Fast and sensitive multiple sequence alignments on a microcomputer

A strategy is described for the rapid alignment of many longnucleic acid or protein sequences on a microcomputer. The programdescribed can handle up to 100 sequences of 1200 residues each.The approach is based on progressively aligning sequences accordingto the branching order in an initial phylogenetic tree. Theresults obtained using the package appear to be as sensitiveas those from any other available method. Received on October 7, 1988; accepted on December 6, 1988     

A fast and sensitive multiple sequence alignment algorithm

A two-step multiple alignment strategy is presented that allowsrapid alignment of a set of homologous sequences and comparisonof pre-aligned groups of sequences. Examples are given demonstratingthe improvement in the quality of alignments when comparingentire groups instead of single sequences. The modular designof computer programs based on this algorithm allows for storageof aligned sequences and successive alignment of any numberof sequences. Received on August 23, 1988; accepted on December 6, 1988     

Constructing Sequence Alignments from a Markov Decision Model with Estimated Parameter Values

Current methods for aligning biological sequences are based on dynamic programming algorithms. If large numbers of sequences or a number of long sequences are to be aligned, the required computations are expensive in memory and central processing unit (CPU) time. In an attempt to bring the tools of large-scale linear programming (LP) methods to bear on this problem, we formulate the alignment process as a controlled Markov chain and construct a suggested alignment based on policies that minimise the expected total cost of the alignment. We discuss the LP associated with the total expected discounted cost and show the results of a solution of the problem based on a primal-dual interior point method. Model parameters, estimated from aligned sequences, along with cost function parameters are used to construct the objective and constraint conditions of the LP problem. This article concludes with a discussion of some alignments obtained from the LP solutions of problems with various cost function parameter values.     

Sequence search on a supercomputer.

A set of programs was developed for searching nucleic acid and protein sequence data bases for sequences similar to a given sequence. The programs, written in FORTRAN 77, were optimized for vector processing on a Hitachi S810-20 supercomputer. A search of a 500-residue protein sequence against the entire PIR data base Ver. 1.0 (1) (0.5 M residues) is carried out in a CPU time of 45 sec. About 4 min is required for an exhaustive search of a 1500-base nucleotide sequence against all mammalian sequences (1.2M bases) in Genbank Ver. 29.0. The CPU time is reduced to about a quarter with a faster version.     

Progressive sequence alignment as a prerequisitetto correct phylogenetic trees

A progressive alignment method is described that utilizes the Needleman and Wunsch pairwise alignment algorithm iteratively to achieve the multiple alignment of a set of protein sequences and to construct an evolutionary tree depicting their relationship. The sequences are assumed a priori to share a common ancestor, and the trees are constructed from difference matrices derived directly from the multiple alignment. The thrust of the method involves putting more trust in the comparison of recently diverged sequences than in those evolved in the distant past. In particular, this rule is followed: "once a gap, always a gap." The method has been applied to three sets of protein sequences: 7 superoxide dismutases, 11 globins, and 9 tyrosine kinase-like sequences. Multiple alignments and phylogenetic trees for these sets of sequences were determined and compared with trees derived by conventional pairwise treatments. In several instances, the progressive method led to trees that appeared to be more in line with biological expectations than were trees obtained by more commonly used methods.     

ALIGNMENT SERVICE: creation and processing of alingments of sequences of unlimited length

A package for the creation and processing of multiple sequencealignment is described. There is no limit on the lengths ofthe processed nucleotide or amino acid sequences, and the numberof sequences in the alignment is also unlimited. The main groupsof functions are: a semi–automatic alignment editor; awide set of functions for technical processing of alignments;nucleotide alignment mapping and translation; and similaritysearch functions. A user-friendly interface and a set of generallyused file actions provide a special operational subsystem foreveryday tasks     

REVCOM: a robust Bayesian method for evolutionary rate estimation

MOTIVATION: Evolutionary conservation estimated from a multiple sequence alignment is a powerful indicator of the functional significance of a residue and helps to predict active sites, ligand binding sites, and protein interaction interfaces. Many algorithms that calculate conservation work well, provided an accurate and balanced alignment is used. However, such a strong dependence on the alignment makes the results highly variable. We attempted to improve the conservation prediction algorithm by making it more robust and less sensitive to (1) local alignment errors, (2) overrepresentation of sequences in some branches and (3) occasional presence of unrelated sequences. RESULTS: A novel method is presented for robust constrained Bayesian estimation of evolutionary rates that avoids overfitting independent rates and satisfies the above requirements. The method is evaluated and compared with an entropy-based conservation measure on a set of 1494 protein interfaces. We demonstrated that approximately 62% of the analyzed protein interfaces are more conserved than the remaining surface at the 5% significance level. A consistent method to incorporate alignment reliability is proposed and demonstrated to reduce arbitrary variation of calculated rates upon inclusion of distantly related or unrelated sequences into the alignment.     

