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Journal articles on the topic 'Multiple alignment'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Wheeler, Travis J., and John D. Kececioglu. "Multiple alignment by aligning alignments." Bioinformatics 23, no. 13 (July 1, 2007): i559—i568. http://dx.doi.org/10.1093/bioinformatics/btm226.

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2

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 (October 1, 2011): 78–82. http://dx.doi.org/10.15373/2249555x/apr2012/24.

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3

WANG, YI, and KUO-BIN LI. "MULTIPLE SEQUENCE ALIGNMENT USING AN EXHAUSTIVE AND GREEDY ALGORITHM." Journal of Bioinformatics and Computational Biology 03, no. 02 (April 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 will be accepted. The algorithms have been implemented in the EGMA (Exhaustive and Greedy Multiple Alignment) package using Java programming language, and have been evaluated using the BAliBASE benchmark alignment database. Although EGMA is not guaranteed to produce globally optimized alignment, the tests indicate that EGMA is able to build alignments with high quality consistently, compared with other commonly used iterative and non-iterative alignment programs. It is also useful for refining multiple alignments obtained by other methods.
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4

WANG, ZHUOZHI, and KAIZHONG ZHANG. "MULTIPLE RNA STRUCTURE ALIGNMENT." Journal of Bioinformatics and Computational Biology 03, no. 03 (June 2005): 609–26. http://dx.doi.org/10.1142/s0219720005001296.

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Ribonucleic Acid (RNA) structures can be viewed as a special kind of strings where characters in a string can bond with each other. The question of aligning two RNA structures has been studied for a while, and there are several successful algorithms that are based upon different models. In this paper, by adopting the model introduced in Wang and Zhang,19 we propose two algorithms to attack the question of aligning multiple RNA structures. Our methods are to reduce the multiple RNA structure alignment problem to the problem of aligning two RNA structure alignments. Meanwhile, we will show that the framework of sequence center star alignment algorithm can be applied to the problem of multiple RNA structure alignment, and if the triangle inequality is met in the scoring matrix, the approximation ratio of the algorithm remains to be [Formula: see text], where n is the total number of structures.
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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 single best alignment is eventually found. This method for resolving ambiguity in multiple sequence alignment can be easily implemented by use of the improved scoring matrix.
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6

Bucka-Lassen, K., O. Caprani, and J. Hein. "Combining many multiple alignments in one improved alignment." Bioinformatics 15, no. 2 (February 1, 1999): 122–30. http://dx.doi.org/10.1093/bioinformatics/15.2.122.

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7

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

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8

Kleinjung, J., N. Douglas, and J. Heringa. "Parallelized multiple alignment." Bioinformatics 18, no. 9 (September 1, 2002): 1270–71. http://dx.doi.org/10.1093/bioinformatics/18.9.1270.

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9

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

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10

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 number of putative synapomorphies. We also present a Genetic Algorithm that uses GLOCSA as the objective function to produce sequence alignments refining alignments previously generated by additional existing alignment tools (we recommend MUSCLE). We show that in the example cases our GLOCSA-guided Genetic Algorithm (GGGA) does improve the GLOCSA values, resulting in alignments that imply less putative synapomorphies.
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11

Shatsky, Maxim, Ruth Nussinov, and Haim J. Wolfson. "Optimization of multiple-sequence alignment based on multiple-structure alignment." Proteins: Structure, Function, and Bioinformatics 62, no. 1 (November 17, 2005): 209–17. http://dx.doi.org/10.1002/prot.20665.

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12

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 (March 17, 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 this article, our motivation is to develop a novel refinement method based on splitting- splicing vertically. Method: Here, we present a novel refinement method based on splitting-splicing vertically, called SpliVert. For an alignment, we split it vertically into 3 parts, remove the gap characters in the middle, realign the middle part alone, and splice the realigned middle parts with the other two initial pieces to obtain a refined alignment. In the realign procedure of our method, the aligner will only focus on a certain part, ignoring the disturbance of the other parts, which could help fix the incorrect pairs. Results: We tested our refinement strategy for 2 leading MSA tools on 3 standard benchmarks, according to the commonly used average SP (and TC) score. The results show that given appropriate proportions to split the initial alignment, the average scores are increased comparably or slightly after using our method. We also compared the alignments refined by our method with alignments directly refined by the original alignment tools. The results suggest that using our SpliVert method to refine alignments can also outperform direct use of the original alignment tools. Conclusion: The results reveal that splitting vertically and realigning part of the alignment is a good strategy for the refinement of protein multiple sequence alignments.
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13

Flannick, J. "Using multiple alignments to improve seeded local alignment algorithms." Nucleic Acids Research 33, no. 14 (August 2, 2005): 4563–77. http://dx.doi.org/10.1093/nar/gki767.

