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PubMed2003

RevTrans: Multiple alignment of coding DNA from aligned amino acid sequences.

The simple fact that proteins are built from 20 amino acids while DNA only contains four different bases, means that the 'signal-to-noise ratio' in protein sequence alignments is much better than in alignments of DNA. Besides this information-theoretical advantage, protein alignments also benefit from the information that is implicit in empirical substitution matrices such as BLOSUM-62. Taken together with the generally higher rate of synonymous mutations over non-synonymous ones, this means that the phylogenetic signal disappears much more rapidly from DNA sequences than from the encoded proteins. It is therefore preferable to align coding DNA at the amino acid level and it is for this purpose we have constructed the program RevTrans. RevTrans constructs a multiple DNA alignment by: (i) translating the DNA; (ii) aligning the resulting peptide sequences; and (iii) building a multiple DNA alignment by 'reverse translation' of the aligned protein sequences. In the resulting DNA alignment, gaps occur in groups of three corresponding to entire codons, and analogous codon positions are therefore always lined up. These features are useful when constructing multiple DNA alignments for phylogenetic analysis. RevTrans also accepts user-provided protein alignments for greater control of the alignment process. The RevTrans web server is freely available at http://www.cbs.dtu.dk/services/RevTrans/.

Amino Acid Substitution↗
PubMed2005

Bayesian models and Markov chain Monte Carlo methods for protein motifs with the secondary characteristics.

Statistical methods have been developed for finding local patterns, also called motifs, in multiple protein sequences. The aligned segments may imply functional or structural core regions. However, the existing methods often have difficulties in aligning multiple proteins when sequence residue identities are low (e.g., less than 25%). In this article, we develop a Bayesian model and Markov chain Monte Carlo (MCMC) methods for identifying subtle motifs in protein sequences. Specifically, a motif is defined not only in terms of specific sites characterized by amino acid frequency vectors, but also as a combination of secondary characteristics such as hydrophobicity, polarity, etc. Markov chain Monte Carlo methods are proposed to search for a motif pattern with high posterior probability under the new model. A special MCMC algorithm is developed, involving transitions between state spaces of different dimensions. The proposed methods were supported by a simulated study. It was then tested by two real datasets, including a group of helix-turn-helix proteins, and one set from the CATH Protein Structure Classification Database. Statistical comparisons showed that the new approach worked better than a typical Gibbs sampling approach which is based only on an amino acid model.

Amino Acid Motifs↗
PubMed2005

transAlign: using amino acids to facilitate the multiple alignment of protein-coding DNA sequences.

BACKGROUND: Alignments of homologous DNA sequences are crucial for comparative genomics and phylogenetic analysis. However, multiple alignment represents a computationally difficult problem. For protein-coding DNA sequences, it is more advantageous in terms of both speed and accuracy to align the amino-acid sequences specified by the DNA sequences rather than the DNA sequences themselves. Many implementations making use of this concept of "translated alignments" are incomplete in the sense that they require the user to manually translate the DNA sequences and to perform the amino-acid alignment. As such, they are not well suited to large-scale automated alignments of large and/or numerous DNA data sets. RESULTS: transAlign is an open-source Perl script that aligns protein-coding DNA sequences via their amino-acid translations to take advantage of the superior multiple-alignment capabilities and speed of an amino-acid alignment. It operates by translating each DNA sequence into its corresponding amino-acid sequence, passing the entire matrix to ClustalW for alignment, and then back-translating the resulting amino-acid alignment to derive the aligned DNA sequences. In the translation step, transAlign determines the optimal orientation and reading frame for each DNA sequence according to the desired genetic code. It also checks for apparent frame shifts in the DNA sequences and can handle frame-shifted sequences in one of three ways (delete, align as amino acids regardless, or profile align as DNA). As a set of comparative benchmarks derived from six protein-coding genes for mammals shows, the strategy implemented in transAlign always improves the speed and usually the apparent accuracy of the alignment of protein-coding DNA sequences. CONCLUSION: transAlign represents one of few full and cross-platform implementations of the concept of translated alignments. Both the advantages accruing from performing a translated alignment and the suite of user-definable options available in the program mean that transAlign is ideally suited for large-scale automated alignments of very large and/or very numerous protein-coding DNA data sets. However, the good performance offered by the program also translates to the alignment of any set of protein-coding sequences. transAlign, including the source code, is freely available at http://www.tierzucht.tum.de/Bininda-Emonds/ (under "Programs").