CLUSTAL: a package for performing multiple sequence alignment on a microcomputer

An approach for performing multiple alignments of large numbers of amino acid or nucleotide sequences is described. The method is based on first deriving a phylogenetic tree from a matrix of all pairwise sequence similarity scores, obtained using a fast pairwise alignment algorithm. Then the multiple alignment is achieved from a series of pairwise alignments of clusters of sequences, following the order of branching in the tree. The method is sufficiently fast and economical with memory to be easily implemented on a microcomputer, and yet the results obtained are comparable to those from packages requiring mainframe computer facilities.     

Multiple sequence alignment in parallel on a workstation cluster

SUMMARY: Multiple sequence alignment is the NP-hard problem of aligning three or more DNA or amino acid sequences in an optimal way so as to match as many characters as possible from the set of sequences. The popular sequence alignment program ClustalW uses the classical method of approximating a sequence alignment, by first computing a distance matrix and then constructing a guide tree to show the evolutionary relationship of the sequences. We show that parallelizing the ClustalW algorithm can result in significant speedup. We used a cluster of workstations using C and message passing interface for our implementation. Experimental results show that speedup of over 5.5 on six processors is obtainable for most inputs. AVAILABILITY: The software is available upon request from the second author.     

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.     

Simultaneous and multivariate alignment of protein sequences: correspondence between physicochemical profiles and structurally conserved regions (SCR)

A general protein sequence alignment methodology for detecting a priori unknown common structural and functional regions is described. The method proposed in this paper is based on two basic requirements for a meaningful alignment. First, each sequence or segment of a sequence is characterized by a multivariate physicochemical profile. Second, the alignment is performed by considering all the sequences simultaneously, and the algorithm detects those regions that form a set of similar profiles. In order to test the structural meaning of the alignment obtained from the sequences, quantitative comparisons are performed with structurally conserved regions (SCR) determined from the X-ray structures of three serine proteases. Results suggest that the limits of the SCR may be predicted from the similarities between the physicochemical profiles of the sequences. The procedures are not completely automated. The final step requires a visual screening of alternative pathways in order to determine an optimal alignment.     

Application of discrete Fourier inter-coefficient difference for assessing genetic sequence similarity

Digital signal processing (DSP) techniques for biological sequence analysis continue to grow in popularity due to the inherent digital nature of these sequences. DSP methods have demonstrated early success for detection of coding regions in a gene. Recently, these methods are being used to establish DNA gene similarity. We present the inter-coefficient difference (ICD) transformation, a novel extension of the discrete Fourier transformation, which can be applied to any DNA sequence. The ICD method is a mathematical, alignment-free DNA comparison method that generates a genetic signature for any DNA sequence that is used to generate relative measures of similarity among DNA sequences. We demonstrate our method on a set of insulin genes obtained from an evolutionarily wide range of species, and on a set of avian influenza viral sequences, which represents a set of highly similar sequences. We compare phylogenetic trees generated using our technique against trees generated using traditional alignment techniques for similarity and demonstrate that the ICD method produces a highly accurate tree without requiring an alignment prior to establishing sequence similarity.     

A program for template matching of protein sequences

The matching of a template to a protein sequence is simplifiedby treating it as a special case of sequence alignment. Restrictionof the distances between motifs in the template controls againstspurious matches within very long sequences. The program usingthis algorithm is fast enough to be used in scanning large databasesfor sequences matching a complex template. Received on August 17, 1987; accepted on January 11, 1988     

ParAlign: a parallel sequence alignment algorithm for rapid and sensitive database searches

