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PubMed2006

Evaluation of sequence alignments and oligonucleotide probes with respect to three-dimensional structure of ribosomal RNA using ARB software package.

BACKGROUND: Availability of high-resolution RNA crystal structures for the 30S and 50S ribosomal subunits and the subsequent validation of comparative secondary structure models have prompted the biologists to use three-dimensional structure of ribosomal RNA (rRNA) for evaluating sequence alignments of rRNA genes. Furthermore, the secondary and tertiary structural features of rRNA are highly useful and successfully employed in designing rRNA targeted oligonucleotide probes intended for in situ hybridization experiments. RNA3D, a program to combine sequence alignment information with three-dimensional structure of rRNA was developed. Integration into ARB software package, which is used extensively by the scientific community for phylogenetic analysis and molecular probe designing, has substantially extended the functionality of ARB software suite with 3D environment. RESULTS: Three-dimensional structure of rRNA is visualized in OpenGL 3D environment with the abilities to change the display and overlay information onto the molecule, dynamically. Phylogenetic information derived from the multiple sequence alignments can be overlaid onto the molecule structure in a real time. Superimposition of both statistical and non-statistical sequence associated information onto the rRNA 3D structure can be done using customizable color scheme, which is also applied to a textual sequence alignment for reference. Oligonucleotide probes designed by ARB probe design tools can be mapped onto the 3D structure along with the probe accessibility models for evaluation with respect to secondary and tertiary structural conformations of rRNA. CONCLUSION: Visualization of three-dimensional structure of rRNA in an intuitive display provides the biologists with the greater possibilities to carry out structure based phylogenetic analysis. Coupled with secondary structure models of rRNA, RNA3D program aids in validating the sequence alignments of rRNA genes and evaluating probe target sites. Superimposition of the information derived from the multiple sequence alignment onto the molecule dynamically allows the researchers to observe any sequence inherited characteristics (phylogenetic information) in real-time environment. The extended ARB software package is made freely available for the scientific community via http://www.arb-home.de.

Algorithms↗
PubMed2003

AntiClustal: Multiple Sequence Alignment by antipole clustering and linear approximate 1-median computation.

In this paper we present a new Multiple Sequence Alignment (MSA) algorithm called AntiClusAl. The method makes use of the commonly use idea of aligning homologous sequences belonging to classes generated by some clustering algorithm, and then continue the alignment process ina bottom-up way along a suitable tree structure. The final result is then read at the root of the tree. Multiple sequence alignment in each cluster makes use of the progressive alignment with the 1-median (center) of the cluster. The 1-median of set S of sequences is the element of S which minimizes the average distance from any other sequence in S. Its exact computation requires quadratic time. The basic idea of our proposed algorithm is to make use of a simple and natural algorithmic technique based on randomized tournaments which has been successfully applied to large size search problems in general metric spaces. In particular a clustering algorithm called Antipole tree and an approximate linear 1-median computation are used. Our algorithm compared with Clustal W, a widely used tool to MSA, shows a better running time results with fully comparable alignment quality. A successful biological application showing high aminoacid conservation during evolution of Xenopus laevis SOD2 is also cited.

Algorithms↗
PubMed2006

SinicView: a visualization environment for comparisons of multiple nucleotide sequence alignment tools.

BACKGROUND: Deluged by the rate and complexity of completed genomic sequences, the need to align longer sequences becomes more urgent, and many more tools have thus been developed. In the initial stage of genomic sequence analysis, a biologist is usually faced with the questions of how to choose the best tool to align sequences of interest and how to analyze and visualize the alignment results, and then with the question of whether poorly aligned regions produced by the tool are indeed not homologous or are just results due to inappropriate alignment tools or scoring systems used. Although several systematic evaluations of multiple sequence alignment (MSA) programs have been proposed, they may not provide a standard-bearer for most biologists because those poorly aligned regions in these evaluations are never discussed. Thus, a tool that allows cross comparison of the alignment results obtained by different tools simultaneously could help a biologist evaluate their correctness and accuracy. RESULTS: In this paper, we present a versatile alignment visualization system, called SinicView, (for Sequence-aligning INnovative and Interactive Comparison VIEWer), which allows the user to efficiently compare and evaluate assorted nucleotide alignment results obtained by different tools. SinicView calculates similarity of the alignment outputs under a fixed window using the sum-of-pairs method and provides scoring profiles of each set of aligned sequences. The user can visually compare alignment results either in graphic scoring profiles or in plain text format of the aligned nucleotides along with the annotations information. We illustrate the capabilities of our visualization system by comparing alignment results obtained by MLAGAN, MAVID, and MULTIZ, respectively. CONCLUSION: With SinicView, users can use their own data sequences to compare various alignment tools or scoring systems and select the most suitable one to perform alignment in the initial stage of sequence analysis.

