Progressive Alignment Method Research Articles

Progressive deep video dehazing without explicit alignment estimation

To solve the issue of video dehazing, there are two main tasks to attain: how to align adjacent frames to the reference frame; how to restore the reference frame. Some papers adopt explicit approaches (e.g., the Markov random field, optical flow, deformable convolution, 3D convolution) to align neighboring frames with the reference frame in feature space or image space, then various restoration methods are used to achieve the final dehazing results. In this paper, we propose a progressive alignment and restoration method for video dehazing, which fuses the process of alignment and restoration and simplifies complexity of network models. The alignment process aligns consecutive neighboring frames stage by stage without using the optical flow estimation or deformable convolution. The restoration process is not only implemented under the alignment process but also uses a refinement network to improve the dehazing performance of the whole network. The proposed networks include four fusion networks and one refinement network. To decrease the parameters of networks, three fusion networks share the same parameters in the first fusion stage. Extensive experiments demonstrate that the proposed video dehazing method achieves outstanding performance against the-state-of-art methods.

Multiobjective artificial fish swarm algorithm for multiple sequence alignment

Multiple sequence alignment (MSA) represents a basic task for many bioinformatics applications. MSA allows finding common conserved regions among various sequences of proteins or DNA. However, to find the optimal multiple sequence alignment, it is necessary to design an efficient exploration approach that could explore a huge number of possible multiple sequence alignments. As well as, it is required to use a powerful evaluation method to assess the biological relevance of these multiple sequence alignment. To address these main problems, this article presents a multiobjective artificial fish swarm algorithm (MOAFS) to solve multiple sequence alignment. MOAFS uses the behaviors of artificial fish swarm algorithm such as the cooperation, decentralization and parallelism to ensure a good trade-off between the exploration and the exploitation of the search space of MSA problem. To preserve the quality and consistency of alignment, two fitness functions have been simultaneously used by the MOAFS algorithm: (i) Weighted Sum of Pairs to determine similar regions horizontally and (ii) Similarity function to determine vertically similar regions between the sequences of an alignment. Following the exploration of space search, the Pareto-optimal set is obtained by MOAFS which performs the optimal multiple sequence alignments for both fitness functions. The performance of MOAFS algorithm has been proved by comparing our algorithm with different progressive alignment methods, and other alignment methods based on evolutionary algorithms with single-objective and many-objective. The experiment results conducted on BAliBASE 2.0 and BAliBASE 3.0 benchmark confirm that the MOAFS algorithm provides a greater accuracy statistical significance in terms of SP or CS scores.

Multi-Layer Progressive Face Alignment by Integrating Global Match and Local Refinement

Robust and accurate face alignment remains a challenging task, especially when local noises, illumination variations and partial occlusions exist in images. The existing local search and global match methods often misalign due to local optima without global constraints or limited local representation of global appearance. To solve these problems, we propose a new multi-layer progressive face alignment method that combines global matches for a whole face with local refinement for a given region, where the errors caused by local optima are restricted by globally-matched appearance, and the local misalignments in the global method are avoided by supplementing the representation of local details. Our method consists of the following processes: Firstly, an input image is encoded as a multi-mode Local Binary Pattern (LBP) image to regress the face shape parameters. Secondly, the local multi-mode histogram of oriented gradient (HOG) features is applied to update each landmark position. Thirdly, the above two alignment shapes are weighted as the final result. The contributions of this paper are as follows: (1) Shape initialization by applying an affine transformation to the mean shape. (2) Face representation by integrating multi-mode information in a whole face or a face region. (3) Face alignment by combining handcrafted features with convolutional neural networks (CNN). Extensive experiments on public datasets show that our method demonstrates improved performance in real environments in comparison to some state-of-the-art methods which apply single scale features or single CNN networks. Applying our method to the challenging HELEN dataset, the samples with fewer than 8 mean errors reach 81.1%.

Generalized Bootstrap Supports for Phylogenetic Analyses of Protein Sequences Incorporating Alignment Uncertainty.

