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We present the Scalable Nucleotide Alignment Program (SNAP), a new short and long read aligner that is both more accurate (i.e., aligns more reads with fewer errors) and 10-100x faster than state-of-the-art tools such as BWA. Unlike recent aligners based on the Burrows-Wheeler transform, SNAP uses a simple hash index of short seed sequences from the genome, similar to BLAST's. However, SNAP greatly reduces the number and cost of local alignment checks performed through several measures: it uses longer seeds to reduce the false positive locations considered, leverages larger memory capacities to speed index lookup, and excludes most candidate locations without fully computing their edit distance to the read. The result is an algorithm that scales well for reads from one hundred to thousands of bases long and provides a rich error model that can match classes of mutations (e.g., longer indels) that today's fast aligners ignore. We calculate that SNAP can align a dataset with 30x coverage of a human genome in less than an hour for a cost of $2 on Amazon EC2, with higher accuracy than BWA. Finally, we describe ongoing work to further improve SNAP.

The self-alignment concentrations, $c(x)$, as functions of the length, $x$, of the identically matching maximal segments in the genomes of a variety of species, typically present power-law tails extending to the largest scales, i.e., $c(x) \propto x^{\alpha}$, with similar or apparently different negative $\alpha$s ($

We introduce a new heuristic for the multiple alignment of a set of sequences. The heuristic is based on a set cover of the residue alphabet of the sequences, and also on the determination of a significant set of blocks comprising subsequences of the sequences to be aligned. These blocks are obtained with the aid of a new data structure, called a suffix-set tree, which is constructed from the input sequences with the guidance of the residue-alphabet set cover and generalizes the well-known suffix tree of the sequence set. We provide performance results on selected BAliBASE amino-acid sequences and compare them with those yielded by some prominent approaches.

The use of contrast medium allows joining the high-resolution anatomical information provided by standard magnetic resonance with functional information obtained by means of the diffusion of contrast agent in tissues or in the vascular net. To effectively use this kind of images for medical diagnosis, quantitative analysis should be performed. We propose an automatic registration procedure based on maximization of the mutual information that address the requirement of fast and automatic tools for quantitative analysis of contrast medium enhanced MR images. Two optimization algorithms for maximization of the mutual information are discussed, taking into account both time performance and registration quality. We present also preliminary results on cardiac and wrist MR images showing that misalignments and artifacts introduced by patient movement during the examination are greatly reduced by our application.

This article is from BMC Bioinformatics , volume 13 . Abstract Background: Alignment of protein sequences (MPSA) is the starting point for a multitude of applications in molecular biology. Here, we present a novel MPSA program based on the SeqAn sequence alignment library. Our implementation has a strict modular structure, which allows to swap different components of the alignment process and, thus, to investigate their contribution to the alignment quality and computation time. We systematically varied information sources, guiding trees, score transformations and iterative refinement options, and evaluated the resulting alignments on BAliBASE and SABmark. Results: Our results indicate the optimal alignment strategy based on the choices compared. First, we show that pairwise global and local alignments contain sufficient information to construct a high quality multiple alignment. Second, single linkage clustering is almost invariably the best algorithm to build a guiding tree for progressive alignment. Third, triplet library extension, with introduction of new edges, is the most efficient consistency transformation of those compared. Alternatively, one can apply tree dependent partitioning as a post processing step, which was shown to be comparable with the best consistency transformation in both time and accuracy. Finally, propagating information beyond four transitive links introduces more noise than signal. Conclusions: This is the first time multiple protein alignment strategies are comprehensively and clearly compared using a single implementation platform. In particular, we showed which of the existing consistency transformations and iterative refinement techniques are the most valid. Our implementation is freely available at http://ekhidna.biocenter.helsinki.fi/MMSA and as a supplementary file attached to this article (see Additional file 1).

This article is from BMC Bioinformatics , volume 13 . Abstract Background: Alignment of protein sequences (MPSA) is the starting point for a multitude of applications in molecular biology. Here, we present a novel MPSA program based on the SeqAn sequence alignment library. Our implementation has a strict modular structure, which allows to swap different components of the alignment process and, thus, to investigate their contribution to the alignment quality and computation time. We systematically varied information sources, guiding trees, score transformations and iterative refinement options, and evaluated the resulting alignments on BAliBASE and SABmark. Results: Our results indicate the optimal alignment strategy based on the choices compared. First, we show that pairwise global and local alignments contain sufficient information to construct a high quality multiple alignment. Second, single linkage clustering is almost invariably the best algorithm to build a guiding tree for progressive alignment. Third, triplet library extension, with introduction of new edges, is the most efficient consistency transformation of those compared. Alternatively, one can apply tree dependent partitioning as a post processing step, which was shown to be comparable with the best consistency transformation in both time and accuracy. Finally, propagating information beyond four transitive links introduces more noise than signal. Conclusions: This is the first time multiple protein alignment strategies are comprehensively and clearly compared using a single implementation platform. In particular, we showed which of the existing consistency transformations and iterative refinement techniques are the most valid. Our implementation is freely available at http://ekhidna.biocenter.helsinki.fi/MMSA and as a supplementary file attached to this article (see Additional file 1).

Existing sequence alignment algorithms use heuristic scoring schemes which cannot be used as objective distance metrics. Therefore one relies on measures like the p- or log-det distances, or makes explicit, and often simplistic, assumptions about sequence evolution. Information theory provides an alternative, in the form of mutual information (MI) which is, in principle, an objective and model independent similarity measure. MI can be estimated by concatenating and zipping sequences, yielding thereby the "normalized compression distance". So far this has produced promising results, but with uncontrolled errors. We describe a simple approach to get robust estimates of MI from global pairwise alignments. Using standard alignment algorithms, this gives for animal mitochondrial DNA estimates that are strikingly close to estimates obtained from the alignment free methods mentioned above. Our main result uses algorithmic (Kolmogorov) information theory, but we show that similar results can also be obtained from Shannon theory. Due to the fact that it is not additive, normalized compression distance is not an optimal metric for phylogenetics, but we propose a simple modification that overcomes the issue of additivity. We test several versions of our MI based distance measures on a large number of randomly chosen quartets and demonstrate that they all perform better than traditional measures like the Kimura or log-det (resp. paralinear) distances. Even a simplified version based on single letter Shannon entropies, which can be easily incorporated in existing software packages, gave superior results throughout the entire animal kingdom. But we see the main virtue of our approach in a more general way. For example, it can also help to judge the relative merits of different alignment algorithms, by estimating the significance of specific alignments.

