基于Progressive多序列比对方法的求解多序列比对的启发式算法

在生物信息学研究中,生物序列比对问题占有重要的地位。多序列比对问题是一个NPC问题,由于时间和空间的限制不能够求出精确解。文中简要介绍了Feng和Doolittle提出的多序列比对算法的基本思想,并改进了该算法使之具有更好的比对精度。实验结果表明,新算法对解决一般的progressive多序列比对方法中遇到的局部最优问题有较好的效果。     

A simulated annealing algorithm for finding consensus sequences

MOTIVATION: A consensus sequence for a family of related sequences is, as the name suggests, a sequence that captures the features common to most members of the family. Consensus sequences are important in various DNA sequencing applications and are a convenient way to characterize a family of molecules. RESULTS: This paper describes a new algorithm for finding a consensus sequence, using the popular optimization method known as simulated annealing. Unlike the conventional approach of finding a consensus sequence by first forming a multiple sequence alignment, this algorithm searches for a sequence that minimises the sum of pairwise distances to each of the input sequences. The resulting consensus sequence can then be used to induce a multiple sequence alignment. The time required by the algorithm scales linearly with the number of input sequences and quadratically with the length of the consensus sequence. We present results demonstrating the high quality of the consensus sequences and alignments produced by the new algorithm. For comparison, we also present similar results obtained using ClustalW. The new algorithm outperforms ClustalW in many cases.     

MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing

MOTIVATION: As more non-coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction. RESULT: We present a novel solution to the problem of simultaneous structure prediction and multiple alignment of RNA sequences. Using Markov chain Monte Carlo in a simulated annealing framework, the algorithm MASTR (Multiple Alignment of STructural RNAs) iteratively improves both sequence alignment and structure prediction for a set of RNA sequences. This is done by minimizing a combined cost function that considers sequence conservation, covariation and basepairing probabilities. The results show that the method is very competitive to similar programs available today, both in terms of accuracy and computational efficiency. AVAILABILITY: Source code available from http://mastr.binf.ku.dk/     

A simulated annealing algorithm for maximum likelihood pedigree reconstruction

The calculation of maximum likelihood pedigrees for related organisms using genotypic data is considered. The problem is formulated so that the domain of optimization is a permutation space. This is a feature shared by the travelling salesman problem, for which simulated annealing is known to be effective. Using this technique it is found that pedigrees can be reconstructed with minimal error using genotypic data of a quality currently realizable. In complex pedigrees accurate reconstruction can be done with no a priori age or sex information. For smaller numbers of individuals a method of efficiently enumerating all admissible pedigrees of nonzero likelihood is given.     

Multiple sequence alignment by parallel simulated annealing

We have developed simulated annealing algorithms to solve theproblem of multiple sequence alignment. The algorithm wns shownto give the optimal solution as confirmed by the rigorous dynamicprogramming algorithm for three-sequence alignment. To overcomelong execution times for simulated annealing, we utilized aparallel computer. A sequential algorithm, a simple parallelalgorithm and the temperature parallel algorithm were testedon a problem. The results were compared with the result obtainedby a conventional tree-based algorithm where alignments weremerged by two-' dynamic programming. Every annealing algorithmproduced a better energy value than the conventional algorithm.The best energy value, which probably represents the optimalsolution, wns reached within a reasonable time by both of theparallel annealing algorithms. We consider the temperature parallelalgorithm of simulated annealing to be the most suitable forfinding the optimal multiple sequence alignment because thealgorithm does not require any scheduling for optimization.The algorithm is also usefiui for refining multiple alignmentsobtained by other hewistic methods.     

A simple genetic algorithm for multiple sequence alignment

Multiple sequence alignment plays an important role in molecular sequence analysis. An alignment is the arrangement of two (pairwise alignment) or more (multiple alignment) sequences of 'residues' (nucleotides or amino acids) that maximizes the similarities between them. Algorithmically, the problem consists of opening and extending gaps in the sequences to maximize an objective function (measurement of similarity). A simple genetic algorithm was developed and implemented in the software MSA-GA. Genetic algorithms, a class of evolutionary algorithms, are well suited for problems of this nature since residues and gaps are discrete units. An evolutionary algorithm cannot compete in terms of speed with progressive alignment methods but it has the advantage of being able to correct for initially misaligned sequences; which is not possible with the progressive method. This was shown using the BaliBase benchmark, where Clustal-W alignments were used to seed the initial population in MSA-GA, improving outcome. Alignment scoring functions still constitute an open field of research, and it is important to develop methods that simplify the testing of new functions. A general evolutionary framework for testing and implementing different scoring functions was developed. The results show that a simple genetic algorithm is capable of optimizing an alignment without the need of the excessively complex operators used in prior study. The clear distinction between objective function and genetic algorithms used in MSA-GA makes extending and/or replacing objective functions a trivial task.     