Algorithms↗
PubMed2002

A novel approach to local reliability of sequence alignments.

MOTIVATION: The pairwise alignment of biological sequences obtained from an algorithm will in general contain both correct and incorrect parts. Hence, to allow for a valid interpretation of the alignment, the local trustworthiness of the alignment has to be quantified. RESULTS: We present a novel approach that attributes a reliability index to every pair of residues, including gapped regions, in the optimal alignment of two protein sequences. The method is based on a fuzzy recast of the dynamic programming algorithm for sequence alignment in terms of mean field annealing. An extensive evaluation with structural reference alignments not only shows that the probability for a pair of residues to be correctly aligned grows consistently with increasing reliability index, but moreover demonstrates that the value of the reliability index can directly be translated into an estimate of the probability for a correct alignment.

Algorithms↗
PubMed2002

Constrained multiple sequence alignment tool development and its application to RNase family alignment.

In this paper, we design an algorithm of computing a constrained multiple sequence alignment (CMSA for short) for guaranteeing that the generated alignment satisfies the user-specified constraints that some particular residues should be aligned together. If the number of residues needed to be aligned together is a constant alpha, then the time-complexity of our CMSA algorithm for aligning K sequences is O (alphaKn4), where n is the maximum of the lengths of sequences. In addition, we have build up such a CMSA software system and made several experiments on the RNase sequences, which mainly function in catalyzing the degradation of RNA molecules. The resulting alignments illustrate the practicability of our method.

Algorithms↗
PubMed1998

DINAMO: a coupled sequence alignment editor/molecular graphics tool for interactive homology modeling of proteins.

Gaining functional information about a novel protein is a universal problem in biomedical research. With the explosive growth of the protein sequence and structural databases, it is becoming increasingly common for researchers to attempt to build a three-dimensional model of their protein of interest in order to gain information about its structure and interactions with other molecules. The two most reliable methods for predicting the structure of a protein are homology modeling, in which the novel sequence is modeled on the known three-dimensional structure of a related protein, and fold recognition (threading), where the sequence is scored against a library of fold models, and the highest scoring model is selected. The sequence alignment to a known structure can be ambiguous, and human intervention is often required to optimize the model. We describe an interactive model building and assessment tool in which a sequence alignment editor is dynamically coupled to a molecular graphics display. By means of a set of assessment tools, the user may optimize his or her alignment to satisfy the known heuristics of protein structure. Adjustments to the sequence alignment made by the user are reflected in the displayed model by color and other visual cues. For instance, residues are colored by hydrophobicity in both the three-dimensional model and in the sequence alignment. This aids the user in identifying undesirable buried polar residues. Several different evaluation metrics may be selected including residue conservation, residue properties, and visualization of predicted secondary structure. These characteristics may be mapped to the model both singly and in combination. DINAMO is a Java-based tool that may be run either over the web or installed locally. Its modular architecture also allows Java-literate users to add plug-ins of their own design.

Amino Acid Sequence↗
PubMed2000

The performance of several multiple-sequence alignment programs in relation to secondary-structure features for an rRNA sequence.

The performances of five global multiple-sequence alignment programs (CLUSTAL W, Divide and Conquer, Malign, PileUp, and TreeAlign) were evaluated using part of the animal mitochondrial small subunit (12S) rRNA molecule. Conserved sequence motifs derived from an alignment based on secondary structural information were used to score how well each program aligned a data set of five vertebrate and five invertebrate taxa over a range of parameter values. All of the programs could align the motifs with reasonable accuracy for at least one set of parameter conditions, although if the whole sequence was considered, similarity to the structural alignment was only 25%-34%. Use of small gap costs generally gave more accurate results, although Malign and TreeAlign generated longer alignments when gap costs were low. The programs differed in the consistency of the alignments when gap cost was varied; CLUSTAL W, Divide and Conquer, and TreeAlign were the most accurate and robust, while PileUp performed poorly as gap cost values increased, and the accuracy of Malign fluctuated. Default settings for the programs did not give the best results, and attempting to select similar parameter values in different programs did not always result in more similar alignments. Poor alignment of even well-conserved motifs can occur if these are near sites with insertions or deletions. Since there is no a priori way to determine gap costs and because such costs can vary over the gene, alignment of rRNA sequences, particularly the less well conserved regions, should be treated carefully and aided by secondary structure and conserved motifs. Some motifs are single bases and so are often invisible to alignment programs. Our tests involved the most conserved regions of the 12S rRNA gene, and alignment of less well conserved regions will be more problematical. None of the alignments we examined produced a fully resolved phylogeny for the data set, indicating that this portion of 12S rRNA is insufficient for resolution of distant evolutionary relationships.

Algorithms↗
PubMed1994

Use of amino acid environment-dependent substitution tables and conformational propensities in structure prediction from aligned sequences of homologous proteins. II. Secondary structures.