MOTIVATION: Protein sequence alignment plays a critical role in computational biology as it is an integral part in many analysis tasks designed to solve problems in comparative genomics, structure and function prediction, and homology modeling. METHODS: We have developed novel sequence alignment algorithms that compute the alignment between a pair of sequences based on short fixed- or variable-length high-scoring subsequences. Our algorithms build the alignments by repeatedly selecting the highest scoring pairs of subsequences and using them to construct small portions of the final alignment. We utilize PSI-BLAST generated sequence profiles and employ a profile-to-profile scoring scheme derived from PICASSO. RESULTS: We evaluated the performance of the computed alignments on two recently published benchmark datasets and compared them against the alignments computed by existing state-of-the-art dynamic programming-based profile-to-profile local and global sequence alignment algorithms. Our results show that the new algorithms achieve alignments that are comparable or better to those achieved by existing algorithms. Moreover, our results also showed that these algorithms can be used to provide better information as to which of the aligned positions are more reliable a critical piece of information for comparative modeling applications.

This article is from BMC Genomics , volume 15 . Abstract Background: Sequence alignment has become an indispensable tool in modern molecular biology research, and probabilistic sequence alignment models have been shown to provide an effective framework for building accurate sequence alignment tools. One such example is the pair hidden Markov model (pair-HMM), which has been especially popular in comparative sequence analysis for several reasons, including their effectiveness in modeling and detecting sequence homology, model simplicity, and the existence of efficient algorithms for applying the model to sequence alignment problems. However, despite these advantages, pair-HMMs also have a number of practical limitations that may degrade their alignment performance or render them unsuitable for certain alignment tasks. Results: In this work, we propose a novel scheme for comparing and aligning biological sequences that can effectively address the shortcomings of the traditional pair-HMMs. The proposed scheme is based on a simple message-passing approach, where messages are exchanged between neighboring symbol pairs that may be potentially aligned in the optimal sequence alignment. The message-passing process yields probabilistic symbol alignment confidence scores, which may be used for predicting the optimal alignment that maximizes the expected number of correctly aligned symbol pairs. Conclusions: Extensive performance evaluation on protein alignment benchmark datasets shows that the proposed message-passing scheme clearly outperforms the traditional pair-HMM-based approach, in terms of both alignment accuracy and computational efficiency. Furthermore, the proposed scheme is numerically robust and amenable to massive parallelization.

This article is from Source Code for Biology and Medicine , volume 9 . Abstract Background: The fundamental challenge in optimally aligning homologous sequences is to define a scoring scheme that best reflects the underlying biological processes. Maximising the overall number of matches in the alignment does not always reflect the patterns by which nucleotides mutate. Efficiently implemented algorithms that can be parameterised to accommodate more complex non-linear scoring schemes are thus desirable. Results: We present Cola, alignment software that implements different optimal alignment algorithms, also allowing for scoring contiguous matches of nucleotides in a nonlinear manner. The latter places more emphasis on short, highly conserved motifs, and less on the surrounding nucleotides, which can be more diverged. To illustrate the differences, we report results from aligning 14,100 sequences from 3' untranslated regions of human genes to 25 of their mammalian counterparts, where we found that a nonlinear scoring scheme is more consistent than a linear scheme in detecting short, conserved motifs. Conclusions: Cola is freely available under LPGL from https://github.com/nedaz/cola.

Alignment-based sequence similarity searches, while accurate for some type of sequences, can produce incorrect results when used on more divergent but functionally related sequences that have undergone the sequence rearrangements observed in many bacterial and viral genomes. Here, we propose a classification model that exploits the complementary nature of alignment-based and alignment-free similarity measures with the aim to improve the accuracy with which DNA and protein sequences are characterized. Our model classifies sequences using a combined sequence similarity score calculated by adaptively weighting the contribution of different sequence similarity measures. Weights are determined independently for each sequence in the test set and reflect the discriminatory ability of individual similarity measures in the training set. Since the similarity between some sequences is determined more accurately with one type of measure rather than another, our classifier allows different sets of weights to be associated with different sequences. Using five different similarity measures we show that our model significantly improves the classification accuracy over the current composition and alignment based models, when predicting the taxonomic lineage for both short viral sequence fragments and complete viral sequences. We also show that our model can be used effectively for the classification of reads from a real metagenome dataset as well as protein sequences.

This article is from Source Code for Biology and Medicine , volume 9 . Abstract : Multiobjective sequence alignment brings the advantage of providing a set of alignments that represent the trade-off between performing insertion/deletions and matching symbols from both sequences. Each of these alignments provide a potential explanation of the relationship between the sequences. We introduce MOSAL, a software tool that provides an open-source implementation and an on-line application for multiobjective pairwise sequence alignment.

This article is from PLoS ONE , volume 9 . Abstract Multiple sequence alignment is a crucial task in a number of biological analyses like secondary structure prediction, domain searching, phylogeny, etc. MSAProbs is currently the most accurate alignment algorithm, but its effectiveness is obtained at the expense of computational time. In the paper we present QuickProbs, the variant of MSAProbs customised for graphics processors. We selected the two most time consuming stages of MSAProbs to be redesigned for GPU execution: the posterior matrices calculation and the consistency transformation. Experiments on three popular benchmarks (BAliBASE, PREFAB, OXBench-X) on quad-core PC equipped with high-end graphics card show QuickProbs to be 5.7 to 9.7 times faster than original CPU-parallel MSAProbs. Additional tests performed on several protein families from Pfam database give overall speed-up of 6.7. Compared to other algorithms like MAFFT, MUSCLE, or ClustalW, QuickProbs proved to be much more accurate at similar speed. Additionally we introduce a tuned variant of QuickProbs which is significantly more accurate on sets of distantly related sequences than MSAProbs without exceeding its computation time. The GPU part of QuickProbs was implemented in OpenCL, thus the package is suitable for graphics processors produced by all major vendors.