A new progressive-iterative algorithm for multiple structure alignment

MOTIVATION: Multiple structure alignments are becoming important tools in many aspects of structural bioinformatics. The current explosion in the number of available protein structures demands multiple structural alignment algorithms with an adequate balance of accuracy and speed, for large scale applications in structural genomics, protein structure prediction and protein classification. RESULTS: A new multiple structural alignment program, MAMMOTH-mult, is described. It is demonstrated that the alignments obtained with the new method are an improvement over previous manual or automatic alignments available in several widely used databases at all structural levels. Detailed analysis of the structural alignments for a few representative cases indicates that MAMMOTH-mult delivers biologically meaningful trees and conservation at the sequence and structural levels of functional motifs in the alignments. An important improvement over previous methods is the reduction in computational cost. Typical alignments take only a median time of 5 CPU seconds in a single R12000 processor. MAMMOTH-mult is particularly useful for large scale applications. AVAILABILITY: http://ub.cbm.uam.es/mammoth/mult.     

基于模拟退火算法的高分辨率蛋白质质谱数据特征选择

蛋白质质谱技术是蛋白质组学的重要研究工具,它被出色地应用于癌症早期诊断等领域,但是蛋白质质谱数据带来的维灾难问题使得降维成为质谱分析的必需的步骤。本文首先将美国国家癌症研究所提供的高分辨率SELDI—TOF卵巢质谱数据进行预处理;然后将质谱数据的特征选择问题转化成基于模拟退火算法的组合优化模型,用基于线性判别式分析的分类错误率和样本后验概率构造待优化目标函数,用基于均匀分布和控制参数的方法构造新解产生器,在退火过程中添加记忆功能;然后用10-fold交叉验证法选择训练和测试样本,用线性判别式分析分类器评价降维后的质谱数据。实验证明,用模拟退火算法选择6个以上特征时,能够将高分辨率SELDI—TOF卵巢质谱数据全部正确分类,说明模拟退火算法可以很好地应用于蛋白质质谱数据的特征选择。     

A memory-efficient algorithm for multiple sequence alignment with constraints

MOTIVATION: Recently, the concept of the constrained sequence alignment was proposed to incorporate the knowledge of biologists about structures/functionalities/consensuses of their datasets into sequence alignment such that the user-specified residues/nucleotides are aligned together in the computed alignment. The currently developed programs use the so-called progressive approach to efficiently obtain a constrained alignment of several sequences. However, the kernels of these programs, the dynamic programming algorithms for computing an optimal constrained alignment between two sequences, run in (gamman2) memory, where gamma is the number of the constraints and n is the maximum of the lengths of sequences. As a result, such a high memory requirement limits the overall programs to align short sequences only. RESULTS: We adopt the divide-and-conquer approach to design a memory-efficient algorithm for computing an optimal constrained alignment between two sequences, which greatly reduces the memory requirement of the dynamic programming approaches at the expense of a small constant factor in CPU time. This new algorithm consumes only O(alphan) space, where alpha is the sum of the lengths of constraints and usually alpha < n in practical applications. Based on this algorithm, we have developed a memory-efficient tool for multiple sequence alignment with constraints. AVAILABILITY: http://genome.life.nctu.edu.tw/MUSICME.     

A tabu search algorithm for post-processing multiple sequence alignment

Tabu search is a meta-heuristic approach that is proven to be useful in solving combinatorial optimization problems. We implement the adaptive memory features of tabu search to refine a multiple sequence alignment. Adaptive memory helps the search process to avoid local optima and explores the solution space economically and effectively without getting trapped into cycles. The algorithm is further enhanced by introducing extended tabu search features such as intensification and diversification. The neighborhoods of a solution are generated stochastically and a consistency-based objective function is employed to measure its quality. The algorithm is tested with the datasets from BAliBASE benchmarking database. We have observed through experiments that tabu search is able to improve the quality of multiple alignments generated by other software such as ClustalW and T-Coffee. The source code of our algorithm is available at http://www.bii.a-star.edu.sg/~tariq/tabu/.     

ODS: ordering DNA sequences--a physical mapping algorithm based on simulated annealing

In the program ODS we provide a methodology for quickly orderingrandom clones into a physical map. The process of ordering individualclones with respect to their position along a chromosome isbased on the similarity of binary signatures assigned to eachclone. This binary signature is obtained by hybridizing eachclone to a panel of oligonucleotide probes. By using the factthat the amount of overlap between any two clones is reflectedin the similarity of their binary signatures, it is possibleto reconstruct a chromosome by minimizing the sum of linkingdistances between an ordered sequence of clones. Unlike otherprograms for physical mapping, ODS is very general in the typesof data that can be utilized for chromosome reconstruction.Any trait that can be scored in a presence–absence manner,such as hybridized synthetic oligonucleotides, restriction endonucleaserecognition sites or single copy landmarks, can be used foranalysis. Furthermore, the computational requirements for theconstruction of large physical maps can be measured in a matterof hours on workstations such as the VAX2000.     

