Parametric optimization of sequence alignment

Theoptimal alignment or theweighted minimum edit distance between two DNA or amino acid sequences for a given set of weights is computed by classical dynamic programming techniques, and is widely used in molecular biology. However, in DNA and amino acid sequences there is considerable disagreement about how to weight matches, mismatches, insertions/deletions (indels or spaces), and gaps.Parametric sequence alignment is the problem of computing the optimal-valued alignment between two sequences as afunction of variable weights for matches, mismatches, spaces, and gaps. The goal is to partition the parameter space into regions (which are necessarily convex) such that in each region one alignment is optimal throughout and such that the regions are maximal for this property. In this paper we are primarily concerned with the structure of this convex decomposition, and secondarily with the complexity of computing the decomposition. The most striking results are the following: For the special case where only matches, mismatches, and spaces are counted, and where spaces are counted throughout the alignment, we show that the decomposition is surprisingly simple: all regions are infinite; there are at most n^2/3 regions; the lines that bound the regions are all of the form =c + (c + 0.5); and the entire decomposition can be found inO(knm) time, wherek is the actual number of regions, andn相似文献   

Towards more accurate pharmacophore modeling: Multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2

Pharmacophore modeling, including ligand- and structure-based approaches, has become an important tool in drug discovery. However, the ligand-based method often strongly depends on the training set selection, and the structure-based pharmacophore model is usually created based on apo structures or a single protein-ligand complex, which might miss some important information. In this study, multicomplex-based method has been suggested to generate a comprehensive pharmacophore map of cyclin-dependent kinase 2 (CDK2) based on a collection of 124 crystal structures of human CDK2-inhibitor complex. Our multicomplex-based comprehensive pharmacophore map contains almost all the chemical features important for CDK2-inhibitor interactions. A comparison with previously reported ligand-based pharmacophores has revealed that the ligand-based models are just a subset of our comprehensive map. Furthermore, one most-frequent-feature pharmacophore model consisting of the most frequent pharmacophore features was constructed based on the statistical frequency information provided by the comprehensive map. Validations to the most-frequent-feature model show that it can not only successfully discriminate between known CDK2 inhibitors and the molecules of focused inactive dataset, but also is capable of correctly predicting the activities of a wide variety of CDK2 inhibitors in an external active dataset. Obviously, this investigation provides some new ideas about how to develop a multicomplex-based pharmacophore model that can be used in virtual screening to discover novel potential lead compounds.     

Iterative Grassmannian optimization for robust image alignment

Robust high-dimensional data processing has witnessed an exciting development in recent years. Theoretical results have shown that it is possible using convex programming to optimize data fit to a low-rank component plus a sparse outlier component. This problem is also known as robust PCA, and it has found application in many areas of computer vision. In image and video processing and face recognition, the opportunity to process massive image databases is emerging as people upload photo and video data online in unprecedented volumes. However, data quality and consistency is not controlled in any way, and the massiveness of the data poses a serious computational challenge. In this paper we present t-GRASTA, or “Transformed GRASTA (Grassmannian robust adaptive subspace tracking algorithm)”. t-GRASTA iteratively performs incremental gradient descent constrained to the Grassmann manifold of subspaces in order to simultaneously estimate three components of a decomposition of a collection of images: a low-rank subspace, a sparse part of occlusions and foreground objects, and a transformation such as rotation or translation of the image. We show that t-GRASTA is 4 × faster than state-of-the-art algorithms, has half the memory requirement, and can achieve alignment for face images as well as jittered camera surveillance images.     

Library design using BCUT chemistry-space descriptors and multiple four-point pharmacophore fingerprints: simultaneous optimization and structure-based diversity

New applications of fingerprints of multiple potential 4-point three-dimensional (3D) pharmacophores in combinatorial library design and virtual screening are presented. Preliminary results demonstrating the feasibility of a simulated annealing process for combinatorial reagent selection that concurrently optimizes product diversity in BCUT chemistry space and in terms of unique 4-point pharmacophores are discussed, and the advantage of using a customized chemistry-space derived for the library design is demonstrated. In addition, an extension to the multiple pharmacophore method for structure-based design that uses the shape of the target site as an additional constraint is presented. This development enables the docking process to be quantified in terms of the number and identities of the pharmacophoric hypotheses that can be matched by a compound or a library of compounds. The design of an example combinatorial library based on the Ugi condensation reaction and a serine protease active site is described.     

