Aligning multiple protein sequences by parallel hybrid genetic algorithm.

Multiple protein sequence alignment using double-dynamic programming

Multiple protein sequence alignment using double-dynamic programming.

In this paper, we report a parallel hybrid genetic algorithm (HGA) on consumer-level graphics cards. HGA extends the classical genetic algorithm by incorporating the Cauchy mutation operator from evolutionary programming. In our parallel HGA, all steps except the random number generation procedure are performed in graphics processing unit (GPU) and thus our parallel HGA can be executed effectively and efficiently. We propose the pseudo-deterministic selection method which is comparable to the traditional global selection approach with significant execution time performance advantages. We perform experiments to compare our parallel HGA with our previous parallel FEP (fast evolutionary programming) and demonstrate that the former is much more effective and efficient than the latter. The parallel and sequential implementations of HGA are compared in a number of experiments, it is observed that the former outperforms the latter significantly. The effectiveness and efficiency of the pseudo-deterministic selection method is also studied.

We present MUSCLE, a new program for creating multiple alignments of protein sequences. MUSCLE achieves the highest scores so far reported on four alignment benchmarks: Balibase, PREFAB, SABmark and SMART, achieving accuracy from 1% to 2.5% higher than T-Coffee and execution times that are generally lower than CLUSTALW for typical input data. With options designed for high-throughput applications, MUSCLE gives average accuracy statistically indistinguishable from T-Coffee and is the fastest published method for large numbers of sequences, able to align 5,000 sequences of length 300 in 7 minutes on a desktop computer. MUSCLE is freely available at http://www.drive5.com/muscle.

An algorithm is presented for the accurate and rapid generation of multiple protein sequence alignments from tertiary structure comparisons. A preliminary multiple sequence alignment is performed using sequence information, which then determines an initial superposition of the structures. A structure comparison algorithm is applied to all pairs of proteins in the superimposed set and a similarity tree calculated. Multiple sequence alignments are then generated by following the tree from the branches to the root. At each branchpoint of the tree, a structure-based sequence alignment and coordinate transformations are output, with the multiple alignment of all structures output at the root. The algorithm encoded in STAMP (STructural Alignment of Multiple Proteins) is shown to give alignments in good agreement with published structural accounts within the dehydrogenase fold domains, globins, and serine proteinases. In order to reduce the need for visual verification, two similarity indices are introduced to determine the quality of each generated structural alignment. Sc quantifies the global structural similarity between pairs or groups of proteins, whereas Pij' provides a normalized measure of the confidence in the alignment of each residue. STAMP alignments have the quality of each alignment characterized by Sc and Pij' values and thus provide a reproducible resource for studies of residue conservation within structural motifs.

This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA) problem. MSA is one of the most important tasks in biological sequence analysis. Although this paper focuses on protein alignments, most of the discussion and methodology may also be applied to DNA alignments. The problem of finding the multiple alignment was investigated in the study by Bonizzoni and Vedova and Wang and Jiang, and proved to be a NP-hard (non-deterministic polynomial-time hard) problem. The presented algorithm, called Immunological Multiple Sequence Alignment Algorithm (IMSA), incorporates two new strategies to create the initial population and specific ad hoc mutation operators. It is based on the ‘weighted sum of pairs’ as objective function, to evaluate a given candidate alignment. IMSA was tested using both classical benchmarks of BAliBASE (versions 1.0, 2.0 and 3.0), and experimental results indicate that it is comparable with state-of-the-art multiple alignment algorithms, in terms of quality of alignments, weighted Sums-of-Pairs (SP) and Column Score (CS) values. The main novelty of IMSA is its ability to generate more than a single suboptimal alignment, for every MSA instance; this behaviour is due to the stochastic nature of the algorithm and of the populations evolved during the convergence process. This feature will help the decision maker to assess and select a biologically relevant multiple sequence alignment. Finally, the designed algorithm can be used as a local search procedure to properly explore promising alignments of the search space.

Multiple sequence alignment with the Divide-and-Conquer method.

Alignment of multiple protein or DNA sequences is an important problem in bioinformatics. Previous work has shown that the A* search algorithm can find optimal alignments for up to several sequences, and that a K-group generalization of A* can find approximate alignments for much larger numbers of sequences [T. Ikeda et al. (1999)]. In this paper, we describe the first implementation of K-group A* that uses quasinatural gap costs, the cost model used in practice by biologists. We also introduce a new method for computing gap-opening costs in profile alignment. Our results show that K-group A* can efficiently find optimal or close-to-optimal alignments for small groups of sequences, and, for large numbers of sequences, it can find higher-quality alignments than the widely-used CLUSTAL family of approximate alignment tools. This demonstrates the benefits of A* in aligning large numbers of sequences, as typically compared by biologists, and suggests that K-group A* could become a practical tool for multiple sequence alignment.