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14

Steenwyk, Jacob L., Thomas J. Buida, Yuanning Li, Xing-Xing Shen, and Antonis Rokas. "ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference." PLOS Biology 18, no. 12 (December 2, 2020): e3001007. http://dx.doi.org/10.1371/journal.pbio.3001007.

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Highly divergent sites in multiple sequence alignments (MSAs), which can stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Thus, several different trimming strategies have been developed for identifying and removing these sites prior to phylogenetic inference. However, a recent study reported that doing so can worsen inference, underscoring the need for alternative alignment trimming strategies. Here, we introduce ClipKIT, an alignment trimming software that, rather than identifying and removing putatively phylogenetically uninformative sites, instead aims to identify and retain parsimony-informative sites, which are known to be phylogenetically informative. To test the efficacy of ClipKIT, we examined the accuracy and support of phylogenies inferred from 14 different alignment trimming strategies, including those implemented in ClipKIT, across nearly 140,000 alignments from a broad sampling of evolutionary histories. Phylogenies inferred from ClipKIT-trimmed alignments are accurate, robust, and time saving. Furthermore, ClipKIT consistently outperformed other trimming methods across diverse datasets, suggesting that strategies based on identifying and retaining parsimony-informative sites provide a robust framework for alignment trimming.
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15

PATEL, VANDANABEN, JASON T. L. WANG, SHEFALI SETIA, ANURAG VERMA, CHARLES D. WARDEN, and KAIZHONG ZHANG. "ON COMPARING TWO STRUCTURED RNA MULTIPLE ALIGNMENTS." Journal of Bioinformatics and Computational Biology 08, no. 06 (December 2010): 967–80. http://dx.doi.org/10.1142/s021972001000504x.

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We present a method, called BlockMatch, for aligning two blocks, where a block is an RNA multiple sequence alignment with the consensus secondary structure of the alignment in Stockholm format. The method employs a quadratic-time dynamic programming algorithm for aligning columns and column pairs of the multiple alignments in the blocks. Unlike many other tools that can perform pairwise alignment of either single sequences or structures only, BlockMatch takes into account the characteristics of all the sequences in the blocks along with their consensus structures during the alignment process, thus being able to achieve a high-quality alignment result. We apply BlockMatch to phylogeny reconstruction on a set of 5S rRNA sequences taken from fifteen bacteria species. Experimental results showed that the phylogenetic tree generated by our method is more accurate than the tree constructed based on the widely used ClustalW tool. The BlockMatch algorithm is implemented into a web server, accessible at . A jar file of the program is also available for download from the web server.
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16

Wilton, Richard, and Alexander S. Szalay. "Arioc: High-concurrency short-read alignment on multiple GPUs." PLOS Computational Biology 16, no. 11 (November 9, 2020): e1008383. http://dx.doi.org/10.1371/journal.pcbi.1008383.

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In large DNA sequence repositories, archival data storage is often coupled with computers that provide 40 or more CPU threads and multiple GPU (general-purpose graphics processing unit) devices. This presents an opportunity for DNA sequence alignment software to exploit high-concurrency hardware to generate short-read alignments at high speed. Arioc, a GPU-accelerated short-read aligner, can compute WGS (whole-genome sequencing) alignments ten times faster than comparable CPU-only alignment software. When two or more GPUs are available, Arioc's speed increases proportionately because the software executes concurrently on each available GPU device. We have adapted Arioc to recent multi-GPU hardware architectures that support high-bandwidth peer-to-peer memory accesses among multiple GPUs. By modifying Arioc's implementation to exploit this GPU memory architecture we obtained a further 1.8x-2.9x increase in overall alignment speeds. With this additional acceleration, Arioc computes two million short-read alignments per second in a four-GPU system; it can align the reads from a human WGS sequencer run–over 500 million 150nt paired-end reads–in less than 15 minutes. As WGS data accumulates exponentially and high-concurrency computational resources become widespread, Arioc addresses a growing need for timely computation in the short-read data analysis toolchain.
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17

Pei, Jimin. "Multiple protein sequence alignment." Current Opinion in Structural Biology 18, no. 3 (June 2008): 382–86. http://dx.doi.org/10.1016/j.sbi.2008.03.007.