This article is from BMC Bioinformatics , volume 13 . Abstract Background: Work on protein structure prediction is very useful in biological research. To evaluate their accuracy, experimental protein structures or their derived data are used as the 'gold standard'. However, as proteins are dynamic molecular machines with structural flexibility such a standard may be unreliable. Results: To investigate the influence of the structure flexibility, we analysed 3,652 protein structures of 137 unique sequences from 24 protein families. The results showed that (1) the three-dimensional (3D) protein structures were not rigid: the root-mean-square deviation (RMSD) of the backbone Cα of structures with identical sequences was relatively large, with the average of the maximum RMSD from each of the 137 sequences being 1.06 Å; (2) the derived data of the 3D structure was not constant, e.g. the highest ratio of the secondary structure wobble site was 60.69%, with the sequence alignments from structural comparisons of two proteins in the same family sometimes being completely different. Conclusion: Proteins may have several stable conformations and the data derived from resolved structures as a 'gold standard' should be optimized before being utilized as criteria to evaluate the prediction methods, e.g. sequence alignment from structural comparison. Helix/β-sheet transition exists in normal free proteins. The coil ratio of the 3D structure could affect its resolution as determined by X-ray crystallography.

Document similarity is the problem of formally representing textual documents and then proposing a similarity measure that can be used to compute the linguistic similarity between two documents. Accurate document similarity computation improves many enterprise relevant tasks such as document clustering, text mining, and question-answering. Most contemporary techniques employ bag-of-words (BoW) based document representation models. In this paper, we show that a document's thematic flow, which is often disregarded by bag-of-word techniques, is pivotal in estimating their semantic similarity. In this direction, we propose a novel semantic document similarity framework, called SimDoc. We model documents as topic-sequences, where topics represent latent generative clusters of relative words. We then use a sequence alignment algorithm, that has been adapted from the Smith-Waterman gene-sequencing algorithm, to estimate their semantic similarity. For similarity computation at a finer granularity, we tune the alignment algorithm by integrating it with a word embedding matrix based topic-to-topic similarity measure. A document level similarity score is then computed by again using the sequence alignment algorithm over all sentence pairs. In our experiments, we see that SimDoc outperforms many contemporary bag-of-words techniques in accurately computing document similarity, and on practical applications such as document clustering.

This article is from Nucleic Acids Research , volume 39 . Abstract Multiple sequence alignment, which is of fundamental importance for comparative genomics, is a difficult problem and error-prone. Therefore, it is essential to measure the reliability of the alignments and incorporate it into downstream analyses. We propose a new probabilistic sampling-based alignment reliability (PSAR) score. Instead of relying on heuristic assumptions, such as the correlation between alignment quality and guide tree uncertainty in progressive alignment methods, we directly generate suboptimal alignments from an input multiple sequence alignment by a probabilistic sampling method, and compute the agreement of the input alignment with the suboptimal alignments as the alignment reliability score. We construct the suboptimal alignments by an approximate method that is based on pairwise comparisons between each single sequence and the sub-alignment of the input alignment where the chosen sequence is left out. By using simulation-based benchmarks, we find that our approach is superior to existing ones, supporting that the suboptimal alignments are highly informative source for assessing alignment reliability. We apply the PSAR method to the alignments in the UCSC Genome Browser to measure the reliability of alignments in different types of regions, such as coding exons and conserved non-coding regions, and use it to guide cross-species conservation study.

This article is from Algorithms for Molecular Biology : AMB , volume 9 . Abstract Background: Progressive methods offer efficient and reasonably good solutions to the multiple sequence alignment problem. However, resulting alignments are biased by guide-trees, especially for relatively distant sequences. Results: We propose MSARC, a new graph-clustering based algorithm that aligns sequence sets without guide-trees. Experiments on the BAliBASE dataset show that MSARC achieves alignment quality similar to the best progressive methods.Furthermore, MSARC outperforms them on sequence sets whose evolutionary distances are difficult to represent by a phylogenetic tree. These datasets are most exposed to the guide-tree bias of alignments. Availability: MSARC is available at http://bioputer.mimuw.edu.pl/msarc

This article is from Algorithms for Molecular Biology : AMB , volume 9 . Abstract One can search for messages in the digits of π or a Kazakhstan telephone book, but there may be hidden messages closer to home. A recent publication in this journal purportedly compared a set of multiple sequence alignment programs. The real purpose of the article may have been to remind readers how to present scientific data.

The multiple sequence alignment (MSA) of a protein family provides a wealth of information in terms of the conservation pattern of amino acid residues not only at each alignment site but also between distant sites. In order to statistically model the MSA incorporating both short-range and long-range correlations as well as insertions, I have derived a lattice gas model of the MSA based on the principle of maximum entropy. The partition function, obtained by the transfer matrix method with a mean-field approximation, accounts for all possible alignments with all possible sequences. The model parameters for short-range and long-range interactions were determined by a self-consistent condition and by a Gaussian approximation, respectively. Using this model with and without long-range interactions, I analyzed the globin and V-set domains by increasing the "temperature" and by "mutating" a site. The correlations between residue conservation and various measures of the system's stability indicate that the long-range interactions make the conservation pattern more specific to the structure, and increasingly stabilize better conserved residues.

One of the major problems in biology is related to protein folding. The folding process is known to depend on both the protein's sequence (1-D) and structure (3-D). Both these properties need to be considered when aligning two proteins; they are also influenced by the evolutionary distance between the proteins to be aligned. We propose a Bayesian method to align proteins using both the sequence and 3-D structure of the proteins. The problem involves what are known as "gaps" in the sequence, which we incorporate in our model, and an MCMC implementation is provided. Also, we show that the procedure can be used to give insight into evolutionary distances between proteins. Various illustrative examples from protein bioinformatics are studied.

