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PubMed2002

Local weighting schemes for protein multiple sequence alignment.

This paper describes three weighting schemes for improving the accuracy of progressive multiple sequence alignment methods: (1) global profile pre-processing, to capture for each sequence information about other sequences in a profile before the actual multiple alignment takes place; (2) local pre-processing; which incorporates a new protocol to only use non-overlapping local sequence regions to construct the pre-processed profiles; and (3) local-global alignment, a weighting scheme based on the double dynamic programming (DDP) technique to softly bias global alignment to local sequence motifs. The first two schemes allow the compilation of residue-specific multiple alignment reliability indices, which can be used in an iterative fashion. The schemes have been implemented with associated iterative modes in the PRALINE multiple sequence alignment method, and have been evaluated using the BAliBASE benchmark alignment database. These tests indicate that PRALINE is a toolbox able to build alignments with very high quality. We found that local profile pre-processing raises the alignment quality by 5.5% compared to PRALINE alignments generated under default conditions. Iteration enhances the quality by a further percentage point. The implications of multiple alignment scoring functions and iteration in relation to alignment quality and benchmarking are discussed.

Amino Acid Motifs↗
PubMed2006

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.

Algorithms↗
PubMed2003

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

Algorithms↗
PubMed2002

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

Algorithms↗
PubMed2004

3DCoffee: combining protein sequences and structures within multiple sequence alignments.

Most bioinformatics analyses require the assembly of a multiple sequence alignment. It has long been suspected that structural information can help to improve the quality of these alignments, yet the effect of combining sequences and structures has not been evaluated systematically. We developed 3DCoffee, a novel method for combining protein sequences and structures in order to generate high-quality multiple sequence alignments. 3DCoffee is based on TCoffee version 2.00, and uses a mixture of pairwise sequence alignments and pairwise structure comparison methods to generate multiple sequence alignments. We benchmarked 3DCoffee using a subset of HOMSTRAD, the collection of reference structural alignments. We found that combining TCoffee with the threading program Fugue makes it possible to improve the accuracy of our HOMSTRAD dataset by four percentage points when using one structure only per dataset. Using two structures yields an improvement of ten percentage points. The measures carried out on HOM39, a HOMSTRAD subset composed of distantly related sequences, show a linear correlation between multiple sequence alignment accuracy and the ratio of number of provided structure to total number of sequences. Our results suggest that in the case of distantly related sequences, a single structure may not be enough for computing an accurate multiple sequence alignment.

Protein Conformation↗
PubMed2026

Fast, accurate construction of multiple sequence alignments from protein language embeddings.

Multiple sequence alignment (MSA) is a foundational task in computational biology, underpinning protein structure prediction, evolutionary analysis, and domain annotation. Traditional MSA algorithms rely on pairwise amino acid substitution matrices derived from conserved protein families. While effective for aligning closely related sequences, these scoring schemes struggle in the low-identity "twilight zone." Here, we present a new approach for constructing MSAs leveraging amino acid embeddings generated by protein language models (PLMs), which capture rich evolutionary and contextual information from massive and diverse sequence datasets. We introduce a windowed reciprocal-weighted embedding similarity metric that is surprisingly effective in identifying corresponding amino acids across sequences. Building on this metric, we develop ARIES (Alignment via RecIprocal Embedding Similarity), an algorithm that constructs a PLM-generated template embedding and aligns each sequence to this template via dynamic time warping in order to build a global MSA. Across diverse benchmark datasets, ARIES achieves higher accuracies than existing state-of-the-art approaches, especially in low-identity regimes where traditional methods degrade, while scaling almost linearly with the number of sequences to be aligned. Together, these results provide the first large-scale demonstration of the power of PLMs for accurate and scalable MSA construction across protein families of varying sizes and levels of similarity, highlighting the potential of PLMs to transform comparative sequence analysis.

Deep Learning↗
PubMed2004

Statistical methods for identifying conserved residues in multiple sequence alignment.

The assessment of residue conservation in a multiple sequence alignment is a central issue in bioinformatics. Conserved residues and regions are used to determine structural and functional motifs or evolutionary relationships between the sequences of a multiple sequence alignment. For this reason, residue conservation is a valuable measure for database and motif search or for estimating the quality of alignments. In this paper, we present statistical methods for identifying conserved residues in multiple sequence alignments. While most earlier studies examine the positional conservation of the alignment, we focus on the detection of individual conserved residues at a position. The major advantages of multiple comparison methods originate from their ability to select conserved residues simultaneously and to consider the variability of the residue estimates. Large-scale simulations were used for the comparative analysis of the methods. Practical performance was studied by comparing the structurally and functionally important residues of Src homology 2 (SH2) domains to the assignments of the conservation indices. The applicability of the indices was also compared in three additional protein families comprising different degrees of entropy and variability in alignment positions. The results indicate that statistical multiple comparison methods are sensitive and reliable in identifying conserved residues.

