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Journal articles on the topic 'Sequence alignment'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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

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Pervez, Muhammad Tariq, Hayat Ali Shah, Masroor Ellahi Babar, Nasir Naveed, and Muhammad Shoaib. "SAliBASE: A Database of Simulated Protein Alignments." Evolutionary Bioinformatics 15 (January 2019): 117693431882108. http://dx.doi.org/10.1177/1176934318821080.

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Simulated alignments are alternatives to manually constructed multiple sequence alignments for evaluating performance of multiple sequence alignment tools. The importance of simulated sequences is recognized because their true evolutionary history is known, which is very helpful for reconstructing accurate phylogenetic trees and alignments. However, generating simulated alignments require expertise to use bioinformatics tools and consume several hours for reconstructing even a few hundreds of simulated sequences. It becomes a tedious job for an end user who needs a few datasets of variety of s

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Martin, Andrew C. R. "Viewing multiple sequence alignments with the JavaScript Sequence Alignment Viewer (JSAV)." F1000Research 3 (October 23, 2014): 249. http://dx.doi.org/10.12688/f1000research.5486.1.

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The JavaScript Sequence Alignment Viewer (JSAV) is designed as a simple-to-use JavaScript component for displaying sequence alignments on web pages. The display of sequences is highly configurable with options to allow alternative coloring schemes, sorting of sequences and ’dotifying’ repeated amino acids. An option is also available to submit selected sequences to another web site, or to other JavaScript code. JSAV is implemented purely in JavaScript making use of the JQuery and JQuery-UI libraries. It does not use any HTML5-specific options to help with browser compatibility. The code is doc

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Arenas-Díaz, Edgar D., Helga Ochoterena, and Katya Rodríguez-Vázquez. "Multiple Sequence Alignment Using a Genetic Algorithm and GLOCSA." Journal of Artificial Evolution and Applications 2009 (August 27, 2009): 1–10. http://dx.doi.org/10.1155/2009/963150.

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Algorithms that minimize putative synapomorphy in an alignment cannot be directly implemented since trivial cases with concatenated sequences would be selected because they would imply a minimum number of events to be explained (e.g., a single insertion/deletion would be required to explain divergence among two sequences). Therefore, indirect measures to approach parsimony need to be implemented. In this paper, we thoroughly present a Global Criterion for Sequence Alignment (GLOCSA) that uses a scoring function to globally rate multiple alignments aiming to produce matrices that minimize the n

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Aadland, Kelsey, and Bryan Kolaczkowski. "Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy." Genome Biology and Evolution 12, no. 9 (2020): 1549–65. http://dx.doi.org/10.1093/gbe/evaa164.

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Abstract Ancestral sequence reconstruction (ASR) uses an alignment of extant protein sequences, a phylogeny describing the history of the protein family and a model of the molecular-evolutionary process to infer the sequences of ancient proteins, allowing researchers to directly investigate the impact of sequence evolution on protein structure and function. Like all statistical inferences, ASR can be sensitive to violations of its underlying assumptions. Previous studies have shown that, whereas phylogenetic uncertainty has only a very weak impact on ASR accuracy, uncertainty in the protein se

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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 m

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Blackshields, Gordon, Iain M. Wallace, Mark Larkin, and Desmond G. Higgins. "Analysis and Comparison of Benchmarks for Multiple Sequence Alignment." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 6, no. 4 (2006): 321–39. https://doi.org/10.3233/isb-00245.

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The most popular way of comparing the performance of multiple sequence alignment programs is to use empirical testing on sets of test sequences. Several such test sets now exist, each with potential strengths and weaknesses. We apply several different alignment packages to 6 benchmark datasets, and compare their relative performances. HOMSTRAD, a collection of alignments of homologous proteins, is regularly used as a benchmark for sequence alignment though it is not designed as such, and lacks annotation of reliable regions within the alignment. We introduce this annotation into HOMSTRAD using

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FÜRER, MARTIN, and WEBB MILLER. "ALIGNMENT-TO-ALIGNMENT EDITING WITH “MOVE GAP” OPERATIONS." International Journal of Foundations of Computer Science 07, no. 01 (1996): 23–41. http://dx.doi.org/10.1142/s012905419600004x.