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18

Gambin, Anna, and Rafał Otto. "Contextual Multiple Sequence Alignment." Journal of Biomedicine and Biotechnology 2005, no. 2 (2005): 124–31. http://dx.doi.org/10.1155/jbb.2005.124.

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In a recently proposed contextual alignment model, efficient algorithms exist for global and local pairwise alignment of protein sequences. Preliminary results obtained for biological data are very promising. Our main motivation was to adopt the idea of context dependency to the multiple-alignment setting. To this aim the relaxation of the model was developed (we call this new modelaveraged contextual alignment) and a new family of amino acids substitution matrices are constructed. In this paper we present a contextual multiple-alignment algorithm and report the outcomes of experiments performed for the BAliBASE test set. The contextual approach turned out to give much better results for the set of sequences containing orphan genes.
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19

ZHANG, ZHENG, BALAJI RAGHAVACHARI, ROSS C. HARDISON, and WEBB MILLER. "Chaining Multiple-Alignment Blocks." Journal of Computational Biology 1, no. 3 (January 1994): 217–26. http://dx.doi.org/10.1089/cmb.1994.1.217.

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20

Taylor, William R., Tomas P. Flores, and Christine A. Orengo. "Multiple protein structure alignment." Protein Science 3, no. 10 (October 1994): 1858–70. http://dx.doi.org/10.1002/pro.5560031025.

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21

Mitchell, C. "MultAlin–multiple sequence alignment." Bioinformatics 9, no. 5 (1993): 614. http://dx.doi.org/10.1093/bioinformatics/9.5.614.

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22

Hohl, M., S. Kurtz, and E. Ohlebusch. "Efficient multiple genome alignment." Bioinformatics 18, Suppl 1 (July 1, 2002): S312—S320. http://dx.doi.org/10.1093/bioinformatics/18.suppl_1.s312.

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23

Bray, N. "MAVID multiple alignment server." Nucleic Acids Research 31, no. 13 (July 1, 2003): 3525–26. http://dx.doi.org/10.1093/nar/gkg623.

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24

Wallace, I. M., O. Orla, and D. G. Higgins. "Evaluation of iterative alignment algorithms for multiple alignment." Bioinformatics 21, no. 8 (November 25, 2004): 1408–14. http://dx.doi.org/10.1093/bioinformatics/bti159.

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25

Errami, Mounir, Christophe Geourjon, and Gilbert Deléage. "Conservation of Amino Acids into Multiple Alignments Involved in Pairwise Interactions in Three-Dimensional Protein Structures." Journal of Bioinformatics and Computational Biology 01, no. 03 (October 2003): 505–20. http://dx.doi.org/10.1142/s0219720003000228.

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We present an original strategy, that involves a bioinformatic software structure, in order to perform an exhaustive and objective statistical analysis of three-dimensional structures of proteins. We establish the relationship between multiple sequences alignments and various structural features of proteins. We show that amino acids implied in disulfide bonds, salt bridges and hydrophobic interactions are particularly conserved. Effects of identity, global similarity within alignments, and accessibility of interactions have been studied. Furthermore, we point out that the more variable the sequences within a multiple alignment, the more informative the multiple alignment. The results support multiple alignments usefulness for predictions of structural features.
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26

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 (January 30, 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 alignment, we focus on the detection of individual conserved residues at a position. The major advantages of multiple comparison methods originate from their ability to select conserved residues simultaneously and to consider the variability of the residue estimates. Large-scale simulations were used for the comparative analysis of the methods. Practical performance was studied by comparing the structurally and functionally important residues of Src homology 2 (SH2) domains to the assignments of the conservation indices. The applicability of the indices was also compared in three additional protein families comprising different degrees of entropy and variability in alignment positions. The results indicate that statistical multiple comparison methods are sensitive and reliable in identifying conserved residues.
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27

Micale, Giovanni, Andrea Continella, Alfredo Ferro, Rosalba Giugno, and Alfredo Pulvirenti. "GASOLINE: a Cytoscape app for multiple local alignment of PPI networks." F1000Research 3 (September 23, 2014): 140. http://dx.doi.org/10.12688/f1000research.4537.2.