Journal Article↗
PubMed2006

Look-Align: an interactive web-based multiple sequence alignment viewer with polymorphism analysis support.

UNLABELLED: We have developed Look-Align, an interactive web-based viewer to display pre-computed multiple sequence alignments. Although initially developed to support the visualization needs of the maize diversity website Panzea (http://www.panzea.org), the viewer is a generic stand-alone tool that can be easily integrated into other websites. AVAILABILITY: Look-Align is written in Perl using open-source components and is available under an open-source license. Live installation and download information can be found at the Panzea website (http://www.panzea.org/software/alignment_viewer.html). CONTACT: ware@cshl.edu SUPPLEMENTARY INFORMATION: The Supplementary information includes sample lists of multiple sequence alignment software and sample screenshots of the viewer.

Databases, Genetic↗
PubMed2006

Refining multiple sequence alignments with conserved core regions.

Accurate multiple sequence alignments of proteins are very important to several areas of computational biology and provide an understanding of phylogenetic history of domain families, their identification and classification. This article presents a new algorithm, REFINER, that refines a multiple sequence alignment by iterative realignment of its individual sequences with the predetermined conserved core (block) model of a protein family. Realignment of each sequence can correct misalignments between a given sequence and the rest of the profile and at the same time preserves the family's overall block model. Large-scale benchmarking studies showed a noticeable improvement of alignment after refinement. This can be inferred from the increased alignment score and enhanced sensitivity for database searching using the sequence profiles derived from refined alignments compared with the original alignments. A standalone version of the program is available by ftp distribution (ftp://ftp.ncbi.nih.gov/pub/REFINER) and will be incorporated into the next release of the Cn3D structure/alignment viewer.

Algorithms↗
PubMed1992

CLUSTAL V: improved software for multiple sequence alignment.

The CLUSTAL package of multiple sequence alignment programs has been completely rewritten and many new features added. The new software is a single program called CLUSTAL V, which is written in C and can be used on any machine with a standard C compiler. The main new features are the ability to store and reuse old alignments and the ability to calculate phylogenetic trees after alignment. The program is simple to use, completely menu driven and on-line help is provided.

Phylogeny↗
PubMedDate not supplied

Faster algorithms for optimal multiple sequence alignment based on pairwise comparisons.

Multiple Sequence Alignment (MSA) is one of the most fundamental problems in computational molecular biology. The running time of the best known scheme for finding an optimal alignment, based on dynamic programming, increases exponentially with the number of input sequences. Hence, many heuristics were suggested for the problem. We consider a version of the MSA problem where the goal is to find an optimal alignment in which matches are restricted to positions in predefined matching segments. We present several techniques for making the dynamic programming algorithm more efficient, while still finding an optimal solution under these restrictions. We prove that it suffices to find an optimal alignment of the predefined sequence segments, rather than single letters, thereby reducing the input size and thus improving the running time. We also identify "shortcuts" that expedite the dynamic programming scheme. Empirical study shows that, taken together, these observations lead to an improved running time over the basic dynamic programming algorithm by 4 to 12 orders of magnitude, while still obtaining an optimal solution. Under the additional assumption that matches between segments are transitive, we further improve the running time for finding the optimal solution by restricting the search space of the dynamic programming algorithm.

Algorithms↗
PubMed2003

PCMA: fast and accurate multiple sequence alignment based on profile consistency.

UNLABELLED: PCMA (profile consistency multiple sequence alignment) is a progressive multiple sequence alignment program that combines two different alignment strategies. Highly similar sequences are aligned in a fast way as in ClustalW, forming pre-aligned groups. The T-Coffee strategy is applied to align the relatively divergent groups based on profile-profile comparison and consistency. The scoring function for local alignments of pre-aligned groups is based on a novel profile-profile comparison method that is a generalization of the PSI-BLAST approach to profile-sequence comparison. PCMA balances speed and accuracy in a flexible way and is suitable for aligning large numbers of sequences. AVAILABILITY: PCMA is freely available for non-commercial use. Pre-compiled versions for several platforms can be downloaded from ftp://iole.swmed.edu/pub/PCMA/.