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An alignment of k given sequences is a k-rowed matrix frequently used by molecular biologists to display correspondences between entries from each sequence. Under one approach, an alignment is represented by a matrix of ‘x’ and ’-’ characters, where each x in row r indicates the position of an entry of sequence r. It is sometimes efficient to store only the run-length encoding of each row of this bit-matrix. A natural class of commands for editing one such row into another consists of operations of the form: “Move the d dashes that begin at position i of row r to position j of that row,” for r

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9

WANG, YI, and KUO-BIN LI. "MULTIPLE SEQUENCE ALIGNMENT USING AN EXHAUSTIVE AND GREEDY ALGORITHM." Journal of Bioinformatics and Computational Biology 03, no. 02 (2005): 243–55. http://dx.doi.org/10.1142/s021972000500103x.

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We describe an exhaustive and greedy algorithm for improving the accuracy of multiple sequence alignment. A simple progressive alignment approach is employed to provide initial alignments. The initial alignment is then iteratively optimized against an objective function. For any working alignment, the optimization involves three operations: insertions, deletions and shuffles of gaps. The optimization is exhaustive since the algorithm applies the above operations to all eligible positions of an alignment. It is also greedy since only the operation that gives the best improving objective score w

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Tu, Shin-Lin, Jeannette Staheli, Colum McClay, Kathleen McLeod, Timothy Rose, and Chris Upton. "Base-By-Base Version 3: New Comparative Tools for Large Virus Genomes." Viruses 10, no. 11 (2018): 637. http://dx.doi.org/10.3390/v10110637.

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Base-By-Base is a comprehensive tool for the creation and editing of multiple sequence alignments that is coded in Java and runs on multiple platforms. It can be used with gene and protein sequences as well as with large viral genomes, which themselves can contain gene annotations. This report describes new features added to Base-By-Base over the last 7 years. The two most significant additions are: (1) The recoding and inclusion of “consensus-degenerate hybrid oligonucleotide primers” (CODEHOP), a popular tool for the design of degenerate primers from a multiple sequence alignment of proteins

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Piña, Johan S., Simon Orozco-Arias, Nicolas Tobón-Orozco, Leonardo Camargo-Forero, Reinel Tabares-Soto, and Romain Guyot. "G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era." Evolutionary Bioinformatics 19 (January 2023): 117693432211505. http://dx.doi.org/10.1177/11769343221150585.

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A common task in bioinformatics is to compare DNA sequences to identify similarities between organisms at the sequence level. An approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze DNA or protein alignments. Dot-plots alignment software existed before the sequencing revolution, and now there is an ongoing limitation when dealing with large-size sequences, resulting in very long execution times. High-Performance Computing (HPC) techniques have been successfully used in many applications to reduce computing times, but so far, very few applications fo

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Ji, Guoli, Yong Zeng, Zijiang Yang, Congting Ye, and Jingci Yao. "A multiple sequence alignment method with sequence vectorization." Engineering Computations 31, no. 2 (2014): 283–96. http://dx.doi.org/10.1108/ec-01-2013-0026.

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Purpose – The time complexity of most multiple sequence alignment algorithm is O(N2) or O(N3) (N is the number of sequences). In addition, with the development of biotechnology, the amount of biological sequences grows significantly. The traditional methods have some difficulties in handling large-scale sequence. The proposed Lemk_MSA method aims to reduce the time complexity, especially for large-scale sequences. At the same time, it can keep similar accuracy level compared to the traditional methods. Design/methodology/approach – LemK_MSA converts multiple sequence alignment into correspondi

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Keith, Jonathan M., Peter Adams, Darryn Bryant, Keith R. Mitchelson, Duncan A. E. Cochran, and Gita H. Lala. "Inferring an Original Sequence from Erroneous Copies: Two Approaches." Asia-Pacific Biotech News 07, no. 03 (2003): 107–14. http://dx.doi.org/10.1142/s0219030303000284.

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This paper considers the problem of inferring an original sequence from a number of erroneous copies. The problem arises in DNA sequencing, particularly in the context of emerging technologies that provide high throughput or other advantages at the cost of an increased number of errors. We describe and compare two approaches that have recently been developed by the authors. The first approach searches for a sequence known as a Steiner string; the second searches for the most probable original sequence with respect to a simple Bayesian model of sequencing errors. We present the results of exten

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Prerna, Prerna, Pankaj Bhambri, and Dr O. P. Gupta Dr. O.P. Gupta. "Multiple Sequence Alignment of Different Species." Indian Journal of Applied Research 1, no. 7 (2011): 78–82. http://dx.doi.org/10.15373/2249555x/apr2012/24.