Phylogenetic reconstructions are essential in genomics data analyses and depend on accurate multiple sequence alignment (MSA) models. We show that all currently available large-scale progressive multiple alignment methods are numerically unstable when dealing with amino-acid sequences. They produce significantly different output when changing sequence input order. We used the HOMFAM protein sequences dataset to show that on datasets larger than 100 sequences, this instability affects on average 21.5% of the aligned residues. The resulting Maximum Likelihood (ML) trees estimated from these MSAs are equally unstable with over 38% of the branches being sensitive to the sequence input order. We established that about two-thirds of this uncertainty stems from the unordered nature of children nodes within the guide trees used to estimate MSAs. To quantify this uncertainty we developed unistrap, a novel approach that estimates the combined effect of alignment uncertainty and site sampling on phylogenetic tree branch supports. Compared with the regular bootstrap procedure, unistrap provides branch support estimates that take into account a larger fraction of the parameters impacting tree instability when processing datasets containing a large number of sequences.

PnpProbs: a better multiple sequence alignment tool by better handling of guide trees.

BackgroundThis paper describes a new MSA tool called PnpProbs, which constructs better multiple sequence alignments by better handling of guide trees. It classifies sequences into two types: normally related and distantly related. For normally related sequences, it uses an adaptive approach to construct the guide tree needed for progressive alignment; it first estimates the input’s discrepancy by computing the standard deviation of their percent identities, and based on this estimate, it chooses the better method to construct the guide tree. For distantly related sequences, PnpProbs abandons the guide tree and uses instead some non-progressive alignment method to generate the alignment.ResultsTo evaluate PnpProbs, we have compared it with thirteen other popular MSA tools, and PnpProbs has the best alignment scores in all but one test. We have also used it for phylogenetic analysis, and found that the phylogenetic trees constructed from PnpProbs’ alignments are closest to the model trees.ConclusionsBy combining the strength of the progressive and non-progressive alignment methods, we have developed an MSA tool called PnpProbs. We have compared PnpProbs with thirteen other popular MSA tools and our results showed that our tool usually constructed the best alignments.

Application of the MAFFT sequence alignment program to large data-reexamination of the usefulness of chained guide trees.

Motivation: Large multiple sequence alignments (MSAs), consisting of thousands of sequences, are becoming more and more common, due to advances in sequencing technologies. The MAFFT MSA program has several options for building large MSAs, but their performances have not been sufficiently assessed yet, because realistic benchmarking of large MSAs has been difficult. Recently, such assessments have been made possible through the HomFam and ContTest benchmark protein datasets. Along with the development of these datasets, an interesting theory was proposed: chained guide trees increase the accuracy of MSAs of structurally conserved regions. This theory challenges the basis of progressive alignment methods and needs to be examined by being compared with other known methods including computationally intensive ones.Results: We used HomFam, ContTest and OXFam (an extended version of OXBench) to evaluate several methods enabled in MAFFT: (1) a progressive method with approximate guide trees, (2) a progressive method with chained guide trees, (3) a combination of an iterative refinement method and a progressive method and (4) a less approximate progressive method that uses a rigorous guide tree and consistency score. Other programs, Clustal Omega and UPP, available for large MSAs, were also included into the comparison. The effect of method 2 (chained guide trees) was positive in ContTest but negative in HomFam and OXFam. Methods 3 and 4 increased the benchmark scores more consistently than method 2 for the three datasets, suggesting that they are safer to use.Availability and Implementation: http://mafft.cbrc.jp/alignment/software/Contact: katoh@ifrec.osaka-u.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

MDAT- Aligning multiple domain arrangements.

BackgroundProteins are composed of domains, protein segments that fold independently from the rest of the protein and have a specific function. During evolution the arrangement of domains can change: domains are gained, lost or their order is rearranged. To facilitate the analysis of these changes we propose the use of multiple domain alignments.ResultsWe developed an alignment program, called MDAT, which aligns multiple domain arrangements. MDAT extends earlier programs which perform pairwise alignments of domain arrangements. MDAT uses a domain similarity matrix to score domain pairs and aligns the domain arrangements using a consistency supported progressive alignment method.ConclusionMDAT will be useful for analysing changes in domain arrangements within and between protein families and will thus provide valuable insights into the evolution of proteins and their domains. MDAT is coded in C++, and the source code is freely available for download at http://www.bornberglab.org/pages/mdat.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0442-7) contains supplementary material, which is available to authorized users.

A New Quantum Cuckoo Search Algorithm for Multiple Sequence Alignment

AbstractMultiple sequence alignment (MSA) is one of the major problems that can be encountered in the bioinformatics field. MSA consists in aligning a set of biological sequences to extract the similarities between them. Unfortunately, this problem has been shown to be NP-hard. In this article, a new algorithm was proposed to deal with this problem; it is based on a quantum-inspired cuckoo search algorithm. The other feature of the proposed approach is the use of a randomized progressive alignment method based on a hybrid global/local pairwise algorithm to construct the initial population. The results obtained by this hybridization are very encouraging and show the feasibility and effectiveness of the proposed solution.

