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PubMed1998

Segment-based scores for pairwise and multiple sequence alignments.

In this paper, we discuss a novel scoring scheme for sequence alignments. The score of an alignment is defined as the sum of so-called weights of aligned segment pairs. A simple modification of the weight function used by the original version of the DIALIGN alignment program turns out to have a crucial advantage: it can be applied to both, global and local alignment problems without the need to specify a threshold parameter.

Amino Acid Sequence↗

PubMed1995

Obtaining a more resolute teleost growth hormone phylogeny by the introduction of gaps in sequence alignment.

In order to obtain a more resolute phylogeny of teleosts based on growth hormone (GH) sequences, phylogenetic analyses were performed in which deletions (gaps), which appear to be order specific, were upheld to maintain GH's structural information. Sequences were analyzed at 194 amino acid positions. In addition, the two closest genealogically related groups to the teleosts, Amia calva and Acipenser guldenstadti, were used as outgroups. Modified sequence alignments were also analyzed to determine clade stability. Analyses indicated, in the most parsimonious cladogram, that molecular and morphological relationships for the orders of fishes are congruent. With GH molecular sequence data it was possible to resolve all clades at the familial level. Analyses of the primary sequence data indicate that: (a) the halecomorphean and chondrostean GH sequences are the appropriate outgroups for generating the most parsimonious cladogram for teleosts; (b) proper alignment of teleost GH sequence by the inclusion of gaps is necessary for resolution of the Percomorpha; and (c) removal of sequence information by deleting improperly aligned sequence decreases the phylogenetic signal obtained.

Amino Acid Sequence↗

PubMed1993

DCSE, an interactive tool for sequence alignment and secondary structure research.

DCSE provides a user-friendly package for the creation and editing of sequence alignments. The program runs on different platforms, including microcomputers and workstations. Apart from available hardware, the program is not limited in the size of the alignment it can handle. It deviates more from classical text editors than other available sequence editors because it uses a different approach towards editing. It shifts characters or entire blocks of aligned characters, rather than inserting or deleting gaps in the sequences. Alignment of a new sequence to an existing alignment is partly automated. Although DCSE can be used on protein sequence alignments, it is especially targeted at the examination of RNA. The secondary structure for every sequence can be incorporated easily in the alignment. DCSE also has extensive built-in support for finding and checking secondary structure elements. A sophisticated system of markers allows notation of special positions in an alignment. This system can be used to store information such as the position of hidden breaks, introns and tertiary structure interactions.

Base Sequence↗

PubMed1999

Positional statistical significance in sequence alignment.

Beginning with the concept of near-optimal sequence alignments, we can assign a probability that each element in one sequence is paired in an alignment with each element in another sequence. This involves a sum over the set of all possible pairwise alignments. The method employs a designed hidden Markov model (HMM) and the rigorous forward and forward-backward algorithms of Rabiner. The approach can use any standard sequence-element-to-element probabilistic similarity measures and affine gap penalty functions. This allows the positional alignment statistical significance to be obtained as a function of such variables. A measure of the probabilistic relationship between any single sequence and a set of sequences can be directly obtained. In addition, the employed HMM with the Viterbi algorithm provides a simple link to the standard dynamic programming optimal alignment algorithms.

Adenylate Kinase↗

PubMed2005

ProbCons: Probabilistic consistency-based multiple sequence alignment.

To study gene evolution across a wide range of organisms, biologists need accurate tools for multiple sequence alignment of protein families. Obtaining accurate alignments, however, is a difficult computational problem because of not only the high computational cost but also the lack of proper objective functions for measuring alignment quality. In this paper, we introduce probabilistic consistency, a novel scoring function for multiple sequence comparisons. We present ProbCons, a practical tool for progressive protein multiple sequence alignment based on probabilistic consistency, and evaluate its performance on several standard alignment benchmark data sets. On the BAliBASE, SABmark, and PREFAB benchmark alignment databases, ProbCons achieves statistically significant improvement over other leading methods while maintaining practical speed. ProbCons is publicly available as a Web resource.

Algorithms↗

PubMed2002

Rapid significance estimation in local sequence alignment with gaps.

In order to assess the significance of sequence alignments, it is crucial to know the distribution of alignment scores of pairs of random sequences. For gapped local alignment, it is empirically known that the shape of this distribution is of the Gumbel form. However, the determination of the parameters of this distribution is a computationally very expensive task. We present a new algorithmic approach which allows estimation of the more important of the Gumbel parameters at least five times faster than the traditional methods. Actual runtimes of our algorithm between less than a second and a few minutes on a workstation bring significance estimation into the realm of interactive applications.

Algorithms↗

PubMed1994

Motif-biased protein sequence alignment.