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Lu, Yue, and Sing-Hoi Sze. "Multiple Sequence Alignment Based on Profile Alignment of Intermediate Sequences." Journal of Computational Biology 15, no. 7 (2008): 767–77. http://dx.doi.org/10.1089/cmb.2007.0132.

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Ji, Guo Li, Jing Ci Yao, Zi Jiang Yang, and Cong Ting Ye. "LemK_MSA: A Multiple Sequence Alignment Method with Sequence Vectorization Based on Lempel-Ziv." Applied Mechanics and Materials 284-287 (January 2013): 3203–7. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3203.

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In this paper, we propose a method for multiple sequence alignment, LemK_MSA, which integrates Lempel-Ziv based sequence vectorization and k-means clustering analysis. LemK_MSA converts multiple sequence alignment into corresponding 10-dimensional vector alignment by 10 types of copy modes. Then it uses k-means algorithm and NJ algorithm to divide the sequences into several groups and calculate guide tree of each part with the vectors of the sequences. A complete guide tree for multiple sequence alignment could be constructed by merging guide tree of every group. Thus, the time efficiency of p

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Kanagarajadurai, Karuppiah, Singaravelu Kalaimathy, Paramasivam Nagarajan, and Ramanathan Sowdhamini. "PASS2." International Journal of Knowledge Discovery in Bioinformatics 2, no. 4 (2011): 53–66. http://dx.doi.org/10.4018/jkdb.2011100104.

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A detailed comparison of protein domains that belong to families and superfamilies shows that structure is better conserved than sequence during evolutionary divergence. Sequence alignments, guided by structural features, permit a better sampling of the protein sequence space and effective construction of libraries for fold recognition. Sequence alignments are useful evolutionary models in defining structure-function relationships for protein superfamilies. The PASS2 database, maintained by the authors, presents alignments of proteins related at the superfamily level and characterised by low s

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Cavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 21, 2015): 1097. http://dx.doi.org/10.12688/f1000research.6348.1.

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known

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Cavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 15, 2020): 1097. http://dx.doi.org/10.12688/f1000research.6348.2.

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known

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Ji, Mingeun, Yejin Kan, Dongyeon Kim, Jaehee Jung, and Gangman Yi. "cPlot: Contig-Plotting Visualization for the Analysis of Short-Read Nucleotide Sequence Alignments." International Journal of Molecular Sciences 23, no. 19 (2022): 11484. http://dx.doi.org/10.3390/ijms231911484.

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Advances in the next-generation sequencing technology have led to a dramatic decrease in read-generation cost and an increase in read output. Reconstruction of short DNA sequence reads generated by next-generation sequencing requires a read alignment method that reconstructs a reference genome. In addition, it is essential to analyze the results of read alignments for a biologically meaningful inference. However, read alignment from vast amounts of genomic data from various organisms is challenging in that it involves repeated automatic and manual analysis steps. We, here, devised cPlot softwa

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Shu, Jian-Jun, Kian Yan Yong, and Weng Kong Chan. "An Improved Scoring Matrix for Multiple Sequence Alignment." Mathematical Problems in Engineering 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/490649.

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The way for performing multiple sequence alignment is based on the criterion of the maximum-scored information content computed from a weight matrix, but it is possible to have two or more alignments to have the same highest score leading to ambiguities in selecting the best alignment. This paper addresses this issue by introducing the concept of joint weight matrix to eliminate the randomness in selecting the best multiple sequence alignment. Alignments with equal scores are iteratively rescored with the joint weight matrix of increasing level (nucleotide pairs, triplets, and so on) until one

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Linheiro, Raquel, Stephen Sabatino, Diana Lobo, and John Archer. "CView: A network based tool for enhanced alignment visualization." PLOS ONE 17, no. 6 (2022): e0259726. http://dx.doi.org/10.1371/journal.pone.0259726.

