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Journal articles on the topic 'Pairwise sequence alignment'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 June 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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2

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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3

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, an

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4

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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5

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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6

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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7

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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8

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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9

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 alignm

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10

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

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11

Pujari, Jeevana Jyothi, and Karteeka Pavan Kanadam. "Semi Global Pairwise Sequence Alignment Using New Chromosome Structure Genetic Algorithm." Ingénierie des systèmes d information 27, no. 1 (2022): 67–74. http://dx.doi.org/10.18280/isi.270108.

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Biological sequence alignment is a prominent and eminent task in the analysis of biological data. This paper proposes a pair wise semi global sequence alignment technique using New Chromosome Structure based Genetic algorithm (NCSGA) for aligning sequences by automatically detecting optimal number of gaps and their positions to explore the optimal score for DNA or protein sequences. The experimental results are conducted using simulated real datasets from NCBI. The proposed method can be tested on real data sets of nucleotide sequence pairs. The computational results show that NCSGA produces t

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12

Lailil, Muflikhah, and Santoso Edy. "Pairwise Sequence Alignment between HBV and HCC Using Modified Needleman-Wunsch Algorithm." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 4 (2017): 1785–93. https://doi.org/10.12928/TELKOMNIKA.v15i4.5813.

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Ths paper aims to find similarity of Hepatitis B virus (HBV) and Hepatocelluler Carcinoma (HCC) DNA sequences. It is very important in bioformatics task. The similarity of sequence allignments indicates that they have similarity of chemical and physical properties. Mutation of the virus DNA in X region has potential role in HCC. It is observed using pairwise sequence alignment of genotype-A in HBV. The complexity of DNA sequence using dynamic programming, Needleman-Wunsch algorithm, is very high. Therefore, it is purpose to modifiy the method of Needleman Wunsch algorithm for optimum global DN

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13

KONAGURTHU, ARUN SIDDHARTH, JAMES WHISSTOCK, and PETER J. STUCKEY. "PROGRESSIVE MULTIPLE ALIGNMENT USING SEQUENCE TRIPLET OPTIMIZATIONS AND THREE-RESIDUE EXCHANGE COSTS." Journal of Bioinformatics and Computational Biology 02, no. 04 (2004): 719–45. http://dx.doi.org/10.1142/s0219720004000831.

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In this paper we demonstrate a practical approach to construct progressive multiple alignments using sequence triplet optimizations rather than a conventional pairwise approach. Using the sequence triplet alignments progressively provides a scope for the synthesis of a three-residue exchange amino acid substitution matrix. We develop such a 20×20×20 matrix for the first time and demonstrate how its use in optimal sequence triplet alignments increases the sensitivity of building multiple alignments. Various comparisons were made between alignments generated using the progressive triplet methods

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14

Bandyopadhyay, S., and R. Mitra. "A Parallel Pairwise Local Sequence Alignment Algorithm." IEEE Transactions on NanoBioscience 8, no. 2 (2009): 139–46. http://dx.doi.org/10.1109/tnb.2009.2019642.

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15

Zhu, Xiangyuan, and Jian Li. "Chemical Reaction Optimization for Pairwise Sequence Alignment." Journal of Computational and Theoretical Nanoscience 12, no. 12 (2015): 5351–57. http://dx.doi.org/10.1166/jctn.2015.4526.

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16

Taylor, William Ramsay. "Multiple sequence alignment by a pairwise algorithm." Bioinformatics 3, no. 2 (1987): 81–87. http://dx.doi.org/10.1093/bioinformatics/3.2.81.

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17

Mullan, Lisa. "Pairwise sequence alignment—it's all about us!" Briefings in Bioinformatics 7, no. 1 (2006): 113–15. http://dx.doi.org/10.1093/bib/bbk008.

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18

González Laffitte, Marcos E., and Peter F. Stadler. "Progressive Multiple Alignment of Graphs." Algorithms 17, no. 3 (2024): 116. http://dx.doi.org/10.3390/a17030116.

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The comparison of multiple (labeled) graphs with unrelated vertex sets is an important task in diverse areas of applications. Conceptually, it is often closely related to multiple sequence alignments since one aims to determine a correspondence, or more precisely, a multipartite matching between the vertex sets. There, the goal is to match vertices that are similar in terms of labels and local neighborhoods. Alignments of sequences and ordered forests, however, have a second aspect that does not seem to be considered for graph comparison, namely the idea that an alignment is a superobject from

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19

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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20

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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21

Wilburn, Grey W., and Sean R. Eddy. "Remote homology search with hidden Potts models." PLOS Computational Biology 16, no. 11 (2020): e1008085. http://dx.doi.org/10.1371/journal.pcbi.1008085.

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Most methods for biological sequence homology search and alignment work with primary sequence alone, neglecting higher-order correlations. Recently, statistical physics models called Potts models have been used to infer all-by-all pairwise correlations between sites in deep multiple sequence alignments, and these pairwise couplings have improved 3D structure predictions. Here we extend the use of Potts models from structure prediction to sequence alignment and homology search by developing what we call a hidden Potts model (HPM) that merges a Potts emission process to a generative probability

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22

Fareed, Zeinab A., Hoda M. O. Mokhtar, and Ahmed Ahmed. "Gpcodon Alignment: A Global Pairwise Codon Based Sequence Alignment Approach." International Journal of Database Management Systems 8, no. 1 (2016): 1–12. http://dx.doi.org/10.5121/ijdms.2016.8101.

