Relevant bibliographies by topics / Pairwise sequence alignment

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Pairwise sequence alignment'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 June 2025

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Journal articles on the topic "Pairwise sequence alignment"

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Staritzbichler, René, Edoardo Sarti, Emily Yaklich, et al. "Refining pairwise sequence alignments of membrane proteins by the incorporation of anchors." PLOS ONE 16, no. 4 (2021): e0239881. http://dx.doi.org/10.1371/journal.pone.0239881.

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The alignment of primary sequences is a fundamental step in the analysis of protein structure, function, and evolution, and in the generation of homology-based models. Integral membrane proteins pose a significant challenge for such sequence alignment approaches, because their evolutionary relationships can be very remote, and because a high content of hydrophobic amino acids reduces their complexity. Frequently, biochemical or biophysical data is available that informs the optimum alignment, for example, indicating specific positions that share common functional or structural roles. Currently, if those positions are not correctly matched by a standard pairwise sequence alignment procedure, the incorporation of such information into the alignment is typically addressed in an ad hoc manner, with manual adjustments. However, such modifications are problematic because they reduce the robustness and reproducibility of the aligned regions either side of the newly matched positions. Previous studies have introduced restraints as a means to impose the matching of positions during sequence alignments, originally in the context of genome assembly. Here we introduce position restraints, or “anchors” as a feature in our alignment tool AlignMe, providing an aid to pairwise global sequence alignment of alpha-helical membrane proteins. Applying this approach to realistic scenarios involving distantly-related and low complexity sequences, we illustrate how the addition of anchors can be used to modify alignments, while still maintaining the reproducibility and rigor of the rest of the alignment. Anchored alignments can be generated using the online version of AlignMe available at www.bioinfo.mpg.de/AlignMe/.
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Lakshmi, Naga Jayaprada.Gavarraju, and Karteeka Pavan K. "Pairwise Sequence Alignment by Differential Evolutionary Algorithm with New Mutation Strategy." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 445–53. https://doi.org/10.35940/ijeat.B3136.129219.

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Sequence alignment is a significant facet in the bio-informatics research field for the molecular sequence analysis. Arrangement of two biological sequences by maximizing the similarities between the sequences by incorporating and adjusting gaps is Pairwise Sequence Alignment (PSA). Arrangement of multiple sequences is Multiple Sequence Alignment (MSA). Though Dynamic programming can produce optimal sequence alignment for PSA it suffers from a problem when multiple optimal paths are present and trace back is required. Back tracking becomes complex and it is also not suitable for MSA. So many meta-heuristic algorithms like Genetic Algorithm (GA) and Differential Evolutionary Algorithm (DE) are developed in the recent years to resolve the issue of optimization. Both GA and DE are used to produce optimal sequence alignment. But Compared to GA, DE is able to produce more optimal sequence alignment. To further enhance the performance of DE a new mutant is proposed by considering best, worst and a random candidate solution and applied on DE. It is named as New Differential Evolutionary Algorithm (NDE). By taking the test sequences from a bench mark data set “prefab4ref” tests are performed on GA, All DE mutants and NDE and it is observed that the proposed algorithm NDE outperformed all the other algorithms.
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PENG, YUNG-HSING, CHANG-BIAU YANG, KUO-TSUNG TSENG, and KUO-SI HUANG. "AN ALGORITHM AND APPLICATIONS TO SEQUENCE ALIGNMENT WITH WEIGHTED CONSTRAINTS." International Journal of Foundations of Computer Science 21, no. 01 (2010): 51–59. http://dx.doi.org/10.1142/s012905411000712x.

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Given two sequences S1, S2, and a constrained sequence C, a longest common subsequence of S1, S2 with restriction to C is called a constrained longest common subsequence of S1 and S2 with C. At the same time, an optimal alignment of S1, S2 with restriction to C is called a constrained pairwise sequence alignment of S1 and S2 with C. Previous algorithms have shown that the constrained longest common subsequence problem is a special case of the constrained pairwise sequence alignment problem, and that both of them can be solved in O(rnm) time, where r, n, and m represent the lengths of C, S1, and S2, respectively. In this paper, we extend the definition of constrained pairwise sequence alignment to a more flexible version, called weighted constrained pairwise sequence alignment, in which some constraints might be ignored. We first give an O(rnm)-time algorithm for solving the weighted constrained pairwise sequence alignment problem, then show that our extension can be adopted to solve some constraint-related problems that cannot be solved by previous algorithms for the constrained longest common subsequence problem or the constrained pairwise sequence alignment problem. Therefore, in contrast to previous results, our extension is a new and suitable model for sequence analysis.
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DeRonne, Kevin W., and George Karypis. "Pareto Optimal Pairwise Sequence Alignment." IEEE/ACM Transactions on Computational Biology and Bioinformatics 10, no. 2 (2013): 481–93. http://dx.doi.org/10.1109/tcbb.2013.2.