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To date basic visualization of sequence alignments have largely focused on displaying per-site columns of nucleotide, or amino acid, residues along with associated frequency summarizations. The persistence of this tendency to the recent tools designed for viewing mapped read data indicates that such a perspective not only provides a reliable visualization of per-site alterations, but also offers implicit reassurance to the end-user in relation to data accessibility. However, the initial insight gained is limited, something that is especially true when viewing alignments consisting of many sequ

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Jiang, Yatong, Tao Shang, and Jianwei Liu. "Privacy-preserving Multiple Sequence Alignment Scheme for Long Gene Sequence." Proceedings on Privacy Enhancing Technologies 2025, no. 1 (2025): 236–49. http://dx.doi.org/10.56553/popets-2025-0014.

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Gene Multiple Sequence Alignment is crucial for genomic data analysis, forming the basis for studying its biological significance. The digitization of genomic data allows collaborative analysis on cloud platforms, improving the efficiency and precision of genomic research. However, gene sequences contain sensitive information, posing a risk of privacy leakage with unauthorized access. Balancing privacy, accuracy, and efficiency in multiple sequence alignment for long gene sequences remains a challenge. In this paper, we propose a distributed privacy-preserving multiple sequence alignment schem

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Barton, Geoffrey J. "Protein Sequence Alignment Techniques." Acta Crystallographica Section D Biological Crystallography 54, no. 6 (1998): 1139–46. http://dx.doi.org/10.1107/s0907444998008324.

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The basic algorithms for alignment of two or more protein sequences are explained. Alternative methods for scoring substitutions and gaps (insertions and deletions) are described, as are global and local alignment methods. Multiple alignment techniques are explained, including methods for profile comparison. A summary is given of programs for the alignment and analysis of protein sequences, either from sequence alone, or from three-dimensional structure.

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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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Padua, F. L. C., R. L. Carceroni, G. A. M. R. Santos, and K. N. Kutulakos. "Linear Sequence-to-Sequence Alignment." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 2 (2010): 304–20. http://dx.doi.org/10.1109/tpami.2008.301.

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Ji, Guo Li, Long Teng Chen, and Liang Liang Chen. "Two-Level Parallel Alignment Based on Sequence Parallel Vectorization." Applied Mechanics and Materials 490-491 (January 2014): 757–62. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.757.

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This paper proposed a way of two-level parallel alignment based on sequence parallel vectorization with GPU acceleration on the Fermi architecture, which integrates sequence parallel vectorization, parallel k-means clustering approximate alignment and parallel Smith-Waterman algorithm. The method converts sequence alignment into vector alignment by first. Then it uses k-means alignment to divide sequences into several groups and reduce the size of sequence data. The expected accurate alignment result is achieved using parallel Smith-Waterman algorithm. The high-throughput mouse T-cell receptor

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Alser, Mohammed, Hasan Hassan, Akash Kumar, Onur Mutlu, and Can Alkan. "Shouji: a fast and efficient pre-alignment filter for sequence alignment." Bioinformatics 35, no. 21 (2019): 4255–63. http://dx.doi.org/10.1093/bioinformatics/btz234.

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AbstractMotivationThe ability to generate massive amounts of sequencing data continues to overwhelm the processing capability of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the execution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We introduce Shouji, a highly parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment

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Ahola, Virpi, Tero Aittokallio, Esa Uusipaikka, and Mauno Vihinen. "Statistical Methods for Identifying Conserved Residues in Multiple Sequence Alignment." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (2004): 1–28. http://dx.doi.org/10.2202/1544-6115.1074.

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The assessment of residue conservation in a multiple sequence alignment is a central issue in bioinformatics. Conserved residues and regions are used to determine structural and functional motifs or evolutionary relationships between the sequences of a multiple sequence alignment. For this reason, residue conservation is a valuable measure for database and motif search or for estimating the quality of alignments. In this paper, we present statistical methods for identifying conserved residues in multiple sequence alignments. While most earlier studies examine the positional conservation of the

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Zhan, Qing, Yilei Fu, Qinghua Jiang, Bo Liu, Jiajie Peng, and Yadong Wang. "SpliVert: A Protein Multiple Sequence Alignment Refinement Method Based on Splitting-Splicing Vertically." Protein & Peptide Letters 27, no. 4 (2020): 295–302. http://dx.doi.org/10.2174/0929866526666190806143959.