An iterative refinement algorithm for consistency based multiple structural alignment methods

MOTIVATION: Multiple STructural Alignment (MSTA) provides valuable information for solving problems such as fold recognition. The consistency-based approach tries to find conflict-free subsets of alignments from a pre-computed all-to-all Pairwise Alignment Library (PAL). If large proportions of conflicts exist in the library, consistency can be hard to get. On the other hand, multiple structural superposition has been used in many MSTA methods to refine alignments. However, multiple structural superposition is dependent on alignments, and a superposition generated based on erroneous alignments is not guaranteed to be the optimal superposition. Correcting errors after making errors is not as good as avoiding errors from the beginning. Hence it is important to refine the pairwise library to reduce the number of conflicts before any consistency-based assembly. RESULTS: We present an algorithm, Iterative Refinement of Induced Structural alignment (IRIS), to refine the PAL. A new measurement for the consistency of a library is also proposed. Experiments show that our algorithm can greatly improve T-COFFEE performance for less consistent pairwise alignment libraries. The final multiple alignment outperforms most state-of-the-art MSTA algorithms at assembling 15 transglycosidases. Results on three other benchmarks showed that the algorithm consistently improves multiple alignment performance. AVAILABILITY: The C++ code of the algorithm is available upon request.     

A genome alignment algorithm based on compression

Background  

Traditional genome alignment methods consider sequence alignment as a variation of the string edit distance problem, and perform alignment by matching characters of the two sequences. They are often computationally expensive and unable to deal with low information regions. Furthermore, they lack a well-principled objective function to measure the performance of sets of parameters. Since genomic sequences carry genetic information, this article proposes that the information content of each nucleotide in a position should be considered in sequence alignment. An information-theoretic approach for pairwise genome local alignment, namely XMAligner, is presented. Instead of comparing sequences at the character level, XMAligner considers a pair of nucleotides from two sequences to be related if their mutual information in context is significant. The information content of nucleotides in sequences is measured by a lossless compression technique.     

A fast and sensitive multiple sequence alignment algorithm

A two-step multiple alignment strategy is presented that allowsrapid alignment of a set of homologous sequences and comparisonof pre-aligned groups of sequences. Examples are given demonstratingthe improvement in the quality of alignments when comparingentire groups instead of single sequences. The modular designof computer programs based on this algorithm allows for storageof aligned sequences and successive alignment of any numberof sequences. Received on August 23, 1988; accepted on December 6, 1988     

Constructing phylogenetic supernetworks based on simulated annealing

Different partial phylogenetic trees can be derived from different sources of evidence and different methods. One important problem is to summarize these partial phylogenetic trees using a supernetwork. We propose a novel simulated annealing based method called SNSA which uses an optimization function to produce a simple network that still retains a great deal of phylogenetic information. We report the performance of this new method on real and simulated datasets.     

A novel method of multiple alignment of biopolymer sequences

A novel algorithm for multiple alignment of biological sequences is suggested. At the first step the DotHelix procedure is employed for construction of motifs, i.e. continuous fragments of local similarity of various “thickness” and strength, and then these motifs are concatenated into chains consistent with the order of letters in the sequences. The algorithm is implemented in the MA-Tools program of the GeneBee package. An example illustrating the effectivity of the algorithm is presented.     

Development and validation of a consistency based multiple structure alignment algorithm

SUMMARY: We introduce an algorithm that uses the information gained from simultaneous consideration of an entire group of related proteins to create multiple structure alignments (MSTAs). Consistency-based alignment (CBA) first harnesses the information contained within regions that are consistently aligned among a set of pairwise superpositions in order to realign pairs of proteins through both global and local refinement methods. It then constructs a multiple alignment that is maximally consistent with the improved pairwise alignments. We validate CBA's alignments by assessing their accuracy in regions where at least two of the aligned structures contain the same conserved sequence motif. RESULTS: CBA correctly aligns well over 90% of motif residues in superpositions of proteins belonging to the same family or superfamily, and it outperforms a number of previously reported MSTA algorithms.     

A core-attachment based method to detect protein complexes in PPI networks

Background  

How to detect protein complexes is an important and challenging task in post genomic era. As the increasing amount of protein-protein interaction (PPI) data are available, we are able to identify protein complexes from PPI networks. However, most of current studies detect protein complexes based solely on the observation that dense regions in PPI networks may correspond to protein complexes, but fail to consider the inherent organization within protein complexes.     

MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform

A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.