超大规模序列比对计算的并行优化

针对生物信息学研究中的超大规模序列比对计算问题进行了研究,解决了现有的e-PCR软件包在处理小麦基因引物扩增比对任务中存在的内存瓶颈、I/O瓶颈和计算时间瓶颈问题,利用数据和任务分割的基本方法,使其最关键的引物与模板的比对计算能够大规模并行,进而采用基于主从通信模式的MPI通信框架进行编程实现,并从任务的缩减、负载平衡、容错和多作业并发等方面进行了优化,最终在百万亿次超级计算机上顺利实现了千核级大规模并行计算,在数十日内即可完成原本预期需要数年的小麦序列扩增比对计算.     

基于混合优化和合作接收策略的干扰对齐算法

利用合作接收和混合优化策略,提出了一种干扰对齐算法,该算法可以在最小化泄漏干扰的同时最大化理想接收信号功率.首先通过接收机合作接收技术,接收端可以合作估计出发送端的预编码向量,从而无须事先知道预编码向量;然后利用干扰对齐问题的统计特性仅由干扰协方差矩阵之和就可以描述的原理,可设计出算法的合作接收的结构;最后通过一个基于合作博弈理论的讨价还价过程,可以选出对所有接收机最优的完全合作机制.仿真结果显示,相比传统的分布式干扰对齐机制,基于合作接收和混合优化技术的干扰对齐机制在各种发射功率条件下都具有更好的可行性.     

Mountain railway alignment optimization using stepwise & hybrid particle swarm optimization incorporating genetic operators

Optimizing railway alignments is a quite complex and time-consuming engineering problem. The huge continuous search space, complex constraints, implicit objective function and infinite potential alternatives of this problem pose many challenges. Especially in mountainous regions, finding a near-optimal alignment for extremely complex terrain and constraints is a most arduous task, which cannot be solved satisfactorily with most existing methods. In this study, a stepwise & hybrid particle swarm-genetic algorithm is developed for railway alignment optimization in mountainous regions. It is a continuous search method suitable for railway alignment design. A stepwise horizontal–vertical–integral approach which defines the horizontal and vertical alignments as two kinds of particles, is proposed to solve the three-dimensional railway alignment optimization problem. To enhance the initial diversity and momentum, butterfly-shaped areas are preset on a path generated with a bidirectional distance transform for initializing horizontal particles. For the solution method, specific genetic operators, including roulette wheel selection, four crossovers and two mutations are integrated into the stepwise particle swarm method to address parameter-dependent performance and avoid premature convergence. In addition, a cubic polynomial weight update strategy is employed for thoroughly searching the problem space. This synthesis method has been applied to a real-world case in a very mountainous region. The detailed data analyses demonstrate that it can offer more promising solutions compared with alternatives designed by experienced designers and those generated with a genetic algorithm or non-stepwise particle swarm algorithm.     

基于量子粒子群优化和隐马尔可夫模型的多序列比对算法*

针对用BaumWelch算法训练隐马尔可夫模型用于序列比对算法的搜索空间有限性容易陷入局部最优点的缺陷,提出一种用量子粒子群优化算法训练隐马尔可夫模型的生物多序列比对新方法。该方法克服了BaumWelch算法在收敛性能上的缺陷,在整个可行解空间中进行搜索。从BaliBASE数据库中选取测试例子进行数值实验,实验结果表明,所提算法优于BaumWelch算法,对标准例子进行的实验证明了算法的有效性。     

Control and trajectory optimization of an aircraft’s alignment with the desired track

The synthesis of the control with the optimal trajectory length is constructed for the problem of aligning an aircraft with the desired track with the ground speed vector direction along the desired track. The solution is based on the maximum principle and involves the use of numerical methods for solving the boundary value problem and analytical investigations for determining the set of possible extremals and comparing the lengths of the trajectories when selecting the optimal one. As a result of these investigations, the switching and separation lines are determined that bound the regions in the phase space which correspond to different values of the optimal relay control.     