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Background: Multiple Sequence Alignment (MSA) is a fundamental task in bioinformatics and is required for many biological analysis tasks. The more accurate the alignments are, the more credible the downstream analyses. Most protein MSA algorithms realign an alignment to refine it by dividing it into two groups horizontally and then realign the two groups. However, this strategy does not consider that different regions of the sequences have different conservation; this property may lead to incorrect residue-residue or residue-gap pairs, which cannot be corrected by this strategy. Objective: In

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Schulz, Tizian, Roland Wittler, Sven Rahmann, Faraz Hach, and Jens Stoye. "Detecting high-scoring local alignments in pangenome graphs." Bioinformatics 37, no. 16 (2021): 2266–74. http://dx.doi.org/10.1093/bioinformatics/btab077.

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Abstract Motivation Increasing amounts of individual genomes sequenced per species motivate the usage of pangenomic approaches. Pangenomes may be represented as graphical structures, e.g. compacted colored de Bruijn graphs, which offer a low memory usage and facilitate reference-free sequence comparisons. While sequence-to-graph mapping to graphical pangenomes has been studied for some time, no local alignment search tool in the vein of BLAST has been proposed yet. Results We present a new heuristic method to find maximum scoring local alignments of a DNA query sequence to a pangenome represen

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Askari Rad, Mahbubeh, Alibek Kruglikov, and Xuhua Xia. "Three-Way Alignment Improves Multiple Sequence Alignment of Highly Diverged Sequences." Algorithms 17, no. 5 (2024): 205. http://dx.doi.org/10.3390/a17050205.

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The standard approach for constructing a phylogenetic tree from a set of sequences consists of two key stages. First, a multiple sequence alignment (MSA) of the sequences is computed. The aligned data are then used to reconstruct the phylogenetic tree. The accuracy of the resulting tree heavily relies on the quality of the MSA. The quality of the popularly used progressive sequence alignment depends on a guide tree, which determines the order of aligning sequences. Most MSA methods use pairwise comparisons to generate a distance matrix and reconstruct the guide tree. However, when dealing with

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Ji, Yukai, Tao Huang, Chunlai Ma, Chao Hu, Zhanfeng Wang, and Anmin Fu. "IMCSA: Providing Better Sequence Alignment Space for Industrial Control Protocol Reverse Engineering." Security and Communication Networks 2022 (November 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/8026280.

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Nowadays, with the wide application of industrial control facilities, industrial control protocol reverse engineering has significant security implications. The reverse method of industrial protocol based on sequence alignment is the current mainstream method because of its high accuracy. However, this method will incur a huge time overhead due to unnecessary alignments during the sequence alignment process. In this paper, we optimize the traditional sequence alignment method by combining the characteristics of industrial control protocols. We improve the frequent sequence mining algorithm, Ap

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Tang, Chuan Yi, Chin Lung Lu, Margaret Dah-Tsyr Chang, et al. "Constrained Multiple Sequence Alignment Tool Development and Its Application to RNase Family Alignment." Journal of Bioinformatics and Computational Biology 01, no. 02 (2003): 267–87. http://dx.doi.org/10.1142/s0219720003000095.

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In this paper, we design a heuristic algorithm of computing a constrained multiple sequence alignment (CMSA for short) for guaranteeing that the generated alignment satisfies the user-specified constraints that some particular residues should be aligned together. If the number of residues needed to be aligned together is a constant α, then the time-complexity of our CMSA algorithm for aligning K sequences is O(αKn4), where n is the maximum of the lengths of sequences. In addition, we have built up such a CMSA software system and made several experiments on the RNase sequences, which mainly fun

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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 a

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Catanach, Therese A., Andrew D. Sweet, Nam-phuong D. Nguyen, et al. "Fully automated sequence alignment methods are comparable to, and much faster than, traditional methods in large data sets: an example with hepatitis B virus." PeerJ 7 (January 3, 2019): e6142. http://dx.doi.org/10.7717/peerj.6142.

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Aligning sequences for phylogenetic analysis (multiple sequence alignment; MSA) is an important, but increasingly computationally expensive step with the recent surge in DNA sequence data. Much of this sequence data is publicly available, but can be extremely fragmentary (i.e., a combination of full genomes and genomic fragments), which can compound the computational issues related to MSA. Traditionally, alignments are produced with automated algorithms and then checked and/or corrected “by eye” prior to phylogenetic inference. However, this manual curation is inefficient at the data scales re

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Zhai, Yixiao, Jiannan Chao, Yizheng Wang, Pinglu Zhang, Furong Tang, and Quan Zou. "TPMA: A two pointers meta-alignment tool to ensemble different multiple nucleic acid sequence alignments." PLOS Computational Biology 20, no. 4 (2024): e1011988. http://dx.doi.org/10.1371/journal.pcbi.1011988.