THE HUFFMAN-LIKE ALIGNMENT IN MANIFOLD LEARNING

In manifold learning, the neighborhood is often called a patch of the manifold, and the corresponding open set is called the local coordinate of the patch. The so-called alignment is to align the local coordinates in the d-dimensional Euclidean space to get the global coordinate of the manifold. There are two kinds of alignment methods: global and progressive alignment methods. The global alignment methods align the local coordinates of the manifold all at one time by solving an eigenvalue problem. The progressive alignment methods often take the local coordinate of a patch as the basic local coordinate and then attach other local ordinates to the basic local coordinate patch-by-patch until the basic local coordinate evolves into the global coordinate of the manifold. In this paper, a new progressive alignment method is proposed, where only the local coordinates of the two patches with the largest intersection at the current stage of progressive alignment will be aligned into a larger local coordinate. It is inspired by the famous Huffman coding, where two random events with the smallest probabilities at the current phase will be merged into a random event with a larger probability. Therefore, the proposed method is a Huffman-like alignment method. The experiments on benchmark data show that the proposed method outperforms both the global alignment methods and the other progressive alignment methods and is more robust to the changes of data size. The experiments on real-world data show the feasibility of the proposed method.

Possible Social Deficit Improvement in the Autistic

Author(s): Rafanan, Vanessa | Abstract: Progressive alignment techniques can be utilized with clinically-diagnosed autistic childrento improve their social cognition, or, more specifically, their ability to recognize differentfacial expressions according to the associated feeling/emotion. Progressive alignmenttechniques (that involve comparison and categorization processes) have been shown tolead to changes in children’s representations of relational structure, allowing for them tomore easily identify more abstract features. In general, intensive, long-term, appliedbehavior treatment has already been shown to improve the cognitive, language, adaptive,and academic skills of children with autism. Further, other research has proven that theamygdala is also involved in the process of behavior reinforcement through rewards (orpositive reinforcement). This literature review, more specifically, aims to both A) supportthe idea that there is a positive correlation between progressive alignment methods andmildly autistic children with social deficits and B) demonstrate specific, potentialexperimental methods to support such a relationship

Progressive Alignment Method Using Genetic Algorithm for Multiple Sequence Alignment

In this paper, we have proposed a progressive alignment method using a genetic algorithm for multiple sequence alignment, named GAPAM. We have introduced two new mechanisms to generate an initial population: 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 GAPAM. To test the performance of our algorithm, we have compared it with existing well-known methods, such as 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. To make a fairer comparison with the GA based algorithms such as MSA-GA and RBT-GA, we have performed further experiments covering all the datasets reported by those two algorithms. The experimental results showed that GAPAM achieved better solutions than the others for most of the cases, and also revealed that the overall performance of the proposed method outperformed the other methods mentioned above.

A greedy, graph-based algorithm for the alignment of multiple homologous gene lists

Many comparative genomics studies rely on the correct identification of homologous genomic regions using accurate alignment tools. In such case, the alphabet of the input sequences consists of complete genes, rather than nucleotides or amino acids. As optimal multiple sequence alignment is computationally impractical, a progressive alignment strategy is often employed. However, such an approach is susceptible to the propagation of alignment errors in early pairwise alignment steps, especially when dealing with strongly diverged genomic regions. In this article, we present a novel accurate and efficient greedy, graph-based algorithm for the alignment of multiple homologous genomic segments, represented as ordered gene lists. Based on provable properties of the graph structure, several heuristics are developed to resolve local alignment conflicts that occur due to gene duplication and/or rearrangement events on the different genomic segments. The performance of the algorithm is assessed by comparing the alignment results of homologous genomic segments in Arabidopsis thaliana to those obtained by using both a progressive alignment method and an earlier graph-based implementation. Especially for datasets that contain strongly diverged segments, the proposed method achieves a substantially higher alignment accuracy, and proves to be sufficiently fast for large datasets including a few dozens of eukaryotic genomes. http://bioinformatics.psb.ugent.be/software. The algorithm is implemented as a part of the i-ADHoRe 3.0 package.