There is a need for faster and more sensitive algorithms for sequence similarity searching in view of the rapidly increasing amounts of genomic sequence data available. Parallel processing capabilities in the form of the single instruction, multiple data (SIMD) technology are now available in common microprocessors and enable a single microprocessor to perform many operations in parallel. The ParAlign algorithm has been specifically designed to take advantage of this technology. The new algorithm initially exploits parallelism to perform a very rapid computation of the exact optimal ungapped alignment score for all diagonals in the alignment matrix. Then, a novel heuristic is employed to compute an approximate score of a gapped alignment by combining the scores of several diagonals. This approximate score is used to select the most interesting database sequences for a subsequent Smith-Waterman alignment, which is also parallelised. The resulting method represents a substantial improvement compared to existing heuristics. The sensitivity and specificity of ParAlign was found to be as good as Smith-Waterman implementations when the same method for computing the statistical significance of the matches was used. In terms of speed, only the significantly less sensitive NCBI BLAST 2 program was found to outperform the new approach. Online searches are available at http://dna.uio.no/search/     

Pandit: a database of protein and associated nucleotide domains with inferred trees

MOTIVATION: A large, high-quality database of homologous sequence alignments with good estimates of their corresponding phylogenetic trees will be a valuable resource to those studying phylogenetics. It will allow researchers to compare current and new models of sequence evolution across a large variety of sequences. The large quantity of data may provide inspiration for new models and methodology to study sequence evolution and may allow general statements about the relative effect of different molecular processes on evolution. RESULTS: The Pandit 7.6 database contains 4341 families of sequences derived from the seed alignments of the Pfam database of amino acid alignments of families of homologous protein domains (Bateman et al., 2002). Each family in Pandit includes an alignment of amino acid sequences that matches the corresponding Pfam family seed alignment, an alignment of DNA sequences that contain the coding sequence of the Pfam alignment when they can be recovered (overall, 82.9% of sequences taken from Pfam) and the alignment of amino acid sequences restricted to only those sequences for which a DNA sequence could be recovered. Each of the alignments has an estimate of the phylogenetic tree associated with it. The tree topologies were obtained using the neighbor joining method based on maximum likelihood estimates of the evolutionary distances, with branch lengths then calculated using a standard maximum likelihood approach.     

Multiple sequence alignment by consensus.

An algorithm for multiple sequence alignment is given that matches words of length and degree of mismatch chosen by the user. The alignment maximizes an alignment scoring function. The method is based on a novel extension of our consensus sequence methods. The algorithm works for both DNA and protein sequences, and from earlier work on consensus sequences, it is possible to estimate statistical significance.     

CLAGen: a tool for clustering and annotating gene sequences using a suffix tree algorithm

Most multiple gene sequence alignment methods rely on conventions regarding the score of a multiple alignment in pairwise fashion. Therefore, as the number of sequences increases, the runtime of sequencing expands exponentially. In order to solve the problem, this paper presents a multiple sequence alignment method using a linear-time suffix tree algorithm to cluster similar sequences at one time without pairwise alignment. After searching for common subsequences, cross-matching common subsequences were generated, and sometimes inexact matching was found. So, a procedure aimed at masking the inexact cross-matching pairs was suggested here. In addition, BLAST was combined with a clustering tool in order to annotate the clusters generated by suffix tree clustering. The proposed method for clustering and annotating genes consists of the following steps: (1) construction of a suffix tree; (2) searching and overlapping common subsequences; (3) grouping subsequence pairs; (4) masking cross-matching pairs; (5) clustering gene sequences; (6) annotating gene clusters by the BLAST search. The performance of the proposed system, CLAGen, was successfully evaluated with 42 gene sequences in a TCA cycle (a citrate cycle) of bacteria. The system generated 11 clusters and found the longest subsequences of each cluster, which are biologically significant.     

MELDB: a database for microbial esterases and lipases

MELDB is a comprehensive protein database of microbial esterases and lipases which are hydrolytic enzymes important in the modern industry. Proteins in MELDB are clustered into groups according to their sequence similarities based on a local pairwise alignment algorithm and a graph clustering algorithm (TribeMCL). This differs from traditional approaches that use global pairwise alignment and joining methods. Our procedure was able to reduce the noise caused by dubious alignment in the distantly related or unrelated regions in the sequences. In the database, 883 esterase and lipase sequences derived from microbial sources are deposited and conserved parts of each protein are identified. HMM profiles of each cluster were generated to classify unknown sequences. Contents of the database can be keyword-searched and query sequences can be aligned to sequence profiles and sequences themselves.