A method was developed for pairwise protein sequence alignment to emulate the effect of structural knowledge or multiple sequences. Runs of matches of the preferred length were emphasized through the use of a product-bias allowing short motifs to influence the alignment to a degree that was a realistic reflection of their infrequency of occurrence. This gave motifs a locally high scoring match, making their alignment relatively less sensitive to the value of the gap penalty. This property should be a great advantage when a large number of sequence comparisons are made with a fixed set of parameter values, as typically occurs in the scan of a sequence databank with a probe or in the development of a multiple alignment.

Algorithms↗

PubMed1997

The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools.

CLUSTAL X is a new windows interface for the widely-used progressive multiple sequence alignment program CLUSTAL W. The new system is easy to use, providing an integrated system for performing multiple sequence and profile alignments and analysing the results. CLUSTAL X displays the sequence alignment in a window on the screen. A versatile sequence colouring scheme allows the user to highlight conserved features in the alignment. Pull-down menus provide all the options required for traditional multiple sequence and profile alignment. New features include: the ability to cut-and-paste sequences to change the order of the alignment, selection of a subset of the sequences to be realigned, and selection of a sub-range of the alignment to be realigned and inserted back into the original alignment. Alignment quality analysis can be performed and low-scoring segments or exceptional residues can be highlighted. Quality analysis and realignment of selected residue ranges provide the user with a powerful tool to improve and refine difficult alignments and to trap errors in input sequences. CLUSTAL X has been compiled on SUN Solaris, IRIX5.3 on Silicon Graphics, Digital UNIX on DECstations, Microsoft Windows (32 bit) for PCs, Linux ELF for x86 PCs, and Macintosh PowerMac.

Algorithms↗

PubMed2004

The influence of gapped positions in multiple sequence alignments on secondary structure prediction methods.

All currently leading protein secondary structure prediction methods use a multiple protein sequence alignment to predict the secondary structure of the top sequence. In most of these methods, prior to prediction, alignment positions showing a gap in the top sequence are deleted, consequently leading to shrinking of the alignment and loss of position-specific information. In this paper we investigate the effect of this removal of information on secondary structure prediction accuracy. To this end, we have designed SymSSP, an algorithm that post-processes the predicted secondary structure of all sequences in a multiple sequence alignment by (i) making use of the alignment's evolutionary information and (ii) re-introducing most of the information that would otherwise be lost. The post-processed information is then given to a new dynamic programming routine that produces an optimally segmented consensus secondary structure for each of the multiple alignment sequences. We have tested our method on the state-of-the-art secondary structure prediction methods PHD, PROFsec, SSPro2 and JNET using the HOMSTRAD database of reference alignments. Our consensus-deriving dynamic programming strategy is consistently better at improving the segmentation quality of the predictions compared to the commonly used majority voting technique. In addition, we have applied several weighting schemes from the literature to our novel consensus-deriving dynamic programming routine. Finally, we have investigated the level of noise introduced by prediction errors into the consensus and show that predictions of edges of helices and strands are half the time wrong for all the four tested prediction methods.

Algorithms↗

PubMed2003

Tcoffee@igs: A web server for computing, evaluating and combining multiple sequence alignments.

This paper presents Tcoffee@igs, a new server provided to the community by Hewlet Packard computers and the Centre National de la Recherche Scientifique. This server is a web-based tool dedicated to the computation, the evaluation and the combination of multiple sequence alignments. It uses the latest version of the T-Coffee package. Given a set of unaligned sequences, the server returns an evaluated multiple sequence alignment and the associated phylogenetic tree. This server also makes it possible to evaluate the local reliability of an existing alignment and to combine several alternative multiple alignments into a single new one. Tcoffee@igs can be used for aligning protein, RNA or DNA sequences. Datasets of up to 100 sequences (2000 residues long) can be processed. The server and its documentation are available from: http://igs-server.cnrs-mrs.fr/Tcoffee/.

Consensus Sequence↗

PubMed2004

Analysis of multiple genomic sequence alignments: a web resource, online tools, and lessons learned from analysis of mammalian SCL loci.

Comparative analysis of genomic sequences is becoming a standard technique for studying gene regulation. However, only a limited number of tools are currently available for the analysis of multiple genomic sequences. An extensive data set for the testing and training of such tools is provided by the SCL gene locus. Here we have expanded the data set to eight vertebrate species by sequencing the dog SCL locus and by annotating the dog and rat SCL loci. To provide a resource for the bioinformatics community, all SCL sequences and functional annotations, comprising a collation of the extensive experimental evidence pertaining to SCL regulation, have been made available via a Web server. A Web interface to new tools specifically designed for the display and analysis of multiple sequence alignments was also implemented. The unique SCL data set and new sequence comparison tools allowed us to perform a rigorous examination of the true benefits of multiple sequence comparisons. We demonstrate that multiple sequence alignments are, overall, superior to pairwise alignments for identification of mammalian regulatory regions. In the search for individual transcription factor binding sites, multiple alignments markedly increase the signal-to-noise ratio compared to pairwise alignments.

Animals↗

PubMed1995

Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments.