Algorithms↗
PubMed2001

CHROMA: consensus-based colouring of multiple alignments for publication.

CHROMA annotates multiple protein sequence alignments by consensus to produce formatted and coloured text suitable for incorporation into other documents for publication. The package is designed to be flexible and reliable, and has a simple-to-use graphical user interface running under Microsoft Windows. Both the executables and source code for CHROMA running under Windows and Linux (portable command-line only) are freely available at http://www.lg.ndirect.co.uk/chroma. Software enquiries should be directed to CHROMA@lg.ndirect.co.uk.

Amino Acid Sequence↗
PubMed2006

STRAL: progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time.

MOTIVATION: Alignment of RNA has a wide range of applications, for example in phylogeny inference, consensus structure prediction and homology searches. Yet aligning structural or non-coding RNAs (ncRNAs) correctly is notoriously difficult as these RNA sequences may evolve by compensatory mutations, which maintain base pairing but destroy sequence homology. Ideally, alignment programs would take RNA structure into account. The Sankoff algorithm for the simultaneous solution of RNA structure prediction and RNA sequence alignment was proposed 20 years ago but suffers from its exponential complexity. A number of programs implement lightweight versions of the Sankoff algorithm by restricting its application to a limited type of structure and/or only pairwise alignment. Thus, despite recent advances, the proper alignment of multiple structural RNA sequences remains a problem. RESULTS: Here we present StrAl, a heuristic method for alignment of ncRNA that reduces sequence-structure alignment to a two-dimensional problem similar to standard multiple sequence alignment. The scoring function takes into account sequence similarity as well as up- and downstream pairing probability. To test the robustness of the algorithm and the performance of the program, we scored alignments produced by StrAl against a large set of published reference alignments. The quality of alignments predicted by StrAl is far better than that obtained by standard sequence alignment programs, especially when sequence homologies drop below approximately 65%; nevertheless StrAl's runtime is comparable to that of ClustalW.

Algorithms↗
PubMed1997

Multiple sequence threading: an analysis of alignment quality and stability.

Methods that compare a protein sequence directly to a structure can be divided into those that construct a molecular model (threading methods) and those that perform a sequence alignment with the structure encoded as a sequence of structural states (one-dimensional/three-dimensional (1D/3D) matching). The former take into account the internal packing of the molecule but the latter do not. On the other hand, it is simple to include multiple sequence data in a 1D/3D comparison but difficult in a threading method. Here, a protein sequence/structure alignment method is described that uses a combination of matching predicted and observed residue exposure, predicted and observed secondary structure (1D/3D) together with pairwise packing interactions in the core (threading). Using a variety of distantly related and analogous protein structures, the multiple sequence threading (MST) method was compared to a single sequence threading (SST) method (that uses complex potentials of mean-force) and also to a multiple sequence alignment (MSA) program. It was found that the MST method produced alignments that were better than the best that could be obtained with either the SST or MSA method. The method was found to be stable to error in both secondary structure prediction and predicted exposure and also under variation of the key parameters (fully described in an Appendix). The contribution of the pairwise term was found to be small but without it, the correct alignments were less stable and structurally unreasonable deletions were observed when matching against larger structures. Using the parameters derived for alignment, the method was able to recognise related folds in the structure databank with a specificity comparable to other methods.

Algorithms↗
PubMed2004

Gapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model.