Algorithms↗
PubMed1993

Optimal alignment between groups of sequences and its application to multiple sequence alignment.

Four algorithms, A-D, were developed to align two groups of biological sequences. Algorithm A is equivalent to the conventional dynamic programming method widely used for aligning ordinary sequences, whereas algorithms B-D are designed to evaluate the cost for a deletion/insertion more accurately when internal gaps are present in either or both groups of sequences. Rigorous optimization of the 'sum of pairs' (SP) score is achieved by algorithm D, whose average performance is close to O(MNL2), where M and N are numbers of sequences included in the two groups and L is the mean length of the sequences. Algorithm B uses some approximations to cope with profile-based operations, whereas algorithm C is a simpler variant of algorithm D. These group-to-group alignment algorithms were applied to multiple sequence alignment with two iterative strategies: a progressive method based on a given binary tree and a randomized grouping--realignment method. The advantages and disadvantages of the four algorithms are discussed on the basis of the results of examinations of several protein families.

Algorithms↗
PubMed2000

Color and graphic display (CGD): programs for multiple sequence alignment analysis in spreadsheet software.

Interpretation of multiple sequence alignments is of major interest for the prediction of functional and structural domains in proteins or for the organization of related sequences in families and subfamilies. However, a necessity for the bench scientist is the use of outstanding programs in a friendly computing environment. This paper describes Color and Graphic Display (CGD), a set of modules that runs as part of the Microsoft Excel spreadsheet to color and analyze multiple sequence alignments. Discussed here are the main functions of CGD and the use of the program to highlight residues of importance in a water channel family. Although CGD was created for protein sequences, most of the modules are compatible with DNA sequences.

Chemical Phenomena↗
PubMed1988

Multiple sequence alignment with hierarchical clustering.

An algorithm is presented for the multiple alignment of sequences, either proteins or nucleic acids, that is both accurate and easy to use on microcomputers. The approach is based on the conventional dynamic-programming method of pairwise alignment. Initially, a hierarchical clustering of the sequences is performed using the matrix of the pairwise alignment scores. The closest sequences are aligned creating groups of aligned sequences. Then close groups are aligned until all sequences are aligned in one group. The pairwise alignments included in the multiple alignment form a new matrix that is used to produce a hierarchical clustering. If it is different from the first one, iteration of the process can be performed. The method is illustrated by an example: a global alignment of 39 sequences of cytochrome c.

Algorithms↗
PubMed1993

Efficient methods for multiple sequence alignment with guaranteed error bounds.

Multiple string (sequence) alignment is a difficult and important problem in computational biology, where it is central in two related tasks: finding highly conserved subregions or embedded patterns of a set of biological sequences (strings of DNA, RNA or amino acids), and inferring the evolutionary history of a set of taxa from their associated biological sequences. Several precise measures have been proposed for evaluating the goodness of a multiple alignment, but no efficient methods are known which compute the optimal alignment for any of these measures in any but small cases. In this paper, we consider two previously proposed measures, and give two computationaly efficient multiple alignment methods (one for each measure) whose deviation from the optimal value is guaranteed to be less than a factor of two. This is the novel feature of thse methods. but the methods have additional virtues as well. For both methods, the guaranteed bounds are much smaller than two when the number of strings is small (1.33 for three strings of any length); for one of the methods we give a related randomized method which is much faster and which gives, with high probability, multiple alignments with fairly small error bounds; and for the other measure, the method given yields a non-obvious lower bound on the optimal alignment.

Amino Acid Sequence↗
PubMed1994

On the complexity of multiple sequence alignment.

We study the computational complexity of two popular problems in multiple sequence alignment: multiple alignment with SP-score and multiple tree alignment. It is shown that the first problem is NP-complete and the second is MAX SNP-hard. The complexity of tree alignment with a given phylogeny is also considered.

Algorithms↗
PubMed2006

Expresso: automatic incorporation of structural information in multiple sequence alignments using 3D-Coffee.

Expresso is a multiple sequence alignment server that aligns sequences using structural information. The user only needs to provide sequences. The server runs BLAST to identify close homologues of the sequences within the PDB database. These PDB structures are used as templates to guide the alignment of the original sequences using structure-based sequence alignment methods like SAP or Fugue. The final result is a multiple sequence alignment of the original sequences based on the structural information of the templates. An advanced mode makes it possible to either upload private structures or specify which PDB templates should be used to model each sequence. Providing the suitable structural information is available, Expresso delivers sequence alignments with accuracy comparable with structure-based alignments. The server is available on http://www.tcoffee.org/.

Databases, Protein↗