable bias in further steps of the analysis and give too simplistic interpretations. In this work, we reformulate multiple sequence alignment from a multiobjective point of view. The goal is to generate several sequence alignments that represent a trade-off between maximizing the substitution score and minimizing the number of indels/gaps in the sum-of-pairs score function. This trade-off gives to the practitioner further information about the similarity of the sequences, from which she could analyse and choose the most plausible alignment.

Methods

We introduce several heuristic approaches, based on local search procedures, that compute a set of sequence alignments, which are representative of the trade-off between the two objectives (substitution score and indels). Several algorithm design options are discussed and analysed, with particular emphasis on the influence of the starting alignment and neighborhood search definitions on the overall performance. A perturbation technique is proposed to improve the local search, which provides a wide range of high-quality alignments.

Results and conclusions

The proposed approach is tested experimentally on a wide range of instances. We performed several experiments with sequences obtained from the benchmark database BAliBASE 3.0. To evaluate the quality of the results, we calculate the hypervolume indicator of the set of score vectors returned by the algorithms. The results obtained allow us to identify reasonably good choices of parameters for our approach. Further, we compared our method in terms of correctly aligned pairs ratio and columns correctly aligned ratio with respect to reference alignments. Experimental results show that our approaches can obtain better results than TCoffee and Clustal Omega in terms of the first ratio.

     

A flexible multiple sequence alignment program

The 'regions' method for multisequence alignment used in the previously reported program MALIGN has been generalized to include recursive refinement so that unaligned portions between two regions at the current level of resolution can be handled with increased resolution. Additionally, there is incorporated a limiting of the number of regions to be used at any level of resolution from which to abstract an alignment. This provides a significant increase in speed over the unlimited version. The program GENALIGN uses this improved regions method to execute fast pairwise alignments in the framework of Taylor's multisequence alignment procedure using clustered pairwise alignments. Pairwise alignments by dynamic programming are also provided in the program.     

Recursive MAGUS: Scalable and accurate multiple sequence alignment

Multiple sequence alignment tools struggle to keep pace with rapidly growing sequence data, as few methods can handle large datasets while maintaining alignment accuracy. We recently introduced MAGUS, a new state-of-the-art method for aligning large numbers of sequences. In this paper, we present a comprehensive set of enhancements that allow MAGUS to align vastly larger datasets with greater speed. We compare MAGUS to other leading alignment methods on datasets of up to one million sequences. Our results demonstrate the advantages of MAGUS over other alignment software in both accuracy and speed. MAGUS is freely available in open-source form at https://github.com/vlasmirnov/MAGUS.     

Sigma: multiple alignment of weakly-conserved non-coding DNA sequence

Background  

Existing tools for multiple-sequence alignment focus on aligning protein sequence or protein-coding DNA sequence, and are often based on extensions to Needleman-Wunsch-like pairwise alignment methods. We introduce a new tool, Sigma, with a new algorithm and scoring scheme designed specifically for non-coding DNA sequence. This problem acquires importance with the increasing number of published sequences of closely-related species. In particular, studies of gene regulation seek to take advantage of comparative genomics, and recent algorithms for finding regulatory sites in phylogenetically-related intergenic sequence require alignment as a preprocessing step. Much can also be learned about evolution from intergenic DNA, which tends to evolve faster than coding DNA. Sigma uses a strategy of seeking the best possible gapless local alignments (a strategy earlier used by DiAlign), at each step making the best possible alignment consistent with existing alignments, and scores the significance of the alignment based on the lengths of the aligned fragments and a background model which may be supplied or estimated from an auxiliary file of intergenic DNA.     

POAVIZ: a Partial order multiple sequence alignment visualizer

SUMMARY: POAVIZ creates a visualization of a multiple sequence alignment that makes clear the overall structure of how sequences match and diverge in the alignment. POAVIZ can construct visualizations from any multiple sequence alignment source (e.g. PIR and CLUSTAL formats), and is valuable for revealing complex branching structure (such as domains, large-scale insertions / deletions or recombinations), especially in partnership with the Partial Order Alignment (POA) multiple sequence alignment program. AVAILABILITY: The Partial Order multiple sequence Alignment Visualizer (POAVIZ) program is available at http://www.bioinformatics.ucla.edu/poa     

M-Coffee: combining multiple sequence alignment methods with T-Coffee

We introduce M-Coffee, a meta-method for assembling multiple sequence alignments (MSA) by combining the output of several individual methods into one single MSA. M-Coffee is an extension of T-Coffee and uses consistency to estimate a consensus alignment. We show that the procedure is robust to variations in the choice of constituent methods and reasonably tolerant to duplicate MSAs. We also show that performances can be improved by carefully selecting the constituent methods. M-Coffee outperforms all the individual methods on three major reference datasets: HOMSTRAD, Prefab and Balibase. We also show that on a case-by-case basis, M-Coffee is twice as likely to deliver the best alignment than any individual method. Given a collection of pre-computed MSAs, M-Coffee has similar CPU requirements to the original T-Coffee. M-Coffee is a freeware open-source package available from http://www.tcoffee.org/.