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Comparing protein interaction networks can reveal interesting patterns of interactions for a specific function or process in distantly related species. In this paper we present GASOLINE, a Cytoscape app for multiple local alignments of PPI (protein-protein interaction) networks. The app is based on the homonymous greedy and stochastic algorithm. GASOLINE starts with the identification of sets of similar nodes, called seeds of the alignment. Alignments are then extended in a greedy manner and finally refined. Both the identification of seeds and the extension of alignments are performed through an iterative Gibbs sampling strategy. GASOLINE is a Cytoscape app for computing and visualizing local alignments, without requiring any post-processing operations. GO terms can be easily attached to the aligned proteins for further functional analysis of alignments. GASOLINE can perform the alignment task in few minutes, even for a large number of input networks.
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Dijkstra, Maurits J. J., Atze J. van der Ploeg, K. Anton Feenstra, Wan J. Fokkink, Sanne Abeln, and Jaap Heringa. "Tailor-made multiple sequence alignments using the PRALINE 2 alignment toolkit." Bioinformatics 35, no. 24 (August 1, 2019): 5315–17. http://dx.doi.org/10.1093/bioinformatics/btz572.

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Abstract Summary PRALINE 2 is a toolkit for custom multiple sequence alignment workflows. It can be used to incorporate sequence annotations, such as secondary structure or (DNA) motifs, into the alignment scoring, as well as to customize many other aspects of a progressive multiple alignment workflow. Availability and implementation PRALINE 2 is implemented in Python and available as open source software on GitHub: https://github.com/ibivu/PRALINE/.
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29

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 simulated sequences. Currently, there is no databank available which may help researchers to download simulated sequences/alignments for their study. Major focus of our study was to develop a database of simulated protein sequences (SAliBASE) based on different varying parameters such as insertion rate, deletion rate, sequence length, number of sequences, and indel size. Each dataset has corresponding alignment as well. This repository is very useful for evaluating multiple alignment methods.
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Daniels, Noah M., Shilpa Nadimpalli, and Lenore J. Cowen. "Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment." BMC Bioinformatics 13, no. 1 (2012): 259. http://dx.doi.org/10.1186/1471-2105-13-259.

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31

Kamionskaya, A. M., and M. A. Korotkova. "Multiple Alignment of Promoter Sequences from the Human Genome." Biotekhnologiya 36, no. 4 (2020): 7–14. http://dx.doi.org/10.21519/0234-2758-2020-36-4-7-14.

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A new algorithm for multiple alignment of nucleotide sequences of MAHDS has been developed. A statistically significant multiple alignment of promoter sequences from the human genome was first created using this algorithm. Based on the constructed alignments, 25 classes of promoter sequences were created with the volume of each class exceeding 100 sequences. The classes of promoters can be used to search for promoter sequences in eukaryotic genomes. promoter, class, dynamic programming, human genome. The work was partially supported by the Russian Foundation for Basic Research (Grant no. 20-016-00057).
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32

Lin, Yu-Shiang, Chun-Yuan Lin, Hsiao-Chieh Chi, and Yeh-Ching Chung. "Multiple Sequence Alignments with Regular Expression Constraints on a Cloud Service System." International Journal of Grid and High Performance Computing 5, no. 3 (July 2013): 55–64. http://dx.doi.org/10.4018/jghpc.2013070105.

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Multiple sequence alignments with constraints are of priority concern in computational biology. Constrained sequence alignment incorporates the domain knowledge of biologists into sequence alignments such that the user-specified residues/segments are aligned together according to the alignment results. A series of constrained multiple sequence alignment tools have been developed in relevant literatures in the recent decade. GPU-REMuSiC is the most advanced method with the regular expression constraints, in which graphics processing units (GPUs) with CUDA are used. GPU-REMuSiC can achieve a speedup ratio of 29x for overall computation time based on the experimental results. However, the execution environment of GPU-REMuSiC must be constructed; it is a threshold for biologists to set up it. Therefore, this work presents an intuitive friendly user interface of GPU-REMuSiC for the potential cloud server with GPUs, called Cloud GPU-REMuSiC. Implementing the user interface via a network allows us to transmit the input data to a remote server without a complex cumbersome setting in a local host. Finally, the alignment results can be obtained from a remote cloud server with GPUs. Cloud GPU-REMuSiC is highly promising as an online application that is accessible without time or location constraints.
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Minyoung Kim. "Conditional Alignment Random Fields for Multiple Motion Sequence Alignment." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 11 (November 2013): 2803–9. http://dx.doi.org/10.1109/tpami.2013.95.