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Sierk, Michael L., Michael E. Smoot, Ellen J. Bass, and William R. Pearson. "Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments." BMC Bioinformatics 11, no. 1 (2010): 146. http://dx.doi.org/10.1186/1471-2105-11-146.

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Tyson, Hugh. "Relationships between amino acid sequences determined through optimum alignments, clustering, and specific distance patterns: application to a group of scorpion toxins." Genome 35, no. 2 (1992): 360–71. http://dx.doi.org/10.1139/g92-055.

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Optimum alignment in all pairwise combinations among a group of amino acid sequences generated a distance matrix. These distances were clustered to evaluate relationships among the sequences. The degree of relationship among sequences was also evaluated by calculating specific distances from the distance matrix and examining correlations between patterns of specific distances for pairs of sequences. The sequences examined were a group of 20 amino acid sequences of scorpion toxins originally published and analyzed by M.J. Dufton and H. Rochat in 1984. Alignment gap penalties were constant for all 190 pairwise sequence alignments and were chosen after assessing the impact of changing penalties on resultant distances. The total distances generated by the 190 pairwise sequence aligments were clustered using complete (farthest neighbour) linkage. The square, symmetrical input distance matrix is analogous to diallel cross data where reciprocal and parental values are absent. Diallel analysis methods provided analogues for the distance matrix to genetical specific combining abilities, namely specific distances between all sequence pairs that are independent of the average distances shown by individual sequences. Correlation of specific distance patterns, with transformation to modified z values and a stringent probability level, were used to delineate subgroups of related sequences. These were compared with complete linkage clustering results. Excellent agreement between the two approaches was found. Three originally outlying sequences were placed within the four new subgroups.Key words: sequence alignment, specific distances, sequence relationships.
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Taneda, Akito. "Multi-objective pairwise RNA sequence alignment." Bioinformatics 26, no. 19 (2010): 2383–90. http://dx.doi.org/10.1093/bioinformatics/btq439.

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Li, Junjie, Sanjay Ranka, and Sartaj Sahni. "Pairwise sequence alignment for very long sequences on GPUs." International Journal of Bioinformatics Research and Applications 10, no. 4/5 (2014): 345. http://dx.doi.org/10.1504/ijbra.2014.062989.

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Adi Sunarto, Asril, and Prajoko Prajoko. "NEEDLEMAN-WUNSCH AND SMITH-WATERMAN COMBINATIONS IN PAIRWISE ALIGNMENT." Jurnal Mnemonic 7, no. 2 (2024): 140–43. http://dx.doi.org/10.36040/mnemonic.v7i2.9501.

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Identification of Deoxyribonucleic acid (DNA), Ribonucleic acid (RNA), or protein needs to be done to find functional, structural, or evolutionary relationships between two sequences. There are various applications that already exist such as one of them EMBOSS either web or desktop versions. There are drawbacks to this application, such as repeatedly processing each user who needs sequence alignment results locally and globally at the same time. Therefore, we designed an application that can generate two sequence alignment outputs both locally and globally at the same time with pairwise alignment Needleman-Wunsch and Smith-Waterman. The result show that methods can be produces two outputs of sequence alignment in the same process. The impact is it can reduce the waiting time for users.
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Lipták, Panna, Attila Kiss, and János Márk Szalai-Gindl. "Heuristic Pairwise Alignment in Database Environments." Genes 13, no. 11 (2022): 2005. http://dx.doi.org/10.3390/genes13112005.

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Biological data have gained wider recognition during the last few years, although managing and processing these data in an efficient way remains a challenge in many areas. Increasingly, more DNA sequence databases can be accessed; however, most algorithms on these sequences are performed outside of the database with different bioinformatics software. In this article, we propose a novel approach for the comparative analysis of sequences, thereby defining heuristic pairwise alignment inside the database environment. This method takes advantage of the benefits provided by the database management system and presents a way to exploit similarities in data sets to quicken the alignment algorithm. We work with the column-oriented MonetDB, and we further discuss the key benefits of this database system in relation to our proposed heuristic approach.
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Dissertations / Theses on the topic "Pairwise sequence alignment"

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Ahmed, Nova. "Parallel Algorithm for Memory Efficient Pairwise and Multiple Genome Alignment in Distributed Environment." Digital Archive @ GSU, 2004. http://digitalarchive.gsu.edu/cs_theses/2.