This article is from BMC Bioinformatics , volume 12 . Abstract Background: Multiple Sequence Alignment (MSA) is a basic tool for bioinformatics research and analysis. It has been used essentially in almost all bioinformatics tasks such as protein structure modeling, gene and protein function prediction, DNA motif recognition, and phylogenetic analysis. Therefore, improving the accuracy of multiple sequence alignment is important for advancing many bioinformatics fields. Results: We designed and developed a new method, MSACompro, to synergistically incorporate predicted secondary structure, relative solvent accessibility, and residue-residue contact information into the currently most accurate posterior probability-based MSA methods to improve the accuracy of multiple sequence alignments. The method is different from the multiple sequence alignment methods (e.g. 3D-Coffee) that use the tertiary structure information of some sequences since the structural information of our method is fully predicted from sequences. To the best of our knowledge, applying predicted relative solvent accessibility and contact map to multiple sequence alignment is novel. The rigorous benchmarking of our method to the standard benchmarks (i.e. BAliBASE, SABmark and OXBENCH) clearly demonstrated that incorporating predicted protein structural information improves the multiple sequence alignment accuracy over the leading multiple protein sequence alignment tools without using this information, such as MSAProbs, ProbCons, Probalign, T-coffee, MAFFT and MUSCLE. And the performance of the method is comparable to the state-of-the-art method PROMALS of using structural features and additional homologous sequences by slightly lower scores. Conclusion: MSACompro is an efficient and reliable multiple protein sequence alignment tool that can effectively incorporate predicted protein structural information into multiple sequence alignment. The software is available at http://sysbio.rnet.missouri.edu/multicom_toolbox/.

This article is from BMC Bioinformatics , volume 12 . Abstract Background: Many Bioinformatics studies begin with a multiple sequence alignment as the foundation for their research. This is because multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence structure relationships. Results: In this paper, we have proposed a Vertical Decomposition with Genetic Algorithm (VDGA) for Multiple Sequence Alignment (MSA). In VDGA, we divide the sequences vertically into two or more subsequences, and then solve them individually using a guide tree approach. Finally, we combine all the subsequences to generate a new multiple sequence alignment. This technique is applied on the solutions of the initial generation and of each child generation within VDGA. We have used two mechanisms to generate an initial population in this research: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with VDGA. To test the performance of our algorithm, we have compared it with existing well-known methods, namely PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on Genetic Algorithms (GA), such as SAGA, MSA-GA and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0. Conclusions: The experimental results showed that the VDGA with three vertical divisions was the most successful variant for most of the test cases in comparison to other divisions considered with VDGA. The experimental results also confirmed that VDGA outperformed the other methods considered in this research.

This article is from BMC Bioinformatics , volume 12 . Abstract Background: Many Bioinformatics studies begin with a multiple sequence alignment as the foundation for their research. This is because multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence structure relationships. Results: In this paper, we have proposed a Vertical Decomposition with Genetic Algorithm (VDGA) for Multiple Sequence Alignment (MSA). In VDGA, we divide the sequences vertically into two or more subsequences, and then solve them individually using a guide tree approach. Finally, we combine all the subsequences to generate a new multiple sequence alignment. This technique is applied on the solutions of the initial generation and of each child generation within VDGA. We have used two mechanisms to generate an initial population in this research: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with VDGA. To test the performance of our algorithm, we have compared it with existing well-known methods, namely PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on Genetic Algorithms (GA), such as SAGA, MSA-GA and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0. Conclusions: The experimental results showed that the VDGA with three vertical divisions was the most successful variant for most of the test cases in comparison to other divisions considered with VDGA. The experimental results also confirmed that VDGA outperformed the other methods considered in this research.

This article is from Bioinformatics , volume 30 . Abstract Motivation: Alignment-free methods for sequence comparison are increasingly used for genome analysis and phylogeny reconstruction; they circumvent various difficulties of traditional alignment-based approaches. In particular, alignment-free methods are much faster than pairwise or multiple alignments. They are, however, less accurate than methods based on sequence alignment. Most alignment-free approaches work by comparing the word composition of sequences. A well-known problem with these methods is that neighbouring word matches are far from independent.Results: To reduce the statistical dependency between adjacent word matches, we propose to use ‘spaced words’, defined by patterns of ‘match’ and ‘don’t care’ positions, for alignment-free sequence comparison. We describe a fast implementation of this approach using recursive hashing and bit operations, and we show that further improvements can be achieved by using multiple patterns instead of single patterns. To evaluate our approach, we use spaced-word frequencies as a basis for fast phylogeny reconstruction. Using real-world and simulated sequence data, we demonstrate that our multiple-pattern approach produces better phylogenies than approaches relying on contiguous words.Availability and implementation: Our program is freely available at http://spaced.gobics.de/.Contact:[email protected] tary information:Supplementary data are available at Bioinformatics online.

This article is from Nucleic Acids Research , volume 41 . Abstract We report a new web server, aLeaves (http://aleaves.cdb.riken.jp/), for homologue collection from diverse animal genomes. In molecular comparative studies involving multiple species, orthology identification is the basis on which most subsequent biological analyses rely. It can be achieved most accurately by explicit phylogenetic inference. More and more species are subjected to large-scale sequencing, but the resultant resources are scattered in independent project-based, and multi-species, but separate, web sites. This complicates data access and is becoming a serious barrier to the comprehensiveness of molecular phylogenetic analysis. aLeaves, launched to overcome this difficulty, collects sequences similar to an input query sequence from various data sources. The collected sequences can be passed on to the MAFFT sequence alignment server (http://mafft.cbrc.jp/alignment/server/), which has been significantly improved in interactivity. This update enables to switch between (i) sequence selection using the Archaeopteryx tree viewer, (ii) multiple sequence alignment and (iii) tree inference. This can be performed as a loop until one reaches a sensible data set, which minimizes redundancy for better visibility and handling in phylogenetic inference while covering relevant taxa. The work flow achieved by the seamless link between aLeaves and MAFFT provides a convenient online platform to address various questions in zoology and evolutionary biology.