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Samal, Dibyaranjan, Nibedita Sahoo, and Meesala Krishna Murthy. "A Program for Multiple Sequence Alignment by Star Alignment." International Journal of Electrical, Electronics and Computers 3, no. 6 (2018): 1–7. http://dx.doi.org/10.22161/eec.3.6.1.

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35

Tang, Chuan Yi, Chin Lung Lu, Margaret Dah-Tsyr Chang, Yin-Te Tsai, Yuh-Ju Sun, Kun-Mao Chao, Jia-Ming 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 (July 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 function in catalyzing the degradation of RNA molecules. The resulting alignments illustrate the practicability of our method.
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36

Korotkov, Eugene V., Yulia M. Suvorova, Dmitrii O. Kostenko, and Maria A. Korotkova. "Multiple Alignment of Promoter Sequences from the Arabidopsis thaliana L. Genome." Genes 12, no. 2 (January 21, 2021): 135. http://dx.doi.org/10.3390/genes12020135.

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In this study, we developed a new mathematical method for performing multiple alignment of highly divergent sequences (MAHDS), i.e., sequences that have on average more than 2.5 substitutions per position (x). We generated sets of artificial DNA sequences with x ranging from 0 to 4.4 and applied MAHDS as well as currently used multiple sequence alignment algorithms, including ClustalW, MAFFT, T-Coffee, Kalign, and Muscle to these sets. The results indicated that most of the existing methods could produce statistically significant alignments only for the sets with x < 2.5, whereas MAHDS could operate on sequences with x = 4.4. We also used MAHDS to analyze a set of promoter sequences from the Arabidopsis thaliana genome and discovered many conserved regions upstream of the transcription initiation site (from −499 to +1 bp); a part of the downstream region (from +1 to +70 bp) also significantly contributed to the obtained alignments. The possibilities of applying the newly developed method for the identification of promoter sequences in any genome are discussed. A server for multiple alignment of nucleotide sequences has been created.
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37

Souza, Jairo Francisco de, Sean Wolfgand Matsui Siqueira, and Bernardo Nunes. "A framework to aggregate multiple ontology matchers." International Journal of Web Information Systems 16, no. 2 (October 16, 2019): 151–69. http://dx.doi.org/10.1108/ijwis-05-2019-0023.

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Purpose Although ontology matchers are annually proposed to address different aspects of the semantic heterogeneity problem, finding the most suitable alignment approach is still an issue. This study aims to propose a computational solution for ontology meta-matching (OMM) and a framework designed for developers to make use of alignment techniques in their applications. Design/methodology/approach The framework includes some similarity functions that can be chosen by developers and then, automatically, set weights for each function to obtain better alignments. To evaluate the framework, several simulations were performed with a data set from the Ontology Alignment Evaluation Initiative. Simple similarity functions were used, rather than aligners known in the literature, to demonstrate that the results would be more influenced by the proposed meta-alignment approach than the functions used. Findings The results showed that the framework is able to adapt to different test cases. The approach achieved better results when compared with existing ontology meta-matchers. Originality/value Although approaches for OMM have been proposed, it is not easy to use them during software development. On the other hand, this work presents a framework that can be used by developers to align ontologies. New ontology matchers can be added and the framework is extensible to new methods. Moreover, this work presents a novel OMM approach modeled as a linear equation system which can be easily computed.
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Badlani, Harshita, Abhinav Sinha, Abhinav Mittal, and Alok Kumar. "Multiple Sequence Alignment in Bioinformatics." International Journal of Advanced Research 4, no. 4 (April 30, 2016): 816–20. http://dx.doi.org/10.21474/ijar01/269.

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39

EDDY, S. "Multiple-alignment and -sequence searches." Trends in Biotechnology 16 (November 1998): 15–18. http://dx.doi.org/10.1016/s0167-7799(98)00130-9.

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40

S. Schwartz, A., and L. Pachter. "Multiple alignment by sequence annealing." Bioinformatics 23, no. 2 (January 15, 2007): e24-e29. http://dx.doi.org/10.1093/bioinformatics/btl311.