BACKGROUND: Certain protein families are highly conserved across distantly related organisms and belong to large and functionally diverse superfamilies. The patterns of conservation present in these protein sequences presumably are due to selective constraints maintaining important but unknown structural mechanisms with some constraints specific to each family and others shared by a larger subset or by the entire superfamily. To exploit these patterns as a source of functional information, we recently devised a statistically based approach called contrast hierarchical alignment and interaction network (CHAIN) analysis, which infers the strengths of various categories of selective constraints from co-conserved patterns in a multiple alignment. The power of this approach strongly depends on the quality of the multiple alignments, which thus motivated development of theoretical concepts and strategies to improve alignment of conserved motifs within large sets of distantly related sequences. RESULTS: Here we describe a hidden Markov model (HMM), an algebraic system, and Markov chain Monte Carlo (MCMC) sampling strategies for alignment of multiple sequence motifs. The MCMC sampling strategies are useful both for alignment optimization and for adjusting position specific background amino acid frequencies for alignment uncertainties. Associated statistical formulations provide an objective measure of alignment quality as well as automatic gap penalty optimization. Improved alignments obtained in this way are compared with PSI-BLAST based alignments within the context of CHAIN analysis of three protein families: Gialpha subunits, prolyl oligopeptidases, and transitional endoplasmic reticulum (p97) AAA+ ATPases. CONCLUSION: While not entirely replacing PSI-BLAST based alignments, which likewise may be optimized for CHAIN analysis using this approach, these motif-based methods often more accurately align very distantly related sequences and thus can provide a better measure of selective constraints. In some instances, these new approaches also provide a better understanding of family-specific constraints, as we illustrate for p97 ATPases. Programs implementing these procedures and supplementary information are available from the authors.

Amino Acid Motifs↗
PubMed2005

jPHYDIT: a JAVA-based integrated environment for molecular phylogeny of ribosomal RNA sequences.

jPHYDIT is a Java application designed to furnish a visual and integrated environment for molecular phylogeny. The program can be used to visualize intra-strand base-pairing information in secondary and tertiary structures of ribosomal RNA (rRNA) sequences. A function for the semi-automated alignment was included to facilitate handling of the database containing a large number of multiple-aligned rRNA sequences. Integration of nucleotide sequence editing, pairwise alignment, multiple alignment and phylogenetic treeing functions provide an easy and efficient way of analyzing rRNA sequences for molecular evolution, systematics, epidemiology and ecology.

Algorithms↗
PubMedDate not supplied

Structure-based multiple alignment of extracellular pectate lyase sequences.

Pectate lyases are secreted virulence factors which degrade the pectate component of plant cell walls. The evolutionary-based multiple alignment of extracellular pectate lyases has been corrected using three-dimensional structural information derived from Erwinia chyrsanthemi pectate lyases C and E. The new multiple alignment reveals invariant amino acids likely to be involved in two different enzymatic functions.

Amino Acid Sequence↗
PubMed1997

Analysis of comparative modeling predictions for CASP2 targets 1, 3, 9, and 17.

Comparative modeling targets 1, 3, 9 and 17 were predicted by alignment of multiple sequences and structures, when available, followed by minimization using the program AMMP. The minimization used improved potentials, and distance restraints for regions of common structure. New prediction procedures were evaluated. Three tested solvent corrections did not significantly improve the predictions. Target 17 had 85.3% sequence identity with the parent and no insertions or deletions. The prediction had a root-mean-square deviation from target 17 of 0.56 A on C alpha atoms, and 0.59 A for the ligand atoms, which verified the accuracy of the minimization. Targets 1, 3, and 9 had 36.4%, 46.7%, and 33.3% identity with the parent sequences, and predictions resulted in root-mean-square deviations for 79-85% of C alpha atoms of 1.49, 1.11, and 1.24 A, respectively. Conformational differences between parent and target crystal structures were difficult to predict. The use of distance restraints and multiple structures improved the positioning of gaps in sequence alignment. Distance restraints did not overcome errors in sequence alignment or ambiguities due to conformational variation in proteins. Predictions for targets 3 and 9 successfully reduced large deviations between parent and target structures.

Animals↗
PubMed2003

GPRM: A genetic programming approach to finding common RNA secondary structure elements.

RNA molecules play an important role in many biological activities. Knowing its secondary structure can help us better understand the molecule's ability to function. The methods for RNA structure determination have traditionally been implemented through biochemical, biophysical and phylogenetic analyses. As the advance of computer technology, an increasing number of computational approaches have recently been developed. They have different goals and apply various algorithms. For example, some focus on secondary structure prediction for a single sequence; some aim at finding a global alignment of multiple sequences. Some predict the structure based on free energy minimization; some make comparative sequence analyses to determine the structure. In this paper, we describe how to correctly use GPRM, a genetic programming approach to finding common secondary structure elements in a set of unaligned coregulated or homologous RNA sequences. GPRM can be accessed at http://bioinfo.cis.nctu.edu.tw/service/gprm/.