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The genome sequence alignment problems are very important ones from the computational biology perspective. These problems deal with large amount of data which is memory intensive as well as computation intensive. In the literature, two separate algorithms have been studied and improved – one is a Pairwise sequence alignment algorithm which aligns pairs of genome sequences with memory reduction and parallelism for the computation and the other one is the multiple sequence alignment algorithm that aligns multiple genome sequences and this algorithm is also parallelized efficiently so that the workload of the alignment program is well distributed. The parallel applications can be launched on different environments where shared memory is very well suited for these kinds of applications. But shared memory environment has the limitation of memory usage as well as scalability also these machines are very costly. A better approach is to use the cluster of computers and the cluster environment can be further enhanced to a grid environment so that the scalability can be improved introducing multiple clusters. Here the grid environment is studied as well as the shared memory and cluster environment for the two applications. It can be stated that for carefully designed algorithms the grid environment is comparable for its performance to other distributed environments and it sometimes outperforms the others in terms of the limitations of resources the other distributed environments have.
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Cuevas, Tristan Lee. "A PAIRWISE COMPARISON OF DNA SEQUENCE ALIGNMENT USING AN OPENMP IMPLEMENTATION OF THE SWAMP PARALLEL SMITH-WATERMAN ALGORITHM." Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1429528937.

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Darbha, Sriram. "RNA Homology Searches Using Pair Seeding." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/1172.

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Due to increasing numbers of non-coding RNA (ncRNA) being discovered recently, there is interest in identifying homologs of a given structured RNA sequence. Exhaustive homology searching for structured RNA molecules using covariance models is infeasible on genome-length sequences. Hence, heuristic methods are employed, but they largely ignore structural information in the query. We present a novel method, which uses secondary structure information, to perform homology searches for a structured RNA molecule. We define the concept of a <em>pair seed</em> and theoretically model alignments of random and related paired regions to compute expected sensitivity and specificity. We show that our method gives theoretical gains in sensitivity and specificity compared to a BLAST-based heuristic approach. We provide experimental verification of this gain. <br /><br /> We also show that pair seeds can be effectively combined with the spaced seeds approach to nucleotide homology search. The hybrid search method has theoretical specificity superior to that of the BLAST seed. We provide experimental evaluation of our hypotheses. Finally, we note that our method is easily modified to process pseudo-knotted regions in the query, something outside the scope of covariance model based methods.
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Montañola, Lacort Alberto. "The pairwise problem with High Performance Computing Systems, contextualized as a key part to solve the Multiple Sequence Alignment problem." Doctoral thesis, Universitat de Lleida, 2016. http://hdl.handle.net/10803/385282.

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L'alineació múltiple de seqüencies (MSA), com a repte dins de la bioinformàtica, es un element clau per entendre el funcionament del genoma. Aquest consisteix en alinear en un temps òptim aquestes seqüencies garantint un nivell de qualitat. Aquest problema esdevé un repte de computació de altes prestacions degut als requeriments de recursos de memòria i còmput. S'han estudiat diferents implementacions, les quals es comparen i es presenten en aquesta investigació. Hem contribuït en la millora dels primers passos del problema MSA de diverses maneres. Amb l'objectiu de reduir el temps de càlcul i l'ús de memòria, adaptem T-Coffee per treballar en paral·lel amb ús de fils lleugers. Seguidament, hem desenvolupat un mètode de alineació de parells paral·lel, amb una assignació eficient de seqüències a nodes. Finalment es presenta un mètode per determinar la quantitat mínima de recursos del sistema, necessaris per resoldre un problema d'una mida determinada, per tal de configurar el sistema per un ús eficient.<br>El alineamiento múltiple de secuencias (MSA), como reto dentro de la bioinformática, es un elemento clave para entender el funcionamiento del genoma. Este consiste en alinear en un tiempo óptimo esta secuencias garantizando un nivel de calidad. Este problema es un reto de computo de altas prestaciones debido a los altos requerimientos de memoria y computo. Se han estudiado diferentes implementaciones, las cuales se comparan y se presentan en esta investigación. Hemos contribuido en la mejora de los primeros pasos del problema MSA de diversas formas. Con el objetivo de reducir el tiempo de cálculo y el uso de memoria, adaptamos T-Coffee para trabajar en paralelo con el uso de hilos ligeros. Seguidamente, hemos desarrollado un método de alineación de pares en paralelo, con una asignación eficiente de secuencias a nodos. Finalmente se presenta un método para determinar la cantidad mínima de recursos del sistema, necesarios para resolver el problema de un tamaño determinado, para poder configurar el sistema para un uso eficiente.<br>The multiple sequence alignment (MSA), as a challenge in bioinformatics, becomes a key element for understanding the inner working of the genome. This consists on aligning these sequences in an optimal time, with a good level of quality. This problem is a challenge for the high performance computing, because of the high memory and processing requirements. Different implementations were studied, which are being compared and presented on this thesis. We have contributed in the improvement of the first steps of the MSA problem in different ways. With the goal of reducing the computing time and the memory usage, we adapted T-Coffee for working in parallel with the usage of threads. Furthermore, we have developed a pair-wise sequence alignment method, with an efficient mapping of sequences to nodes. Finally, we are presenting the method for determining the minimal amount of resources, required for solving the problem of a determined size, in order to configure the system for an efficient use.
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Hunziker, Angela. "Improving multiple sequence alignments using information from libraries of optimal pairwise alignments /." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, [Departement für Informatik, Institut für Wissenschaftliches Rechnen], 2003. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=117.