With the development of new sequencing techniques, whole genomes of many species have become available. This huge amount of data gives rise to new opportunities and challenges. These new sequences provide valuable information on relationships among species, e.g. genome recombination and conservation. One of the principal ways to investigate such information is multiple sequence alignment (MSA). Currently, there is large amount of MSA data on the internet, such as the UCSC genome database, but how to effectively use this information to solve classical and new problems is still an area lacking of exploration. In this thesis, we explored how to use this information in four problems, i.e. sequence orthology search problem, multiple alignment improvement problem, short read mapping problem, and genome rearrangement inference problem. For the first problem, we developed a EM algorithm to iteratively align a query with a multiple alignment database with the information from a phylogeny relating the query species and the species in the multiple alignment. We also infer the query's location in the phylogeny. We showed that by doing alignment and phylogeny inference together, we can improve the accuracies for both problems. For the second problem, we developed an optimization algorithm to iteratively refine the multiple alignment quality. Experiment results showed our algorithm is very stable in term of resulting alignments. The results showed that our method is more accurate than existing methods, i.e. Mafft, Clustal-O, and Mavid, on test data from three sets of species from the UCSC genome database. For the third problem, we developed a model, PhyMap, to align a read to a multiple alignment allowing mismatches and indels. PhyMap computes local alignments of a query sequence against a fixed multiple-genome alignment of closely related species. PhyMap uses a known phylogenetic tree on the species in the multiple alignment to improve the quality of its computed alignments while also estimating the placement of the query on this tree. Both theoretical computation and experiment results show that our model can differentiate between orthologous and paralogous alignments better than other popular short read mapping tools (BWA, BOWTIE and BLAST). For the fourth problem, we gave a simple genome recombination model which can express insertions, deletions, inversions, translocations and inverted translocations on aligned genome segments. We also developed an MCMC algorithm to infer the order of the query segments. We proved that using any Euclidian metrics to measure distance between two sequence orders in the tree optimization goal function will lead to a degenerated solution where the inferred order will be the order of one of the leaf nodes. We also gave a graph-based formulation of the problem which can represent the probability distribution of the order of the query sequences.

The object of this work was to improve multiple sequence alignments using public-domain software and methods as far as possible. A method is described where the secondary structure of proteins is predicted and this information, coupled with a simplified description of the amino acids, is used to produce multiple sequence alignments. This method improved the accuracy of the resulting alignments by between 5 and 14% when compared with full sequence profile alignments (as scored against structural alignments). These improved alignments were used to predict the secondary structure of the sequences they contain. The resultant predictions were more accurate than those produced from less optimal alignments. An improvement of 6% for a three-state (helix, sheet and coil) prediction was observed when using the best alignment from the method presented here and the alignment obtained using sequence only. The method makes use of public domain software and all the associated files required to repeat the work are available from the primary author.

A program for the multiple alignment of protein sequences is presented. The program is an extension of the fast alignment program by Wilbur et al. (1984) into higher dimensions. The use of hash procedures on fragments of the protein sequences increases the speed of calculation. Thereby we also take into account fragments which are present in some, but not in all, sequences considered. The results of some multiple alignments are given.

An automatic sequence searching method (ProtEST) is described which constructs multiple protein sequence alignments from protein sequences and translated expressed sequence tags (ESTs). ProtEST is more effective than a simple TBLASTN search of the query against the EST database, as the sequences are automatically clustered, assembled, made non-redundant, checked for sequence errors, translated into protein and then aligned and displayed. A ProtEST search found a non-redundant, translated, error- and length-corrected EST sequence for > 58% of sequences when single sequences from 1407 Pfam-A seed alignments were used as the probe. The average family size of the resulting alignments of translated EST sequences contained > 10 sequences. In a cross-validated test of protein secondary structure prediction, alignments from the new procedure led to an improvement of 3.4% average Q3 prediction accuracy over single sequences. The ProtEST method is available as an Internet World Wide Web service http://barton.ebi.ac.uk/servers/protest.html+ ++ The Wise2 package for protein and genomic comparisons and the ProtESTWise script can be found at http://www.sanger.ac.uk/Software/Wise2 geoff@ebi.ac.uk