A three-step method is presented to predict secondary structures of proteins, by utilizing aligned sequences of homologous proteins. Mean propensities and amino acid substitution patterns at a given site in the aligned sequences are first evaluated for four conformational states (i.e. alpha-helix, beta-strand, buried coil and exposed coil). Capping rules are applied in order to define boundaries of the secondary-structure segments more precisely. In the second step beta-strand is predicted by searching regions predicted as coil for the two patterns characteristic of alternating and fully buried beta-strands. The complete sequences of the solvent-accessibility classes predicted by substitution tables and propensities are also searched using Fourier transform methods for alpha-helical periodicity. After applying capping rules, the alpha-helices and beta-strands predicted in the second step replace, where appropriate, the conformational states predicted in the first step. Finally, in the third step, if one of the four conformational states is assigned to the residues at an equivalent site of aligned sequences in more than a given fraction of the proteins, such a state is reassigned to all the residues at that site. The method is applied to 13 protein families, which contain four folding types, alpha, beta, alpha/beta and alpha + beta. The accuracy of the prediction ranges from 60 to 79% (mean percentage over the 13 families is 69%). For comparison the Garnier-Osguthorpe-Robson (GOR) method is also applied to them. Although the mean prediction accuracy for the GOR method, 58%, can be improved to 63% by applying the second and third steps in this method, there remain four families with less than 55% accuracy. The mean accuracy is relatively higher and poor predictions are reduced in this method.

Amino Acid Sequence↗
PubMed1994

On global sequence alignment.

We present a dynamic programming algorithm for computing a best global alignment of two sequences. The proposed algorithm is robust in identifying any of several global relationships between two sequences. The algorithm delivers a best alignment of two sequences in linear space and quadratic time. We also describe a multiple alignment algorithm based on the pairwise algorithm. Both algorithms have been implemented as portable C programs. Experimental results indicate that for a commonly used set of gap penalties, the new programs produce more satisfactory alignments on sequences of various lengths than some existing pairwise and multiple programs based on the dynamic programming algorithm of Needleman and Wunsch.

Algorithms↗
PubMed2002

CASA: a server for the critical assessment of protein sequence alignment accuracy.

SUMMARY: A public server for evaluating the accuracy of protein sequence alignment methods is presented. CASA is an implementation of the alignment accuracy benchmark presented by Sauder et al. (Proteins, 40, 6-22, 2000). The benchmark currently contains 39321 pairwise protein structure alignments produced with the CE program from SCOP domain definitions. The server produces graphical and tabular comparisons of the accuracy of a user's input sequence alignments with other commonly used programs, such as BLAST, PSI-BLAST, Clustal W, and SAM-T99. AVAILABILITY: The server is located at http://capb.dbi.udel.edu/casa.

Algorithms↗
PubMed2003

Improved Hidden Markov Model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid.

Multiple sequence alignment (MSA) is one of the basic problems in computational biology. Realistic problem instances of MSA are computationally intractable for exact algorithms. One way to tackle MSA is to use Hidden Markov Models (HMMs), which are known to be very powerful in the related problem domain of speech recognition. However, the training of HMMs is computationally hard and there is no known exact method that can guarantee optimal training within reasonable computing time. Perhaps the most powerful training method is the Baum-Welch algorithm, which is fast, but bears the problem of stagnation at local optima. In the study reported in this paper, we used a hybrid algorithm combining particle swarm optimization with evolutionary algorithms to train HMMs for the alignment of protein sequences. Our experiments show that our approach yields better alignments for a set of benchmark protein sequences than the most commonly applied HMM training methods, such as Baum-Welch and Simulated Annealing.

Algorithms↗
PubMed1994

Exploratory analysis of multiple sequence alignments using phylogenies.

The significance of an alignment between two sequences can be determined using well-known techniques but cannot be easily evaluated with multiple alignments due to the computational complexity. Therefore multiple alignment algorithms may produce an alignment between sequences even when they have little homology with other sequences. A program is presented that makes use of a phylogeny to explore the implications of an alignment. Using the phylogeny, branch lengths are inferred and a search is conducted for regions of unusually rapid or slow rates of change given the observed rates in the rest of the sequence. A very rapid rate of change can be due to either poor homology or due to rapid divergence because of selection. Phylogenies are calculated using either the neighbor joining algorithm of Saitou and Nei (Mol. Biol. Evol., 4, 406-425, 1987) or a phylogeny supplied by the user. The program also permits randomization of subsections of the sequences to determine the significance of the multiple alignment for these individual regions. The combination of these two simple methods permits rapid and interactive exploration of multiple sequence alignments.

Algorithms↗
PubMed2001

Structure-based sequence alignment of type-II restriction endonucleases.