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Nosek, Ondřej. "Hardwarová akcelerace algoritmu pro hledání podobnosti dvou DNA řetězců." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2007. http://www.nusl.cz/ntk/nusl-236882.

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Methods for aproximate string matching of various sequences used in bioinformatics are crucial part of development in this branch. Tasks are of very large time complexity and therefore we want create a hardware platform for acceleration of these computations. Goal of this work is to design a generalized architecture based on FPGA technology, which can work with various types of sequences. Designed acceleration card will use especially dynamic algorithms like Needleman-Wunsch and Smith-Waterman.
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Trněný, Ondřej. "Metody vícenásobného zarovnávání nukleotidových sekvencí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220010.

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To be able to understand characteristics and purpose of biological sequences correctly, it is crucial to have a possibility to sort and compare them. Because of this need and to extend existing knowledge pool, numerous methods were proposed. Especially in field of multiple sequences alignment. Methods for multiple sequences alignment may provide various valuable information about sequences which failed to show enough similarity in pairwise alignment. According to this, several algorithms were implemented in various computer applications which provide a way to analyse huge sets of data. One of those, the progressive alignment algorithm, is implemented as a part of this thesis
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Ying, Chung Li, and 鍾立穎. "Multiple Sequence Alignment using Pairwise Suboptimal Alignment." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/78872248303598965142.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>94<br>Multiple sequence alignment is an important tool to analysis biological sequence from searching similar sequence in database to protein structure. The optimal solution of dynamic programming is not always real biological solution when the number of sequence is increasing. Another method is progressive algorithm, it combined most similar sequence and then added next similar sequence. But the order of combining sequence have different alignment. Due to the optimal alignment is not always the best alignment in biological alignment, combining the pairwise suboptimal alignment have the possibility to find a better solution. The method also can decrease the time complexity. On the other hand, there is a possibility to find better alignment when we take a few time to try all combination.
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Zhang, Yan. "Generic C++ implementations of pairwise sequence alignment : instantiation for global alignment." Thesis, 2003. http://spectrum.library.concordia.ca/2358/1/MQ83924.pdf.

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Sequence comparison and alignment is a central problem in computational biology. Pairwise sequence alignment of protein or nucleic acid sequences is the foundation upon which most bioinformatics tools are built. EMBOSS has several pairwise alignment algorithms implemented in C, but they are quite dependable on the algorithms. Recently, generic programming has emerged as a programming paradigm capable of providing high levels of performance and re-usability in the presence of in vast numbers of ways to yield very efficient concrete programs. Pairwise alignment algorithms are optimization problems; from the view of a design, all the pairwise alignment shares the most common entities. This report designs and implements an application for pairwise Sequence Alignment using a generic programming approach in C++. Object-oriented programming principles are used for design; generic parameter and parameterized components mechanism in the C++ language are used for implementing this application, from which user can derive instantiations for any pairwise alignment algorithms of interest, this part can be considered as a framework in this application. Semi-global alignment algorithm is instantiated in this project. This implementation offers the possibility for the programmer to instantiate any kind of pairwise alignment algorithm with little efforts and basic knowledge of the C++ language. The implementation provides both robustness and re-usability properties.
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Yang, Xiao. "Generic C++ implementations of pairwise sequence alignment : instantiation for local alignment." Thesis, 2003. http://spectrum.library.concordia.ca/2412/1/MQ91147.pdf.