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Accurate multiple sequence alignment (MSA) is imperative for the comprehensive analysis of biological sequences. However, a notable challenge arises as no single MSA tool consistently outperforms its counterparts across diverse datasets. Users often have to try multiple MSA tools to achieve optimal alignment results, which can be time-consuming and memory-intensive. While the overall accuracy of certain MSA results may be lower, there could be local regions with the highest alignment scores, prompting researchers to seek a tool capable of merging these locally optimal results from multiple ini

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Edgar, Robert C., and Serafim Batzoglou. "Multiple sequence alignment." Current Opinion in Structural Biology 16, no. 3 (2006): 368–73. http://dx.doi.org/10.1016/j.sbi.2006.04.004.

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Chao, Kun-Mao, Ross C. Hardison, and Webb Miller. "Constrained sequence alignment." Bulletin of Mathematical Biology 55, no. 3 (1993): 503–24. http://dx.doi.org/10.1007/bf02460648.

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CHAO, K., R. HARDISON, and W. MILLER. "Constrained sequence alignment." Bulletin of Mathematical Biology 55, no. 3 (1993): 503–24. http://dx.doi.org/10.1016/s0092-8240(05)80237-x.

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Bacon, David J., and Wayne F. Anderson. "Multiple sequence alignment." Journal of Molecular Biology 191, no. 2 (1986): 153–61. http://dx.doi.org/10.1016/0022-2836(86)90252-4.

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Munshi, Hassan, Mark Liberman, and Jianjing Kuang. "Textsetting as sequence alignment." Journal of the Acoustical Society of America 152, no. 4 (2022): A179. http://dx.doi.org/10.1121/10.0015960.

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We argue that textsetting is better accounted for under the framework of sequence comparison, whereby textsetting is taken to be an instance of sequence alignment. Given a text, a tune, and an alignment procedure, we argue that all one needs is to align strong elements of one sequence (the text) to elements of the other (the tune). We show that, in many cases, the only principle needed to account for textsetting in English folk music is Strength Match, i.e., matching stressed syllables to strong beats. Many constraints proposed by previous treatments are to be discarded if we allow our textset

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Tseng, Yu-Hsiang, Sumit Walia, and Yatish Turakhia. "Ultrafast and ultralarge multiple sequence alignments using TWILIGHT." Bioinformatics 41, Supplement_1 (2025): i332—i341. https://doi.org/10.1093/bioinformatics/btaf212.

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Abstract Motivation Multiple sequence alignment (MSA) is a fundamental operation in bioinformatics, yet existing MSA tools are struggling to keep up with the speed and volume of incoming data. This is because the runtimes and memory requirements of current MSA tools become untenable when processing large numbers of long input sequences, and they also fail to fully harness the parallelism provided by modern CPUs and GPUs. Results We present Tall and Wide Alignments at High Throughput (TWILIGHT), a novel MSA tool optimized for speed, accuracy, scalability, and memory constraints, with both CPU a

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de Oliveira Martins, Leonardo, Alison E. Mather, and Andrew J. Page. "Scalable neighbour search and alignment with uvaia." PeerJ 12 (March 6, 2024): e16890. http://dx.doi.org/10.7717/peerj.16890.

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Despite millions of SARS-CoV-2 genomes being sequenced and shared globally, manipulating such data sets is still challenging, especially selecting sequences for focused phylogenetic analysis. We present a novel method, uvaia, which is based on partial and exact sequence similarity for quickly extracting database sequences similar to query sequences of interest. Many SARS-CoV-2 phylogenetic analyses rely on very low numbers of ambiguous sites as a measure of quality since ambiguous sites do not contribute to single nucleotide polymorphism (SNP) differences. Uvaia overcomes this limitation by us

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Md Isa, Mohd Nazrin, Sohiful Anuar Zainol Murad, Mohamad Imran Ahmad, Muhammad M. Ramli, and Rizalafande Che Ismail. "An Efficient Scheduling Technique for Biological Sequence Alignment." Applied Mechanics and Materials 754-755 (April 2015): 1087–92. http://dx.doi.org/10.4028/www.scientific.net/amm.754-755.1087.