This article is from BMC Bioinformatics , volume 13 . Abstract Background: ProGraphMSA is a state-of-the-art multiple sequence alignment tool which produces phylogenetically sensible gap patterns while maintaining robustness by allowing alternative splicings and errors in the branching pattern of the guide tree. Results: This is achieved by incorporating a graph-based sequence representation combined with the advantages of the phylogeny-aware gap placement algorithm of Prank. Further, we account for variations in the substitution pattern by implementing context-specific profiles as in CS-Blast and by estimating amino acid frequencies from input data. Conclusions: ProGraphMSA shows good performance and competitive execution times in various benchmarks.

This article is from BMC Bioinformatics , volume 13 . Abstract Background: Transmembrane proteins (TMPs) constitute about 20~30% of all protein coding genes. The relative lack of experimental structure has so far made it hard to develop specific alignment methods and the current state of the art (PRALINE™) only manages to recapitulate 50% of the positions in the reference alignments available from the BAliBASE2-ref7. Methods: We show how homology extension can be adapted and combined with a consistency based approach in order to significantly improve the multiple sequence alignment of alpha-helical TMPs. TM-Coffee is a special mode of PSI-Coffee able to efficiently align TMPs, while using a reduced reference database for homology extension. Results: Our benchmarking on BAliBASE2-ref7 alpha-helical TMPs shows a significant improvement over the most accurate methods such as MSAProbs, Kalign, PROMALS, MAFFT, ProbCons and PRALINE™. We also estimated the influence of the database used for homology extension and show that highly non-redundant UniRef databases can be used to obtain similar results at a significantly reduced computational cost over full protein databases. TM-Coffee is part of the T-Coffee package, a web server is also available from http://tcoffee.crg.cat/tmcoffee and a freeware open source code can be downloaded from http://www.tcoffee.org/Packages/Stable/Latest.

We propose an approach for multiple sequence alignment (MSA) derived from the dynamic time warping viewpoint and recent techniques of curve synchronization developed in the context of functional data analysis. Starting from pairwise alignments of all the sequences (viewed as paths in a certain space), we construct a median path that represents the MSA we are looking for. We establish a proof of concept that our method could be an interesting ingredient to include into refined MSA techniques. We present a simple synthetic experiment as well as the study of a benchmark dataset, together with comparisons with 2 widely used MSA softwares.

For the Bernoulli Matching model of sequence alignment problem we apply the Bethe ansatz technique via an exact mapping to the 5--vertex model on a square lattice. Considering the terrace--like representation of the sequence alignment problem, we reproduce by the Bethe ansatz the results for the averaged length of the Longest Common Subsequence in Bernoulli approximation. In addition, we compute the average number of nucleation centers of the terraces.

For the Bernoulli Matching model of sequence alignment problem we apply the Bethe ansatz technique via an exact mapping to the 5--vertex model on a square lattice. Considering the terrace--like representation of the sequence alignment problem, we reproduce by the Bethe ansatz the results for the averaged length of the Longest Common Subsequence in Bernoulli approximation. In addition, we compute the average number of nucleation centers of the terraces.

Advances in bio-technology have made available massive amounts of functional, structural and genomic data for many biological sequences. This increased availability of heterogeneous biological data has resulted in biological applications where a multiple sequence alignment (msa) is required for aligning similar features, where a feature is described in structural, functional or evolutionary terms. In these applications, for a given set of sequences, depending on the feature of interest the optimal msa is likely to be different, and sequence similarity can only be used as a rough initial estimate on the accuracy of an msa. This has motivated the growth in template based heuristics that supplement the sequence information with evolutionary, structural and functional data and exploit feature similarity instead of sequence similarity to construct multiple sequence alignments that are biologically more accurate. However, current frameworks for designing template based heuristics do not allow the user to explicitly specify information that can help to classify features into types and associate weights signifying the relative importance of a feature with respect to other features. In this paper, we first provide a mechanism where as a part of the template information the user can explicitly specify for each feature, its type, and weight. The type is to classify the features into different categories based on their characteristics and the weight signifies the relative importance of a feature with respect to other features in that sequence. Second, we exploit the above information to define scoring models for pair-wise sequence alignment that assume segment conservation as opposed to single character (residue) conservation. Finally, we present a fast progressive alignment based heuristic framework that helps in constructing a global msa efficiently.

Advances in bio-technology have made available massive amounts of functional, structural and genomic data for many biological sequences. This increased availability of heterogeneous biological data has resulted in biological applications where a multiple sequence alignment (msa) is required for aligning similar features, where a feature is described in structural, functional or evolutionary terms. In these applications, for a given set of sequences, depending on the feature of interest the optimal msa is likely to be different, and sequence similarity can only be used as a rough initial estimate on the accuracy of an msa. This has motivated the growth in template based heuristics that supplement the sequence information with evolutionary, structural and functional data and exploit feature similarity instead of sequence similarity to construct multiple sequence alignments that are biologically more accurate. However, current frameworks for designing template based heuristics do not allow the user to explicitly specify information that can help to classify features into types and associate weights signifying the relative importance of a feature with respect to other features. In this paper, we first provide a mechanism where as a part of the template information the user can explicitly specify for each feature, its type, and weight. The type is to classify the features into different categories based on their characteristics and the weight signifies the relative importance of a feature with respect to other features in that sequence. Second, we exploit the above information to define scoring models for pair-wise sequence alignment that assume segment conservation as opposed to single character (residue) conservation. Finally, we present a fast progressive alignment based heuristic framework that helps in constructing a global msa efficiently.