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Although there are already several C implementations of pairwise sequence alignment in the EMBOSS library for bioinformatics, all of them are quite independent of each other. The main purpose of this project is to develop a generic application to unify the different implementations and to provide the developer with the capability to develop a pairwise alignment algorithm with little effort. C++ template technology provides high levels of performance and reusability project to achieve generic algorithm. An alignment algorithm is defined as a function object, which will be passed as a parameter to a generic implementation of dynamic programming. In this report, the local alignment algorithm of Smith-Waterman is instantiated. This application provides two kinds of reusability: generic algorithm reusability and function objects reusability.
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Book chapters on the topic "Pairwise sequence alignment"

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Sayood, Khalid, and Hasan H. Otu. "Pairwise Sequence Alignment." In Synthesis Lectures on Biomedical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20017-5_4.

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Zimmermann, Karl-Heinz. "Pairwise Sequence Alignment." In The Kluwer International Series in Engineering and Computer Science. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9210-9_3.

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Ismail, Hamid D. "Pairwise Sequence Alignment." In Bioinformatics. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003226611-7.

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Araujo, Eloi, Fábio V. Martinez, Luiz C. Rozante, and Nalvo F. Almeida. "Extended Pairwise Sequence Alignment." In Computational Science and Its Applications – ICCSA 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36805-9_15.

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Kumar, T. Durai Ananda. "Pairwise Sequence Alignment (PSA)." In Drug Design: A Conceptual Overview. CRC Press, 2022. http://dx.doi.org/10.1201/9781003298755-10.

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Feng, Jin-an. "Improving Pairwise Sequence Alignment between Distantly Related Proteins." In Comparative Genomics. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-514-5_16.

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Horton, Paul, and Martin Frith. "A Tiling Bound for Pairwise Global Sequence Alignment." In Advances in Software Engineering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10242-4_8.

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Issa, Mohamed, Aboul Ella Hassanien, Ahmed Helmi, Ibrahim Ziedan, and Ahmed Alzohairy. "Pairwise Global Sequence Alignment Using Sine-Cosine Optimization Algorithm." In The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74690-6_11.

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Sharif, Mahir M., Alaa Tharwat, Aboul Ella Hassanien, and Hesham A. Hefeny. "Automated Enzyme Function Classification Based on Pairwise Sequence Alignment Technique." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21206-7_43.

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Agarwal, Pankaj K., Yonatan Bilu, and Rachel Kolodny. "Faster Algorithms for Optimal Multiple Sequence Alignment Based on Pairwise Comparisons." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11557067_26.

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Conference papers on the topic "Pairwise sequence alignment"

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Haque, Waqar, Alex Aravind, and Bharath Reddy. "Pairwise sequence alignment algorithms." In the 2009 conference. ACM Press, 2009. http://dx.doi.org/10.1145/1551950.1551980.

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Zivanic, M., O. Daescu, and Anastasia Kurdia. "Rigid region pairwise sequence alignment." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112393.

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Junjie Li, Sanjay Ranka, and Sartaj Sahni. "Pairwise sequence alignment for very long sequences on GPUs." In 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2012. http://dx.doi.org/10.1109/iccabs.2012.6182641.

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DineshDarsi, Rajesh S, P. J. S. Krishna, and Sushma. "Pairwise Sequence Alignment in Biological Sequences using Machine Learning." In 2023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC). IEEE, 2023. http://dx.doi.org/10.1109/icacic59454.2023.10435046.

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Salinas, Sergio, and Pan Li. "Secure Cloud Computing for Pairwise Sequence Alignment." In BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM, 2017. http://dx.doi.org/10.1145/3107411.3107477.

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Carroll, Thomas C., Jude-Thaddeus Ojiaku, and Prudence W. H. Wong. "Pairwise Sequence Alignment with Gaps with GPU." In 2015 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2015. http://dx.doi.org/10.1109/cluster.2015.109.

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Jiang, Xiantao, Xueliang Fu, Gaifang Dong, and Honghui Li. "Research on Pairwise Sequence Alignment Needleman-Wunsch Algorithm." In 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/icmmcce-17.2017.187.

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Kaur, Yadvir, and Neelofar Sohi. "Pairwise sequence alignment method using flower pollination algorithm." In 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2017. http://dx.doi.org/10.1109/ispcc.2017.8269713.

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Clote, Peter, Feng Lou, and Alain Denise. "A New Approach to Suboptimal Pairwise Sequence Alignment." In Intelligent Systems and Control. ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.742-039.

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Montanola, A., C. Roig, and P. Hernandez. "Pairwise Sequence Alignment Method for Distributed Shared Memory Systems." In 2013 21st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2013). IEEE, 2013. http://dx.doi.org/10.1109/pdp.2013.69.

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