Alignment of, and phylogenetic inference from, random sequences: The susceptibility of alternative alignment methods to creating artifactual resolution and support

We used random sequences to determine which alignment methods are most susceptible to aligning sequences so as to create artifactual resolution and branch support in phylogenetic trees derived from those alignments. We compared four alignment methods (progressive pairwise alignment, simultaneous multiple alignment of sequence fragments, local pairwise alignment, and direct optimization) to determine which methods are most susceptible to creating false positives in phylogenetic trees. Implied alignments created using direct optimization provided more artifactual support than progressive pairwise alignment methods, which in turn generally provided more artifactual support than simultaneous and local alignment methods. Artifactual support derived from base pairs was generally reinforced by the incorporation of gap characters for progressive pairwise alignment, local pairwise alignment, and implied alignments. The amount of artifactual resolution and support was generally greater for simulated nucleotide sequences than for simulated amino acid sequences. In the context of direct optimization, the differences between static and dynamic approaches to calculating support were extreme, ranging from maximal to nearly minimal support. When applied to highly divergent sequences, it is important that dynamic, rather than static, characters be used whenever calculating branch support using direct optimization. In contrast to the tree-based approaches to alignment, simultaneous alignment of sequences using the similarity criterion generally does not create alignments that are biased in favor of any particular tree topology.

Phylogeny of the millipede order Spirobolida (Arthropoda: Diplopoda: Helminthomorpha).

This study examines relationships within the millipede order Spirobolida using an exemplar approach, sampling within families to maximize geographical and morphological diversity; due to lack of available material, Allopocockiidae and Hoffmanobolidae were not included in analyses. The focus of this study was to test monophyly of the order, the suborders, and the families of Spirobolida and to propose interfamilial relationships using morphological and molecular data in a total-evidence approach. Both maximum-parsimony analyses and Bayesian inference were employed to analyse two datasets consisting of combined morphological and molecular data, one aligned using progressive alignment methods and the second aligned by secondary structure models. Rhinocricidae was recovered sister to all remaining spirobolidan millipedes and is elevated to suborder status as suborder Rhinocricidea. Trigoniulidea was recovered as monophyletic as was Spirobolidea excluding Rhinocricidae; Spirobolidea is redefined to reflect this change. All previously recognized families were recovered, with the exception of Spirobolidae; in all instances, this family was paraphyletic or part of a polytomy that lacked sufficient resolution to assess its monophyly. The results reaffirm much of the existing taxonomic foundation within Spirobolida. This study provides the first phylogenetic test of higher-level relationships within Spirobolida and will serve as a foundation for future work in this group at finer levels. © The Willi Hennig Society 2010.

An Alignment Confidence Score Capturing Robustness to Guide Tree Uncertainty

Multiple sequence alignment (MSA) is the basis for a wide range of comparative sequence analyses from molecular phylogenetics to 3D structure prediction. Sophisticated algorithms have been developed for sequence alignment, but in practice, many errors can be expected and extensive portions of the MSA are unreliable. Hence, it is imperative to understand and characterize the various sources of errors in MSAs and to quantify site-specific alignment confidence. In this paper, we show that uncertainties in the guide tree used by progressive alignment methods are a major source of alignment uncertainty. We use this insight to develop a novel method for quantifying the robustness of each alignment column to guide tree uncertainty. We build on the widely used bootstrap method for perturbing the phylogenetic tree. Specifically, we generate a collection of trees and use each as a guide tree in the alignment algorithm, thus producing a set of MSAs. We next test the consistency of every column of the MSA obtained from the unperturbed guide tree with respect to the set of MSAs. We name this measure the "GUIDe tree based AligNment ConfidencE" (GUIDANCE) score. Using the Benchmark Alignment data BASE benchmark as well as simulation studies, we show that GUIDANCE scores accurately identify errors in MSAs. Additionally, we compare our results with the previously published Heads-or-Tails score and show that the GUIDANCE score is a better predictor of unreliably aligned regions.

Exploiting parallelism on progressive alignment methods

Multiple Sequence Alignment (MSA) constitutes an extremely powerful tool for important biological applications such as phylogenetic analysis, identification of conserved motifs and domains and structure prediction. In spite of the improvement in speed and accuracy introduced by MSA programs, the computational requirements for large-scale alignments requires high-performance computing and parallel applications. In this paper we present an improvement to a parallel implementation of T-Coffee, a widely used MSA package. Our approximation resolves the bottleneck of the progressive alignment stage on MSA. This is achieved by increasing the degree of parallelism by balancing the guide tree that drives the progressive alignment process. The experimental results show improvements in execution time of over 68% while maintaining the biological accuracy.