Recently Yi & Lander used a neural network and nearest-neighbor method with a scoring system that combined a sequence-similarity matrix with the local structural environment scoring scheme described by Bowie and co-workers for predicting protein secondary structure. We have improved their scoring system by taking into consideration N and C-terminal positions of alpha-helices and beta-strands and also beta-turns as distinctive types of secondary structure. Another improvement, which also decreases the time of computation, is performed by restricting a data base with a smaller subset of proteins that are similar with a query sequence. Using multiple sequence alignments rather than single sequences and a simple jury decision procedure our method reaches a sustained overall three-state accuracy of 72.2%, which is better than that observed for the most accurate multilayered neural-network approach, tested on the same data set of 126 non-homologous protein chains.

Algorithms↗

PubMed2000

Finite-temperature sequence alignment.

We develop a statistical theory of probabilistic sequence alignments derived from a 'thermodynamic' partition function at finite temperature. Such alignments are a generalization of those obtained from information-theoretic approaches. Finite-temperature statistics can be used to characterize the significance of an alignment and the reliability of its single element pairs.

Algorithms↗

PubMed2005

A benchmark of multiple sequence alignment programs upon structural RNAs.

To date, few attempts have been made to benchmark the alignment algorithms upon nucleic acid sequences. Frequently, sophisticated PAM or BLOSUM like models are used to align proteins, yet equivalents are not considered for nucleic acids; instead, rather ad hoc models are generally favoured. Here, we systematically test the performance of existing alignment algorithms on structural RNAs. This work was aimed at achieving the following goals: (i) to determine conditions where it is appropriate to apply common sequence alignment methods to the structural RNA alignment problem. This indicates where and when researchers should consider augmenting the alignment process with auxiliary information, such as secondary structure and (ii) to determine which sequence alignment algorithms perform well under the broadest range of conditions. We find that sequence alignment alone, using the current algorithms, is generally inappropriate <50-60% sequence identity. Second, we note that the probabilistic method ProAlign and the aging Clustal algorithms generally outperform other sequence-based algorithms, under the broadest range of applications.

Algorithms↗

PubMed2002

Recent progress in multiple sequence alignment: a survey.

The assembly of a multiple sequence alignment (MSA) has become one of the most common tasks when dealing with sequence analysis. Unfortunately, the wide range of available methods and the differences in the results given by these methods makes it hard for a non-specialist to decide which program is best suited for a given purpose. In this review we briefly describe existing techniques and expose the potential strengths and weaknesses of the most widely used multiple alignment packages.

Algorithms↗

PubMed1997

Generating benchmarks for multiple sequence alignments and phylogenetic reconstructions.

We present a new probabilistic model of evolution of RNA-, DNA-, or protein-like sequences and a tool rose that implements this model. By insertion, deletion and substitution of characters, a family of sequences is created from a common ancestor. During this artificial evolutionary process, the "true" history is logged and the "correct" multiple sequence alignment is created simultaneously. We also allow for varying rates of mutation within the sequences making it possible to establish so-called sequence motifs. The results are suitable for the evaluation of methods in multiple sequence alignment computation and the prediction of phylogenetic relationships.

Amino Acid Sequence↗

PubMed1997

Embedding strategies for effective use of information from multiple sequence alignments.

We describe a new strategy for utilizing multiple sequence alignment information to detect distant relationships in searches of sequence databases. A single sequence representing a protein family is enriched by replacing conserved regions with position-specific scoring matrices (PSSMs) or consensus residues derived from multiple alignments of family members. In comprehensive tests of these and other family representations, PSSM-embedded queries produced the best results overall when used with a special version of the Smith-Waterman searching algorithm. Moreover, embedding consensus residues instead of PSSMs improved performance with readily available single sequence query searching programs, such as BLAST and FASTA. Embedding PSSMs or consensus residues into a representative sequence improves searching performance by extracting multiple alignment information from motif regions while retaining single sequence information where alignment is uncertain.

Algorithms↗

PubMed2002

Scoring pairwise genomic sequence alignments.

The parameters by which alignments are scored can strongly affect sensitivity and specificity of alignment procedures. While appropriate parameter choices are well understood for protein alignments, much less is known for genomic DNA sequences. We describe a straightforward approach to scoring nucleotide substitutions in genomic sequence alignments, especially human-mouse comparisons. Scores are obtained from relative frequencies of aligned nucleotides observed in alignments of non-coding, non-repetitive genomic regions, and can be theoretically motivated through substitution models. Additional accuracy can be attained by down-weighting alignments characterized by low compositional complexity. We also describe an evaluation protocol that is relevant when alignments are intended to identify all and only the orthologous positions. One particular scoring matrix, called HOXD70, has proven to be generally effective for human-mouse comparisons, and has been used by the PipMaker server since July, 2000. We discuss but leave open the problem of effectively scoring regions of strongly biased nucleotide composition, such as low G + C content.

Base Pairing↗