The type-II restriction endonucleases generally do not share appreciable amino acid sequence homology. The crystal structures of restriction endonucleases EcoRI and BamHI have shown these enzymes to possess striking 3D-structural resemblance, i.e., they have a similar overall fold and similar active sites, though they possess <23% sequence identity. Structural superimposition of EcoRI, BamHI, EcoRV, and PvuII based on active site residues led to sequence alignments which showed nine possible sequence motifs. EcoRV and PvuII show a more similar pattern than EcoRI and BamHI suggesting that they belong to a different subgroup. The motifs are characterized by charged and/or hydrophobic residues. From other studies on the structure of these endonucleases, three of the motifs could be implicated in DNA binding, three in forming the active site and one in dimer formation. However, the motifs were not identifiable by regular sequence alignment methods. It is found that motif IX in BamHI is formed by reverse sequence order and the motif IX in PvuII is formed from the symmetry related monomer of the dimer. The inter-motif distance is also quite different in these cases. Of the nine motifs, motif III has been earlier identified as containing the PD motif involving one of the active site residues. These motifs were used in a modified profile analysis procedure to identify similar regions in eight other endonuclease sequences for which structures are not known.

Amino Acid Sequence↗
PubMed2006

ARCS: an aggregated related column scoring scheme for aligned sequences.

MOTIVATION: Biologists frequently align multiple biological sequences to determine consensus sequences and/or search for predominant residues and conserved regions. Particularly, determining conserved regions in an alignment is one of the most important activities. Since protein sequences are often several-hundred residues or longer, it is difficult to distinguish biologically important conserved regions (motifs or domains) from others. The widely used tools, Logos, Al2co, Confind, and the entropy-based method, often fail to highlight such regions. Thus a computational tool that can highlight biologically important regions accurately will be highly desired. RESULTS: This paper presents a new scoring scheme ARCS (Aggregated Related Column Score) for aligned biological sequences. ARCS method considers not only the traditional character similarity measure but also column correlation. In an extensive experimental evaluation using 533 PROSITE patterns, ARCS is able to highlight the motif regions with up to 77.7% accuracy corresponding to the top three peaks. AVAILABILITY: The source code is available on http://bio.informatics.indiana.edu/projects/arcs and http://goldengate.case.edu/projects/arcs

Algorithms↗
PubMed2004

Consensus folding of aligned sequences as a new measure for the detection of functional RNAs by comparative genomics.

Facing the ever-growing list of newly discovered classes of functional RNAs, it can be expected that further types of functional RNAs are still hidden in recently completed genomes. The computational identification of such RNA genes is, therefore, of major importance. While most known functional RNAs have characteristic secondary structures, their free energies are generally not statistically significant enough to distinguish RNA genes from the genomic background. Additional information is required. Considering the wide availability of new genomic data of closely related species, comparative studies seem to be the most promising approach. Here, we show that prediction of consensus structures of aligned sequences can be a significant measure to detect functional RNAs. We report a new method to test multiple sequence alignments for the existence of an unusually structured and conserved fold. We show for alignments of six types of well-known functional RNA that an energy score consisting of free energy and a covariation term significantly improves sensitivity compared to single sequence predictions. We further test our method on a number of non-coding RNAs from Caenorhabditis elegans/Caenorhabditis briggsae and seven Saccharomyces species. Most RNAs can be detected with high significance. We provide a Perl implementation that can be used readily to score single alignments and discuss how the methods described here can be extended to allow for efficient genome-wide screens.

Algorithms↗
PubMed2004

Integrating protein secondary structure prediction and multiple sequence alignment.

Modern protein secondary structure prediction methods are based on exploiting evolutionary information contained in multiple sequence alignments. Critical steps in the secondary structure prediction process are (i) the selection of a set of sequences that are homologous to a given query sequence, (ii) the choice of the multiple sequence alignment method, and (iii) the choice of the secondary structure prediction method. Because of the close relationship between these three steps and their critical influence on the prediction results, secondary structure prediction has received increased attention from the bioinformatics community over the last few years. In this treatise, we discuss recent developments in computational methods for protein secondary structure prediction and multiple sequence alignment, focus on the integration of these methods, and provide some recommendations for state-of-the-art secondary structure prediction in practice.

Amino Acid Sequence↗
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↗
PubMed2003

Constrained multiple sequence alignment tool development and its application to RNase family alignment.

In this paper, we design a heuristic algorithm of computing a constrained multiple sequence alignment (CMSA for short) for guaranteeing that the generated alignment satisfies the user-specified constraints that some particular residues should be aligned together. If the number of residues needed to be aligned together is a constant alpha, then the time-complexity of our CMSA algorithm for aligning K sequences is O(alphaKn(4)), where n is the maximum of the lengths of sequences. In addition, we have built up such a CMSA software system and made several experiments on the RNase sequences, which mainly function in catalyzing the degradation of RNA molecules. The resulting alignments illustrate the practicability of our method.

Algorithms↗