A model of evolution and structure for multiple sequence alignment

We have developed a phylogeny-aware progressive alignment method that recognizes insertions and deletions as distinct evolutionary events and thus avoids systematic errors created by traditional alignment methods. We now extend this method to simultaneously model regional heterogeneity and evolution. This novel method can be flexibly adapted to alignment of nucleotide or amino acid sequences evolving under processes that vary over genomic regions and, being fully probabilistic, provides an estimate of regional heterogeneity of the evolutionary process along the alignment and a measure of local reliability of the solution. Furthermore, the evolutionary modelling of substitution process permits adjusting the sensitivity and specificity of the alignment and, if high specificity is aimed at, leaving sequences unaligned when their divergence is beyond a meaningful detection of homology.

A protocol for large-scale rRNA sequence analysis: Towards a detailed phylogeny of Coleoptera

Large-scale phylogenetic analyses involving thousands of rRNA sequences are complicated due to length variability which compounds the already complex problem of large tree searches. Here, we generated a large data matrix and test phylogenetic procedures for large-scale analysis in the Coleoptera (beetles), as a resource for evolutionary biology and identification of this hugely diverse group. The analysis included nearly 1200 species, including representatives of 126 (75%) families, all 18 superfamilies of Polyphaga, and the four suborders. Alignments were obtained by a fragment-extension method derived from the BLAST algorithm using the BlastAlign script [Belshaw, R., Katzourakis, A., 2005. BlastAlign: a program that uses blast to align problematic nucleotide sequences. Bioinformatics 21, 122–123], followed by fast parsimony and maximum likelihood searches. Trees were assessed against the existing classification, using a formal procedure for coding the hierarchical position of taxa and establishing taxonomic congruence. We found that the BlastAlign procedure greatly exceeded the performance of standard progressive alignment methods such as Clustal. The resulting trees, when used as guide tree, also greatly improved the Clustal-based alignments. Long-branch attraction potentially affecting the quality of the tree was reduced by the systematic removal of all branches longer than a 95% interval of the distribution of branch lengths. We applied this protocol to the test for monophyly of major proposed lineages of Coleoptera, including Crowson’s 18 superfamilies in the hyperdiverse suborder Polyphaga. While searches for very large trees remained challenging and details of the tree topology were not always satisfactory, the strategy for alignment and tree searches used here makes large-scale phylogenetics of super-diverse groups such as Coleoptera amenable to desktop computing.

Multiple sequence alignment.

Publisher Summary This chapter discusses various aspects of multiple sequence alignment. Dynamic programming can be generalized to more than two sequences where one finds the multiple alignments with the best score according to the scoring scheme. Gaps are scored according to their length and the number of sequences that they occur in and the method will find a multiple alignment with the best possible score. Clustal W is perhaps the most widely used program for carrying out automatic multiple alignments. It uses a simple text menu system or command-line interface that is reasonably self-explanatory and simple to use. It allows one to carry out fully automatic and rapid alignments of dozens of sequences of moderate length using the progressive alignment method. The main menu leads to submenus for calculating phylogenetic trees from multiple alignments and for the alignment of existing alignments. The trees cannot be displayed by Clustal W but can be viewed using dedicated tree-viewing programs. The alignments that are produced by Clustal W and X are sent to simple text files. These show the sequences aligned in blocks and use symbols to indicate residues that are exactly conserved in columns or which show some degree of biochemical conservation in the columns. It is found that dedicated alignment editors can move large gaps quickly and can be persuaded to color the columns of residues depending on the degree of conservation and/or residue type.

An algorithm for progressive multiple alignment of sequences with insertions

Dynamic programming algorithms guarantee to find the optimal alignment between two sequences. For more than a few sequences, exact algorithms become computationally impractical, and progressive algorithms iterating pairwise alignments are widely used. These heuristic methods have a serious drawback because pairwise algorithms do not differentiate insertions from deletions and end up penalizing single insertion events multiple times. Such an unrealistically high penalty for insertions typically results in overmatching of sequences and an underestimation of the number of insertion events. We describe a modification of the traditional alignment algorithm that can distinguish insertion from deletion and avoid repeated penalization of insertions and illustrate this method with a pair hidden Markov model that uses an evolutionary scoring function. In comparison with a traditional progressive alignment method, our algorithm infers a greater number of insertion events and creates gaps that are phylogenetically consistent but spatially less concentrated. Our results suggest that some insertion/deletion "hot spots" may actually be artifacts of traditional alignment algorithms.