The U.S. Army Corps of Engineers (USACE) Hurricane Protection Office (HPO) is authorized to provide New Orleans, Louisiana, with a risk reduction system for the one percent exceedance flood (HSDRRS). The purpose and need for the proposed action is to provide, in a timely manner, the 100-year level of risk reduction from flood damage to the areas surrounding the IHNC due to flooding from hurricanes and other severe storm events. This risk reduction is being accomplished through the construction of a comprehensive system of levees, gates, and drainage structures. Several planned structures (to be located along the levee system) allow for continued navigation in the Inner Harbor Navigation Canal (IHNC), Bayou Bienvenue, and the Gulf Intracoastal Waterway (GIWW). The IHNC Seabrook, Bayou Bienvenue, and GIWW gate structures are designed to remain open during normal tidal conditions with the ability to close during surge events. Numerical model studies were performed by the USACE Engineer Research and Development Center (ERDC) Coastal and Hydraulics Laboratory (CHL) to assess the impacts of these navigation structures on hydrodynamics and larval transport. HPO requested that ERDC also perform a numerical modeling study for the purpose of analyzing the temporary construction impacts of proposed HSDRRS measures to be placed in the GIWW and the Mississippi River Gulf Outlet (MRGO). The work presented in this report documents hydrodynamic modeling and analysis of two construction sequence plans which will occur during the construction of the Borgne alignment and Seabrook structures included in the HSDRRS. This report specifically focuses on the construction sequence plans during which the Bayou Bienvenue structure and GIWW sector gate are being built as well as the construction of the Seabrook structure. The February 2010 plan includes cofferdams on the Bayou Bienvenue

A database of guitar-only audio files with accompanying annotations including beat locations and information about repeated content. The names of authors/composers of each audio file can be found in the file 'Audio File Author Information'. Attributions for derived works should be to the relevant author/composer.

Multiple sequence alignment (MSA) has been one of the most important problems in bioinformatics for more decades and it is still heavily examined by many mathematicians and biologists. However, mostly because of the practical motivation of this problem, the research on this topic is focused on aligning long sequences. It is understandable, since the sequences that need to be aligned (usually DNA or protein sequences) are generally quite long (e. g., at least 30-40 characters). Nevertheless, it is a challenging question that exactly where MSA starts to become a real hard problem (since it is known that MSA is NP-complete [2]), and the key to answer this question is to examine short sequences. If the optimal alignment for short sequences could be determined in polynomial time, then these results may help to develop faster or more accurate heuristic algorithms for aligning long sequences. In this work, it is shown that for length-1 sequences using arbitrary metric, as well as for length-2 sequences using unit metric, the optimum of the MSA problem can be achieved by the trivial alignment.

Background: Alignment of biological sequences such as DNA, RNA or proteins is one of the most widely used tools in computational bioscience. All existing alignment algorithms rely on heuristic scoring schemes based on biological expertise. Therefore, these algorithms do not provide model independent and objective measures for how similar two (or more) sequences actually are. Although information theory provides such a similarity measure -- the mutual information (MI) -- previous attempts to connect sequence alignment and information theory have not produced realistic estimates for the MI from a given alignment. Results: Here we describe a simple and flexible approach to get robust estimates of MI from {\it global} alignments. For mammalian mitochondrial DNA, our approach gives pairwise MI estimates for commonly used global alignment algorithms that are strikingly close to estimates obtained by an entirely unrelated approach -- concatenating and zipping the sequences. Conclusions: This remarkable consistency may help establish MI as a reliable tool for evaluating the quality of global alignments, judging the relative merits of different alignment algorithms, and estimating the significance of specific alignments. We expect that our approach can be extended to establish further connections between information theory and sequence alignment, including applications to local and multiple alignment procedures.

This article is from BMC Proceedings , volume 8 . Abstract Background: The 2013 BioVis Contest provided an opportunity to evaluate different paradigms for visualizing protein multiple sequence alignments. Such data sets are becoming extremely large and thus taxing current visualization paradigms. Sequence Logos represent consensus sequences but have limitations for protein alignments. As an alternative, ProfileGrids are a new protein sequence alignment visualization paradigm that represents an alignment as a color-coded matrix of the residue frequency occurring at every homologous position in the aligned protein family. Results: The JProfileGrid software program was used to analyze the BioVis contest data sets to generate figures for comparison with the Sequence Logo reference images. Conclusions: The ProfileGrid representation allows for the clear and effective analysis of protein multiple sequence alignments. This includes both a general overview of the conservation and diversity sequence patterns as well as the interactive ability to query the details of the protein residue distributions in the alignment. The JProfileGrid software is free and available from http://www.ProfileGrid.org.

We study the problem of similarity detection by sequence alignment with gaps, using a recently established theoretical framework based on the morphology of alignment paths. Alignments of sequences without mutual correlations are found to have scale-invariant statistics. This is the basis for a scaling theory of alignments of correlated sequences. Using a simple Markov model of evolution, we generate sequences with well-defined mutual correlations and quantify the fidelity of an alignment in an unambiguous way. The scaling theory predicts the dependence of the fidelity on the alignment parameters and on the statistical evolution parameters characterizing the sequence correlations. Specific criteria for the optimal choice of alignment parameters emerge from this theory. The results are verified by extensive numerical simulations.

The advent of next-generation sequencing (NGS) technologies enables researchers to sequence complex microbial communities directly from environment. Since assembly typically produces only genome fragments, also known as contigs, instead of entire genome, it is crucial to group them into operational taxonomic units (OTUs) for further taxonomic profiling and down-streaming functional analysis. OTU clustering is also referred to as binning. We present COCACOLA, a general framework automatically bin contigs into OTUs based upon sequence composition and coverage across multiple samples. The effectiveness of COCACOLA is demonstrated in both simulated and real datasets in comparison to state-of-art binning approaches such as CONCOCT, GroopM, MaxBin and MetaBAT. The superior performance of COCACOLA relies on two aspects. One is employing $L_{1}$ distance instead of Euclidean distance for better taxonomic identification during initialization. More importantly, COCACOLA takes advantage of both hard clustering and soft clustering by sparsity regularization. In addition, the COCACOLA framework seamlessly embraces customized knowledge to facilitate binning accuracy. In our study, we have investigated two types of additional knowledge, the co-alignment to reference genomes and linkage of contigs provided by paired-end reads, as well as the ensemble of both. We find that both co-alignment and linkage information further improve binning in the majority of cases. COCACOLA is scalable and faster than CONCOCT ,GroopM, MaxBin and MetaBAT. The software is available at https://github.com/younglululu/COCACOLA

In this paper, we propose to learn a Mahalanobis distance to perform alignment of multivariate time series. The learning examples for this task are time series for which the true alignment is known. We cast the alignment problem as a structured prediction task, and propose realistic losses between alignments for which the optimization is tractable. We provide experiments on real data in the audio to audio context, where we show that the learning of a similarity measure leads to improvements in the performance of the alignment task. We also propose to use this metric learning framework to perform feature selection and, from basic audio features, build a combination of these with better performance for the alignment.