Internet↗
PubMed2004

Mauve: multiple alignment of conserved genomic sequence with rearrangements.

As genomes evolve, they undergo large-scale evolutionary processes that present a challenge to sequence comparison not posed by short sequences. Recombination causes frequent genome rearrangements, horizontal transfer introduces new sequences into bacterial chromosomes, and deletions remove segments of the genome. Consequently, each genome is a mosaic of unique lineage-specific segments, regions shared with a subset of other genomes and segments conserved among all the genomes under consideration. Furthermore, the linear order of these segments may be shuffled among genomes. We present methods for identification and alignment of conserved genomic DNA in the presence of rearrangements and horizontal transfer. Our methods have been implemented in a software package called Mauve. Mauve has been applied to align nine enterobacterial genomes and to determine global rearrangement structure in three mammalian genomes. We have evaluated the quality of Mauve alignments and drawn comparison to other methods through extensive simulations of genome evolution.

Chromosomes, Bacterial↗
PubMed1992

Evolutionary divergence plots of homologous proteins.

A simple and efficient method is described for analyzing quantitatively multiple protein sequence alignments and finding the most conserved blocks as well as the maxima of divergence within the set of aligned sequences. It consists of calculating the mean distance and the root-mean-square distance in each column of the multiple alignment, averaging the values in a window of defined length and plotting the results as a function of the position of the window. Due attention is paid to the presence of gaps in the columns. Several examples are provided, using the sequences of several cytochromes c, serine proteases, lysozymes and globins. Two distance matrices are compared, namely the matrix derived by Gribskov and Burgess from the Dayhoff matrix, and the Risler Structural Superposition Matrix. In each case, the divergence plots effectively point to the specific residues which are known to be essential for the catalytic activity of the proteins. In addition, the regions of maximum divergence are clearly delineated. Interestingly, they are generally observed in positions immediately flanking the most conserved blocks. The method should therefore be useful for delineating the peptide segments which will be good candidates for site-directed mutagenesis and for visualizing the evolutionary constraints along homologous polypeptide chains.

Amino Acid Sequence↗
PubMed2005

Ancestral sequence alignment under optimal conditions.

BACKGROUND: Multiple genome alignment is an important problem in bioinformatics. An important subproblem used by many multiple alignment approaches is that of aligning two multiple alignments. Many popular alignment algorithms for DNA use the sum-of-pairs heuristic, where the score of a multiple alignment is the sum of its induced pairwise alignment scores. However, the biological meaning of the sum-of-pairs of pairs heuristic is not obvious. Additionally, many algorithms based on the sum-of-pairs heuristic are complicated and slow, compared to pairwise alignment algorithms. An alternative approach to aligning alignments is to first infer ancestral sequences for each alignment, and then align the two ancestral sequences. In addition to being fast, this method has a clear biological basis that takes into account the evolution implied by an underlying phylogenetic tree. In this study we explore the accuracy of aligning alignments by ancestral sequence alignment. We examine the use of both maximum likelihood and parsimony to infer ancestral sequences. Additionally, we investigate the effect on accuracy of allowing ambiguity in our ancestral sequences. RESULTS: We use synthetic sequence data that we generate by simulating evolution on a phylogenetic tree. We use two different types of phylogenetic trees: trees with a period of rapid growth followed by a period of slow growth, and trees with a period of slow growth followed by a period of rapid growth. We examine the alignment accuracy of four ancestral sequence reconstruction and alignment methods: parsimony, maximum likelihood, ambiguous parsimony, and ambiguous maximum likelihood. Additionally, we compare against the alignment accuracy of two sum-of-pairs algorithms: ClustalW and the heuristic of Ma, Zhang, and Wang. CONCLUSION: We find that allowing ambiguity in ancestral sequences does not lead to better multiple alignments. Regardless of whether we use parsimony or maximum likelihood, the success of aligning ancestral sequences containing ambiguity is very sensitive to the choice of gap open cost. Surprisingly, we find that using maximum likelihood to infer ancestral sequences results in less accurate alignments than when using parsimony to infer ancestral sequences. Finally, we find that the sum-of-pairs methods produce better alignments than all of the ancestral alignment methods.