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Computing alignment matrix score to search for regions of homology between biological sequences is time consuming task. This is due to the recursive nature of the dynamic programming-based algorithms such as the Smith-Waterman and the Needleman-Wunsch algorithmns. Typical FPGA-based protein sequencer comprises of two main logic blocks. One for computing alignment scores i.e. the processing element (PE), while another logic block for configuring the PE with coefficients. During alignment matrix computation, the logic block for configuring the PE are left unused until the time consuming alignmen

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Kholiq, Hibban, Mamika Ujianita Romdhini, and Marliadi Susanto. "Algoritma Needleman-Wunsch dalam Menentukan Tingkat Kemiripan Urutan DNA Rusa Timor (Cervus timorensis) dan Rusa Merah (Cervus elaphus)." EIGEN MATHEMATICS JOURNAL 3, no. 2 (2020): 125. http://dx.doi.org/10.29303/emj.v3i2.65.

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Sequence alignment is a basic method in sequence analysis. This method is used to determine the similaritiy level of DNA sequences. The Needleman-Wunsch algorithm is an algorithm that can be used to solve the problem of sequence alignment. This research shows that the relation T (i, j) used in the Needleman-Wunsch algorithm is a function where T: (ℕ0 ℕ0) → ℤ. The function T (i, j) is a recursive function. Moreover, DNA sequence data used are DNA sequences from the Timor Deer, which are the identities of the provinces of West Nusa Tenggara and Red Deer, which are typical deer from the European

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Wilson, W. C. "Activity Pattern Analysis by Means of Sequence-Alignment Methods." Environment and Planning A: Economy and Space 30, no. 6 (1998): 1017–38. http://dx.doi.org/10.1068/a301017.

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The author describes a method of comparing sequences of characters, called sequence alignment or string matching, and illustrates its use in the analysis of daily activity patterns derived from time-use diaries. It allows definition of measures of similarity or distance between complete sequences, called global alignment, or the evaluation of the best fit of short sequences within long sequences, called local alignment. Alignments may be done pairwise to develop similarity or distance matrices that describe the relatedness of individuals in the set of sequences being examined. Pairwise alignme

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Benítez-Hidalgo, Antonio, José F. Aldana-Montes, Ismael Navas-Delgado, and María del Mar Roldán-García. "SALON ontology for the formal description of sequence alignments." BMC Bioinformatics 24, no. 1 (2023). http://dx.doi.org/10.1186/s12859-023-05190-7.

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Abstract Background Information provided by high-throughput sequencing platforms allows the collection of content-rich data about biological sequences and their context. Sequence alignment is a bioinformatics approach to identifying regions of similarity in DNA, RNA, or protein sequences. However, there is no consensus about the specific common terminology and representation for sequence alignments. Thus, automatically linking the wide existing knowledge about the sequences with the alignments is challenging. Results The Sequence Alignment Ontology (SALON) defines a helpful vocabulary for repr

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Frith, Martin C. "A simple method for finding related sequences by adding probabilities of alternative alignments." Genome Research, August 16, 2024, gr.279464.124. http://dx.doi.org/10.1101/gr.279464.124.

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The main way of analyzing genetic sequences is by finding sequence regions that are related to each other. There are many methods to do that, usually based on this idea: find an alignment of two sequence regions, which would be unlikely to exist between unrelated sequences. Unfortunately, it is hard to tell if an alignment is likely to exist by chance. Also, the precise alignment of related regions is uncertain. One alignment does not hold all evidence that they are related. We should consider alternative alignments too. This is rarely done, because we lack a simple and fast method that fits e

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Anonymous. "Biological sequence alignments from language representations." February 6, 2020. https://doi.org/10.5281/zenodo.3665778.

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Computing sequence similarity is a fundamental task in biology, with alignment forming the basis for the annotation of genes and genomes and providing the core data structures for all evolutionary analysis. Standard approaches rely on variations of edit distance to obtain explicit alignments between pairs of biological sequences.  However, these approaches fail to account for long-range interactions across sequences, and cannot account for evolutionary events that permute sequence modules; taking such events into account is critical for comparing protein sequences that have diverged acros

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