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41

Rausch, T., A. K. Emde, D. Weese, A. Doring, C. Notredame, and K. Reinert. "Segment-based multiple sequence alignment." Bioinformatics 24, no. 16 (August 9, 2008): i187—i192. http://dx.doi.org/10.1093/bioinformatics/btn281.

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42

MYERS, GENE, SANFORD SELZNICK, ZHENG ZHANG, and WEBB MILLER. "Progressive Multiple Alignment with Constraints." Journal of Computational Biology 3, no. 4 (January 1996): 563–72. http://dx.doi.org/10.1089/cmb.1996.3.563.

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43

Hein, J., J. L. Jensen, and C. N. S. Pedersen. "Recursions for statistical multiple alignment." Proceedings of the National Academy of Sciences 100, no. 25 (December 1, 2003): 14960–65. http://dx.doi.org/10.1073/pnas.2036252100.

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44

Ren, Jie, Kai Song, Fengzhu Sun, Minghua Deng, and Gesine Reinert. "Multiple alignment-free sequence comparison." Bioinformatics 29, no. 21 (August 29, 2013): 2690–98. http://dx.doi.org/10.1093/bioinformatics/btt462.

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45

Asparouhov, Tihomir, and Bengt Muthén. "Multiple-Group Factor Analysis Alignment." Structural Equation Modeling: A Multidisciplinary Journal 21, no. 4 (July 16, 2014): 495–508. http://dx.doi.org/10.1080/10705511.2014.919210.

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46

Sobel, Eric, and Hugo M. Martinez. "A multiple sequence alignment program." Nucleic Acids Research 14, no. 1 (1986): 363–74. http://dx.doi.org/10.1093/nar/14.1.363.

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47

Waterman, Michael S. "Multiple sequence alignment by consensus." Nucleic Acids Research 14, no. 22 (1986): 9095–102. http://dx.doi.org/10.1093/nar/14.22.9095.

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48

Nicholas, Hugh B., Alexander J. Ropelewski, and David W. Deerfield. "Strategies for Multiple Sequence Alignment." BioTechniques 32, no. 3 (March 2002): 572–91. http://dx.doi.org/10.2144/02323rv01.

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49

Solano-Roman, A., C. Cruz-Castillo, D. Offenhuber, and A. Colubri. "NX4: a web-based visualization of large multiple sequence alignments." Bioinformatics 35, no. 22 (June 4, 2019): 4800–4802. http://dx.doi.org/10.1093/bioinformatics/btz457.

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Abstract Summary Multiple Sequence Alignments (MSAs) are a fundamental operation in genome analysis. However, MSA visualizations such as sequence logos and matrix representations have changed little since the nineties and are not well suited for displaying large-scale alignments. We propose a novel, web-based MSA visualization tool called NX4, which can handle genome alignments comprising thousands of sequences. NX4 calculates the frequency of each nucleotide along the alignment and visually summarizes the results using a color-blind friendly palette that helps identifying regions of high genetic diversity. NX4 also provides the user with additional assistance in finding these regions with a ‘focus + context’ mechanism that uses a line chart of the Shannon entropy across the alignment. The tool offers geneticists an easy-to-use and scalable analysis for large MSA studies. Availability and implementation NX4 is freely available at https://www.nx4.io, and its source code at https://github.com/NX4/nx4. Supplementary information Supplementary data are available at Bioinformatics online
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50

Chen, Jing, and Jia Huang. "A novel network aligner for the analysis of multiple protein-protein interaction networks." Computer Science and Information Systems, no. 00 (2021): 30. http://dx.doi.org/10.2298/csis200909030c.

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The analysis of protein-protein interaction networks can transfer the knowledge of well-studied biological functions to functions that are not yet adequately investigated by constructing networks and extracting similar network structures in different species. Multiple network alignment can be used to find similar regions among multiple networks. In this paper, we introduce Accurate Combined Clustering Multiple Network Alignment (ACCMNA), which is a new and accurate multiple network alignment algorithm. It uses both topology and sequence similarity information. First, the importance of all the nodes is calculated according to the network structures. Second, the seed-and-extend framework is used to conduct an iterative search. In each iteration, a clustering method is combined to generate the alignment. Extensive experimental results show that ACCMNA outperformed the state-of-the-art algorithms in producing functionally consistent and topological conservation alignments within an acceptable running time.
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