This article is from Genome Biology , volume 14 . Abstract Citizen science games such as Galaxy Zoo, Foldit, and Phylo aim to harness the intelligence and processing power generated by crowds of online gamers to solve scientific problems. However, the selection of the data to be analyzed through these games is under the exclusive control of the game designers, and so are the results produced by gamers. Here, we introduce Open-Phylo, a freely accessible crowd-computing platform that enables any scientist to enter our system and use crowds of gamers to assist computer programs in solving one of the most fundamental problems in genomics: the multiple sequence alignment problem.

This article is from International Journal of Health Geographics , volume 10 . Abstract Background: Global positioning systems (GPS) are increasingly being used in health research to determine the location of study participants. Combining GPS data with data collected via travel/activity diaries allows researchers to assess where people travel in conjunction with data about trip purpose and accompaniment. However, linking GPS and diary data is problematic and to date the only method has been to match the two datasets manually, which is time consuming and unlikely to be practical for larger data sets. This paper assesses the feasibility of a new sequence alignment method of linking GPS and travel diary data in comparison with the manual matching method. Methods: GPS and travel diary data obtained from a study of children's independent mobility were linked using sequence alignment algorithms to test the proof of concept. Travel diaries were assessed for quality by counting the number of errors and inconsistencies in each participant's set of diaries. The success of the sequence alignment method was compared for higher versus lower quality travel diaries, and for accompanied versus unaccompanied trips. Time taken and percentage of trips matched were compared for the sequence alignment method and the manual method. Results: The sequence alignment method matched 61.9% of all trips. Higher quality travel diaries were associated with higher match rates in both the sequence alignment and manual matching methods. The sequence alignment method performed almost as well as the manual method and was an order of magnitude faster. However, the sequence alignment method was less successful at fully matching trips and at matching unaccompanied trips. Conclusions: Sequence alignment is a promising method of linking GPS and travel diary data in large population datasets, especially if limitations in the trip detection algorithm are addressed.

This article is from BMC Bioinformatics , volume 11 . Abstract Background: Although both conservation and correlated mutation (CM) are important information reflecting the different sorts of context in multiple sequence alignment, most of alignment methods use sequence profiles that only represent conservation. There is no general way to represent correlated mutation and incorporate it with sequence alignment yet. Methods: We develop a novel method, CM profile, to represent correlated mutation as the spectral feature derived by using linear predictive coding where correlated mutations among different positions are represented by a fixed number of values. We combine CM profile with conventional sequence profile to improve alignment quality. Results: For distantly related protein pairs, using CM profile improves the profile-profile alignment with or without predicted secondary structure. Especially, at superfamily level, combining CM profile with sequence profile improves profile-profile alignment by 9.5% while predicted secondary structure does by 6.0%. More significantly, using both of them improves profile-profile alignment by 13.9%. We also exemplify the effectiveness of CM profile by demonstrating that the resulting alignment preserves share coevolution and contacts. Conclusions: In this work, we introduce a novel method, CM profile, which represents correlated mutation information as paralleled form, and apply it to the protein sequence alignment problem. When combined with conventional sequence profile, CM profile improves alignment quality significantly better than predicted secondary structure information, which should be beneficial for target-template alignment in protein structure prediction. Because of the generality of CM profile, it can be used for other bioinformatics applications in the same way of using sequence profile.

This article is from Scientific Reports , volume 3 . Abstract Protein sequence alignment is essential for template-based protein structure prediction and function annotation. We collect 20 sequence alignment algorithms, 10 published and 10 newly developed, which cover all representative sequence- and profile-based alignment approaches. These algorithms are benchmarked on 538 non-redundant proteins for protein fold-recognition on a uniform template library. Results demonstrate dominant advantage of profile-profile based methods, which generate models with average TM-score 26.5% higher than sequence-profile methods and 49.8% higher than sequence-sequence alignment methods. There is no obvious difference in results between methods with profiles generated from PSI-BLAST PSSM matrix and hidden Markov models. Accuracy of profile-profile alignments can be further improved by 9.6% or 21.4% when predicted or native structure features are incorporated. Nevertheless, TM-scores from profile-profile methods including experimental structural features are still 37.1% lower than that from TM-align, demonstrating that the fold-recognition problem cannot be solved solely by improving accuracy of structure feature predictions.

This article is from Molecular Biology and Evolution , volume 30 . Abstract We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.

This article is from PLoS ONE , volume 9 . Abstract The wide availability of whole-genome sequencing (WGS) and an abundance of open-source software have made detection of single-nucleotide polymorphisms (SNPs) in bacterial genomes an increasingly accessible and effective tool for comparative analyses. Thus, ensuring that real nucleotide differences between genomes (i.e., true SNPs) are detected at high rates and that the influences of errors (such as false positive SNPs, ambiguously called sites, and gaps) are mitigated is of utmost importance. The choices researchers make regarding the generation and analysis of WGS data can greatly influence the accuracy of short-read sequence alignments and, therefore, the efficacy of such experiments. We studied the effects of some of these choices, including: i) depth of sequencing coverage, ii) choice of reference-guided short-read sequence assembler, iii) choice of reference genome, and iv) whether to perform read-quality filtering and trimming, on our ability to detect true SNPs and on the frequencies of errors. We performed benchmarking experiments, during which we assembled simulated and real Listeria monocytogenes strain 08-5578 short-read sequence datasets of varying quality with four commonly used assemblers (BWA, MOSAIK, Novoalign, and SMALT), using reference genomes of varying genetic distances, and with or without read pre-processing (i.e., quality filtering and trimming). We found that assemblies of at least 50-fold coverage provided the most accurate results. In addition, MOSAIK yielded the fewest errors when reads were aligned to a nearly identical reference genome, while using SMALT to align reads against a reference sequence that is ∼0.82% distant from 08-5578 at the nucleotide level resulted in the detection of the greatest numbers of true SNPs and the fewest errors. Finally, we show that whether read pre-processing improves SNP detection depends upon the choice of reference sequence and assembler. In total, this study demonstrates that researchers should test a variety of conditions to achieve optimal results.