Simulating multiple sequence alignments (MSAs) using probabilistic models of sequence evolution plays an important role in the evaluation of phylogenetic inference tools and is crucial to the development of novel learning-based approaches for phylogenetic reconstruction, for instance, neural networks. These models and the resulting simulated data need to be as realistic as possible to be indicative of the performance of the developed tools on empirical data and to ensure that neural networks trained on simulations perform well on empirical data. Over the years, numerous models of evolution have been published with the goal to represent as faithfully as possible the sequence evolution process and thus simulate empirical-like data. In this study, we simulated DNA and protein MSAs under increasingly complex models of evolution with and without insertion/deletion (indel) events using a state-of-the-art sequence simulator. We assessed their realism by quantifying how accurately supervised learning methods are able to predict whether a given MSA is simulated or empirical. Our results show that we can distinguish between empirical and simulated MSAs with high accuracy using two distinct and independently developed classification approaches across all tested models of sequence evolution. Our findings suggest that the current state-of-the-art models fail to accurately replicate several aspects of empirical MSAs, including site-wise rates as well as amino acid and nucleotide composition.

To understand the functional elements of a protein structure biologists use domain specific 3D viewers (PDB) that are written to process the coordinates of atoms that represent the solved protein structure using X-Ray crystallography or NMR. The PDB viewers have been written to capture specific or common features of interest to the researcher. With the explosion of protein sequence data comparative studies and statistical analysis of data can indicate regions of interest in 3D models. The ability to integrate statistical data into existing PDB viewers is difficult because the software is typically written to accomplish very specific functional goals and does not support exporting to a standard 3D format. In this paper, the PDB data is shown as X3D PDB ribbon models that are augmented with statistically significant data and compared to an Information-Rich Virtual Environment represented as a Protein CorreLogo X3D model.A protein family (Pfam) represents multiple alignments of protein sequences where protein domains and the tertiary structures have evolutionary conserved regions representing protein function. Various information properties of the protein family, the tertiary structure from a sequence's PDB structure and ligand binding pockets are combined to create a 3D Protein CorreLogo model. The multiple sequence alignment from the protein family is used to detect co-evolving amino acid pairs using mutual information. Co-evolving pairs are indicated as a column with color coding to represent the physio-chemical properties of each co-evolving amino acid combination. Additional visualizations along each axis include the 2D sequence logo, the degree of insert regions in the protein family and the surface accessibility of each amino acid for the referenced PDB sequence. The Protein CorreLogo model is based on X3D (VRML) facilitating immersive viewing of complex data relationships and detected co-evolving pairs.Two protein families are presented in the results section that compare the Protein CorreLogo model with a representative X3D RDB ribbon model showing the structural significance of predicted co-evolving amino acid pairs using mutual information. One example protein family, with proteins that bind cyclic nucleotides (PF00027.18), is given where the co-evolving pairs are potential markers for ligand binding pocket regions. Another example protein family, with SH3 domains that are involved in signal transduction related to cytoskeletal organization (PF00018.16), shows significant mutual information occurring between two pairs of amino acids that are in contact in the intertwined dimer structure but are on opposite ends of the tertiary structure.Protein CorreLogo X3D models and X3D PDB ribbon models can be found at http://www.proteinx3d.com

BackgroundThere have been many algorithms and software programs implemented for the inference of multiple sequence alignments of protein and DNA sequences. The "true" alignment is usually unknown due to the incomplete knowledge of the evolutionary history of the sequences, making it difficult to gauge the relative accuracy of the programs.ResultsWe tested nine of the most often used protein alignment programs and compared their results using sequences generated with the simulation software Simprot which creates known alignments under realistic and controlled evolutionary scenarios. We have simulated more than 30000 alignment sets using various evolutionary histories in order to define strengths and weaknesses of each program tested. We found that alignment accuracy is extremely dependent on the number of insertions and deletions in the sequences, and that indel size has a weaker effect. We also considered benchmark alignments from the latest version of BAliBASE and the results relative to BAliBASE- and Simprot-generated data sets were consistent in most cases.ConclusionOur results indicate that employing Simprot's simulated sequences allows the creation of a more flexible and broader range of alignment classes than the usual methods for alignment accuracy assessment. Simprot also allows for a quick and efficient analysis of a wider range of possible evolutionary histories that might not be present in currently available alignment sets. Among the nine programs tested, the iterative approach available in Mafft (L-INS-i) and ProbCons were consistently the most accurate, with Mafft being the faster of the two.