Data Interpretation, Statistical↗
PubMed2006

A heuristic Bayesian method for segmenting DNA sequence alignments and detecting evidence for recombination and gene conversion.

We propose a heuristic approach to the detection of evidence for recombination and gene conversion in multiple DNA sequence alignments. The proposed method consists of two stages. In the first stage, a sliding window is moved along the DNA sequence alignment, and phylogenetic trees are sampled from the conditional posterior distribution with MCMC. To reduce the noise intrinsic to inference from the limited amount of data available in the typically short sliding window, a clustering algorithm based on the Robinson-Foulds distance is applied to the trees thus sampled, and the posterior distribution over tree clusters is obtained for each window position. While changes in this posterior distribution are indicative of recombination or gene conversion events, it is difficult to decide when such a change is statistically significant. This problem is addressed in the second stage of the proposed algorithm, where the distributions obtained in the first stage are post-processed with a Bayesian hidden Markov model (HMM). The emission states of the HMM are associated with posterior distributions over phylogenetic tree topology clusters. The hidden states of the HMM indicate putative recombinant segments. Inference is done in a Bayesian sense, sampling parameters from the posterior distribution with MCMC. Of particular interest is the determination of the number of hidden states as an indication of the number of putative recombinant regions. To this end, we apply reversible jump MCMC, and sample the number of hidden states from the respective posterior distribution.

Actins↗
PubMed2004

Rapid detection of conserved regions in protein sequences using wavelets.

We present an algorithm to detect protein sub-structural motifs from primary sequence. The input to the algorithm is a set of aligned multiple protein sequences. It uses wavelet transforms to decompose protein sequences represented numerically by different indices (such as polarity, accessible surface area or electron-ion integration potentials of the amino acids). The numerical representation of a protein sequence has significant correlation with its biological activity, thus common motifs are expected to be observable from the wavelet spectrum. The decomposed signals are then up-sampled and similarity search techniques are used to identify similar regions across all the proteins at multiple scales. Results indicate that wavelet transform techniques are a promising approach for rapid motif detection.

Algorithms↗
PubMed2005

Joint Bayesian estimation of alignment and phylogeny.

We describe a novel model and algorithm for simultaneously estimating multiple molecular sequence alignments and the phylogenetic trees that relate the sequences. Unlike current techniques that base phylogeny estimates on a single estimate of the alignment, we take alignment uncertainty into account by considering all possible alignments. Furthermore, because the alignment and phylogeny are constructed simultaneously, a guide tree is not needed. This sidesteps the problem in which alignments created by progressive alignment are biased toward the guide tree used to generate them. Joint estimation also allows us to model rate variation between sites when estimating the alignment and to use the evidence in shared insertion/deletions (indels) to group sister taxa in the phylogeny. Our indel model makes use of affine gap penalties and considers indels of multiple letters. We make the simplifying assumption that the indel process is identical on all branches. As a result, the probability of a gap is independent of branch length. We use a Markov chain Monte Carlo (MCMC) method to sample from the posterior of the joint model, estimating the most probable alignment and tree and their support simultaneously. We describe a new MCMC transition kernel that improves our algorithm's mixing efficiency, allowing the MCMC chains to converge even when started from arbitrary alignments. Our software implementation can estimate alignment uncertainty and we describe a method for summarizing this uncertainty in a single plot.

Algorithms↗
PubMed1997

RAGA: RNA sequence alignment by genetic algorithm.

We describe a new approach for accurately aligning two homologous RNA sequences when the secondary structure of one of them is known. To do so we developed two software packages, called RAGA and PRAGA, which use a genetic algorithm approach to optimize the alignments. RAGA is mainly an extension of SAGA, an earlier package for multiple protein sequence alignment. In PRAGA several genetic algorithms run in parallel and exchange individual solutions. This method allows us to optimize an objective function that describes the quality of a RNA pairwise alignment, taking into account both primary and secondary structure, including pseudoknots. We report results obtained using PRAGA on nine test cases of pairs of eukaryotic small subunit rRNA sequence (nuclear and mitochondrial).

Algorithms↗