Multiple sequence alignment (MSA) is a ubiquitous problem in computational biology. Although it is NP-hard to find an optimal solution for an arbitrary number of sequences, due to the importance of this problem researchers are trying to push the limits of exact algorithms further. Since MSA can be cast as a classical path finding problem, it is attracting a growing number of AI researchers interested in heuristic search algorithms as a challenge with actual practical relevance. In this paper, we first review two previous, complementary lines of research. Based on Hirschbergs algorithm, Dynamic Programming needs O(kN^(k-1)) space to store both the search frontier and the nodes needed to reconstruct the solution path, for k sequences of length N. Best first search, on the other hand, has the advantage of bounding the search space that has to be explored using a heuristic. However, it is necessary to maintain all explored nodes up to the final solution in order to prevent the search from re-expanding them at higher cost. Earlier approaches to reduce the Closed list are either incompatible with pruning methods for the Open list, or must retain at least the boundary of the Closed list. In this article, we present an algorithm that attempts at combining the respective advantages; like A* it uses a heuristic for pruning the search space, but reduces both the maximum Open and Closed size to O(kN^(k-1)), as in Dynamic Programming. The underlying idea is to conduct a series of searches with successively increasing upper bounds, but using the DP ordering as the key for the Open priority queue. With a suitable choice of thresholds, in practice, a running time below four times that of A* can be expected. In our experiments we show that our algorithm outperforms one of the currently most successful algorithms for optimal multiple sequence alignments, Partial Expansion A*, both in time and memory. Moreover, we apply a refined heuristic based on optimal alignments not only of pairs of sequences, but of larger subsets. This idea is not new; however, to make it practically relevant we show that it is equally important to bound the heuristic computation appropriately, or the overhead can obliterate any possible gain. Furthermore, we discuss a number of improvements in time and space efficiency with regard to practical implementations. Our algorithm, used in conjunction with higher-dimensional heuristics, is able to calculate for the first time the optimal alignment for almost all of the problems in Reference 1 of the benchmark database BAliBASE.

Multiple sequence alignment (MSA) is a ubiquitous problem in computational biology. Although it is NP-hard to find an optimal solution for an arbitrary number of sequences, due to the importance of this problem researchers are trying to push the limits of exact algorithms further. Since MSA can be cast as a classical path finding problem, it is attracting a growing number of AI researchers interested in heuristic search algorithms as a challenge with actual practical relevance. In this paper, we first review two previous, complementary lines of research. Based on Hirschbergs algorithm, Dynamic Programming needs O(kN^(k-1)) space to store both the search frontier and the nodes needed to reconstruct the solution path, for k sequences of length N. Best first search, on the other hand, has the advantage of bounding the search space that has to be explored using a heuristic. However, it is necessary to maintain all explored nodes up to the final solution in order to prevent the search from re-expanding them at higher cost. Earlier approaches to reduce the Closed list are either incompatible with pruning methods for the Open list, or must retain at least the boundary of the Closed list. In this article, we present an algorithm that attempts at combining the respective advantages; like A* it uses a heuristic for pruning the search space, but reduces both the maximum Open and Closed size to O(kN^(k-1)), as in Dynamic Programming. The underlying idea is to conduct a series of searches with successively increasing upper bounds, but using the DP ordering as the key for the Open priority queue. With a suitable choice of thresholds, in practice, a running time below four times that of A* can be expected. In our experiments we show that our algorithm outperforms one of the currently most successful algorithms for optimal multiple sequence alignments, Partial Expansion A*, both in time and memory. Moreover, we apply a refined heuristic based on optimal alignments not only of pairs of sequences, but of larger subsets. This idea is not new; however, to make it practically relevant we show that it is equally important to bound the heuristic computation appropriately, or the overhead can obliterate any possible gain. Furthermore, we discuss a number of improvements in time and space efficiency with regard to practical implementations. Our algorithm, used in conjunction with higher-dimensional heuristics, is able to calculate for the first time the optimal alignment for almost all of the problems in Reference 1 of the benchmark database BAliBASE.

This article is from Bioinformatics , volume 30 . Abstract Motivation: Alignment-based methods for sequence analysis have various limitations if large datasets are to be analysed. Therefore, alignment-free approaches have become popular in recent years. One of the best known alignment-free methods is the average common substring approach that defines a distance measure on sequences based on the average length of longest common words between them. Herein, we generalize this approach by considering longest common substrings with k mismatches. We present a greedy heuristic to approximate the length of such k-mismatch substrings, and we describe kmacs, an efficient implementation of this idea based on generalized enhanced suffix arrays.Results: To evaluate the performance of our approach, we applied it to phylogeny reconstruction using a large number of DNA and protein sequence sets. In most cases, phylogenetic trees calculated with kmacs were more accurate than trees produced with established alignment-free methods that are based on exact word matches. Especially on protein sequences, our method seems to be superior. On simulated protein families, kmacs even outperformed a classical approach to phylogeny reconstruction using multiple alignment and maximum likelihood.Availability and implementation:kmacs is implemented in C++, and the source code is freely available at http://kmacs.gobics.de/Contact:[email protected] ry information:Supplementary data are available at Bioinformatics online.

Continuous action recognition is more challenging than isolated recognition because classification and segmentation must be simultaneously carried out. We build on the well known dynamic time warping (DTW) framework and devise a novel visual alignment technique, namely dynamic frame warping (DFW), which performs isolated recognition based on per-frame representation of videos, and on aligning a test sequence with a model sequence. Moreover, we propose two extensions which enable to perform recognition concomitant with segmentation, namely one-pass DFW and two-pass DFW. These two methods have their roots in the domain of continuous recognition of speech and, to the best of our knowledge, their extension to continuous visual action recognition has been overlooked. We test and illustrate the proposed techniques with a recently released dataset (RAVEL) and with two public-domain datasets widely used in action recognition (Hollywood-1 and Hollywood-2). We also compare the performances of the proposed isolated and continuous recognition algorithms with several recently published methods.