Joint optimization of word alignment and epenthesis generation for Chinese to Taiwanese sign synthesis

This work proposes a novel approach to translate Chinese to Taiwanese sign language and to synthesize sign videos. An aligned bilingual corpus of Chinese and Taiwanese sign language (TSL) with linguistic and signing information is also presented for sign language translation. A two-pass alignment in syntax level and phrase level is developed to obtain the optimal alignment between Chinese sentences and Taiwanese sign sequences. For sign video synthesis, a scoring function is presented to develop motion transition-balanced sign videos with rich combinations of intersign transitions. Finally, the maximum a posteriori (MAP) algorithm is employed for sign video synthesis based on joint optimization of two-pass word alignment and intersign epenthesis generation. Several experiments are conducted in an educational environment to evaluate the performance on the comprehension of sign expression. The proposed approach outperforms the IBM Model2 in sign language translation. Moreover, deaf students perceived sign videos generated by the proposed method to be satisfactory     

General solution to kiln support reactions and multi-objective fuzzy optimization of kiln axis alignment

Based on the analyses of the mechanical features of rotary kiln with multi-supports, a general mechanical model for indeterminate kiln with variable bending rigidities, arbitrary supports and complex loads is established. From this model, the equations of the rotational angle and the deformation are deduced, the general matrix and procedure are developed. The correlation between the roller forces and axis deflections of no. 2 rotary kiln is derived. To improve kiln performance by kiln axis alignment, taking roller forces equilibrium and relative axis deflection minimum as the optimization goal, considering the fuzzy constraints of axis alignment, the fuzzy optimization model of kiln axis alignment is set up. The optimization model is solved with the max–min approach. The results show that fuzzy optimization alignment of rotary kiln can make kiln axis as straight as possible and can distribute kiln loads equally.     

Strategies for generating less toxic P-selectin inhibitors: pharmacophore modeling, virtual screening and counter pharmacophore screening to remove toxic hits

This paper describes the generation of ligand-based as well as structure-based models and virtual screening of less toxic P-selectin receptor inhibitors. Ligand-based model, 3D-pharmacophore was generated using 27 quinoline salicylic acid compounds and is used to retrieve the actives of P-selectin. This model contains three hydrogen bond acceptors (HBA), two ring aromatics (RA) and one hydrophobic feature (HY). To remove the toxic hits from the screened molecules, a counter pharmacophore model was generated using inhibitors of dihydrooratate dehydrogenase (DHOD), an important enzyme involved in nucleic acid synthesis, whose inhibition leads to toxic effects. Structure-based models were generated by docking and scoring of inhibitors against P-selectin receptor, to remove the false positives committed by pharmacophore screening. The combination of these ligand-based and structure-based virtual screening models were used to screen a commercial database containing 538,000 compounds.     

Analysis and optimization of structure-based virtual screening protocols. 2. Examination of docked ligand orientation sampling methodology: mapping a pharmacophore for success

An important element of any structure-based virtual screening (SVS) technique is the method used to orient the ligands in the target active site. This has been a somewhat overlooked issue in recent SVS validation studies, with the assumption being made that the performance of an algorithm for a given set of orientation sampling settings will be representative for the general behavior of said technique. Here, we analyze five different SVS targets using a variety of sampling paradigms within the DOCK, GOLD and PROMETHEUS programs over a data set of approximately 10,000 noise compounds, combined with data sets containing multiple active compounds. These sets have been broken down by chemotype, with chemotype hit rate used to provide a measure of enrichment with a potentially improved relevance to real world SVS experiments. The variability in enrichment results produced by different sampling paradigms is illustrated, as is the utility of using pharmacophores to constrain sampling to regions that reflect known structural biology. The difference in results when comparing chemotype with compound hit rates is also highlighted.     

Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment

Multiple sequence alignment, known as NP-complete problem, is among the most important and challenging tasks in computational biology. For multiple sequence alignment, it is difficult to solve this type of problems directly and always results in exponential complexity. In this paper, we present a novel algorithm of genetic algorithm with ant colony optimization for multiple sequence alignment. The proposed GA-ACO algorithm is to enhance the performance of genetic algorithm (GA) by incorporating local search, ant colony optimization (ACO), for multiple sequence alignment. In the proposed GA-ACO algorithm, genetic algorithm is conducted to provide the diversity of alignments. Thereafter, ant colony optimization is performed to move out of local optima. From simulation results, it is shown that the proposed GA-ACO algorithm has superior performance when compared to other existing algorithms.     

AlineaGA—a genetic algorithm with local search optimization for multiple sequence alignment

The alignment and comparison of DNA, RNA and Protein sequences is one of the most common and important tasks in Bioinformatics. However, due to the size and complexity of the search space involved, the search for the best possible alignment for a set of sequences is not trivial. Genetic Algorithms have a predisposition for optimizing general combinatorial problems and therefore are serious candidates for solving multiple sequence alignment tasks. Local search optimization can be used to refine the solutions explored by Genetic Algorithms. We have designed a Genetic Algorithm which incorporates local search for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the globin family. We also compare the achieved results with the results provided by T-COFFEE.     

A novel method for alignment of two nucleic acid sequences using ant colony optimization and genetic algorithms

We describe a new method for pairwise nucleic acid sequence alignment that can also be used for pattern searching and tandem repeat searching within a nucleic acid sequence. The method is broadly a hybrid algorithm employing ant colony optimization (ACO) and the simple genetic algorithm. The method first employs ACO to obtain a set of alignments, which are then further processed by an elitist genetic algorithm, which employs primitive selection and a novel multipoint crossover-mutation operator to generate accurate alignments. The resulting alignments show a fair amount of accuracy for smaller and medium size sequences. Furthermore, this algorithm can be used rather quickly and efficiently for aligning shorter sequences and also for pattern searching in both nucleic acid and amino acid sequences. Furthermore, it can be used as an effective local alignment method or as a global alignment tool. On improvement of accuracy, this method can be extended for use towards multiple sequence alignment.     

SGA: a grammar-based alignment algorithm

As the cost of genome sequencing continues to drop, comparison of large sequences becomes tantamount to our understanding of evolution and gene function. Rapid genome alignment stands to play a fundamental role in furthering biological understanding. In 2002, a fast algorithm based on statistical estimation called super pairwise alignment (SPA) was developed by Shen et al. The method was proved to be much faster than traditional dynamic programming algorithms, while it suffered small drop in accuracy. In this paper, we propose a new method based on SPA that target analysis of large-scale genomes. The new method, named super genome alignment (SGA), applies Yang-Keiffer coding theory to alignment and results in a grammar-based algorithm. SGA has the same computational complexity as its predecessor SPA, and it can process large-scale genomes. SGA is tested by using numerous pairs of microbial and eukaryotic genomes, which serve as the benchmark to compare it with the competing BLASTZ method. When compared with BLASTZ, the result shows that SGA is significantly faster by at least an order of magnitude (for some genome pairs the differences is as large at two orders of magnitude), and suffers on average only about 1% loss of the similarity of alignment.     

HMG-CoA还原酶的活性位点分析及其抑制剂药效团模型的构建

HMG-CoA还原酶(HMG-CoA Reductase,HMGR)是降血脂药物设计的重要靶标,抑制该酶的活性可以有效地降低血浆总胆固醇水平,从而降低心脑血管疾病的发病几率。虽然已经开发了数种他汀类药物作为HMGR抑制剂应用于临床,但是他汀类药物的安全性,特别是长期服用的安全性一直备受关注,所以设计新型安全的HMGR抑制剂仍然十分迫切。本论文利用蛋白质活性位点分析程序Grid,分析了HMGR底物结合腔的形状和表面特性,在细致地分析了各类药物与HMGR具体的氢键、疏水相互作用后,结合分子对接、3D-QSAR研究结果,总结了HMGR抑制剂的药效基团模型,并提出了可行的HMGR抑制剂的设计方案,为全新HMGR抑制剂的设计和先导化合物的优化提供了可靠的信息,并对HMGR抑制剂的进一步修饰提出了可行的思路。     

Engineering trust alignment: Theory,method and experimentation

In open multi-agent systems trust models are an important tool for agents to achieve effective interactions. However, in these kinds of open systems, the agents do not necessarily use the same, or even similar, trust models, leading to semantic differences between trust evaluations in the different agents. Hence, to successfully use communicated trust evaluations, the agents need to align their trust models. We explicate that currently proposed solutions, such as common ontologies or ontology alignment methods, lead to additional problems and propose a novel approach. We show how the trust alignment can be formed by considering the interactions that agents share and describe a mathematical framework to formulate precisely how the interactions support trust evaluations for both agents. We show how this framework can be used in the alignment process and explain how an alignment should be learned. Finally, we demonstrate this alignment process in practice, using a first-order regression algorithm, to learn an alignment and test it in an example scenario.