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23

Montañola, Alberto, Concepció Roig, and Porfidio Hernández. "Efficient mapping of genomic sequences to optimize multiple pairwise alignment in hybrid cluster platforms." Journal of Integrative Bioinformatics 11, no. 3 (2014): 60–71. http://dx.doi.org/10.1515/jib-2014-251.

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Summary Multiple sequence alignment (MSA), used in biocomputing to study similarities between different genomic sequences, is known to require important memory and computation resources. Nowadays, researchers are aligning thousands of these sequences, creating new challenges in order to solve the problem using the available resources efficiently. Determining the efficient amount of resources to allocate is important to avoid waste of them, thus reducing the economical costs required in running for example a specific cloud instance. The pairwise alignment is the initial key step of the MSA prob

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24

Agung, Widyo Utomo, Ananta Kusuma Wisnu, and Wahjuni Sri. "Parallelization of Pairwise Alignment and Neighbor-Joining Algorithm in Progressive Multiple Sequence Alignment." Indonesian Journal of Electrical Engineering and Computer 9, no. 1 (2018): 234–42. https://doi.org/10.11591/ijeecs.v9.i1.pp234-242.

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A Progressive multiple sequence alignment ClustalW is a widely used heuristic method for computing multiple sequence alignment (MSA). It has three stages: distance matrix computation using pairwise alignment, guide tree reconstruction using neighbor-joining and progressive alignment. To accelerate computing for large data, the progressive MSA algorithm needs to be parallelized. This research aims to identify, decompose and implement the pairwise alignment and neighbor-joining in progressive MSA ClustalW using message passing, shared memory and hybrid programming model in the computer cluster.

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25

Yakovlev, P. A. "Fast trie-based method for multiple pairwise sequence alignment." Доклады Академии наук 484, no. 4 (2019): 401–4. http://dx.doi.org/10.31857/s0869-56524844401-404.

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A method for efficient comparison of a symbol sequence with all strings of a set is presented, which performs considerably faster than the naive enumeration of comparisons with all strings in succession. The procedure is accelerated by applying an original algorithm combining a prefix tree and a standard dynamic programming algorithm searching for the edit distance (Levenshtein distance) between strings. The efficiency of the method is confirmed by numerical experiments with arrays consisting of tens of millions of biological sequences of variable domains of monoclonal antibodies.

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26

Asare, James Owusu, Justice Kwame Appati, and Kwaku Darkwah. "Formulation and Analysis of Patterns in a Score Matrix for Global Sequence Alignment." International Journal of Mathematics and Mathematical Sciences 2020 (June 1, 2020): 1–9. http://dx.doi.org/10.1155/2020/3858057.

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Global sequence alignment is one of the most basic pairwise sequence alignment procedures used in molecular biology to understand the similarity that arises among the structure, function, or evolutionary relationship between two nucleotide sequences. The general algorithm associated with global sequence alignment is the dynamic programming algorithm of Needleman and Wunsch. In this paper, patterns are exploited in the score matrix of the Needleman–Wunsch algorithm. With the help of some examples, the general patterns realized are formulated as new a priori propositions and corollaries that are

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27

Landan, Giddy, and Dan Graur. "Characterization of pairwise and multiple sequence alignment errors." Gene 441, no. 1-2 (2009): 141–47. http://dx.doi.org/10.1016/j.gene.2008.05.016.

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28

Utomo, Agung Widyo. "Parallelization of Pairwise Alignment and Neighbor-Joining Algorithm in Progressive Multiple Sequence Alignment." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 1 (2018): 234. http://dx.doi.org/10.11591/ijeecs.v9.i1.pp234-242.

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Progressive multiple sequence alignment ClustalW is a widely used heuristic method for computing multiple sequence alignment (MSA). It has three stages: distance matrix computation using pairwise alignment, guide tree reconstruction using neighbor-joining and progressive alignment. To accelerate computing for large data, the progressive MSA algorithm needs to be parallelized. This research aims to identify, decompose and implement the pairwise alignment and neighbor-joining in progressive MSA using message passing, shared memory and hybrid programming model in the computer cluster. The experim

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29

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 perform

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30

Zhang, Jikai, Haidong Lan, Yuandong Chan, Yuan Shang, Bertil Schmidt, and Weiguo Liu. "BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures." Bioinformatics 35, no. 13 (2018): 2306–8. http://dx.doi.org/10.1093/bioinformatics/bty930.

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Abstract Motivation Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors. Results BGSA outperforms Edlib, SeqAn and BitPAl for pairwise edit distance computations and Parasail, SeqAn and BitPAl when using more general scoring schemes for pairwise alignments of a batch of sequence reads o

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31

Vishnepolsky, Boris, and Malak Pirtskhalava. "ALIGN_MTX—An optimal pairwise textual sequence alignment program, adapted for using in sequence-structure alignment." Computational Biology and Chemistry 33, no. 3 (2009): 235–38. http://dx.doi.org/10.1016/j.compbiolchem.2009.04.003.

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32

Barlowe, Scott, Heather B. Coan, and Robert T. Youker. "SubVis: an interactive R package for exploring the effects of multiple substitution matrices on pairwise sequence alignment." PeerJ 5 (June 27, 2017): e3492. http://dx.doi.org/10.7717/peerj.3492.

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Understanding how proteins mutate is critical to solving a host of biological problems. Mutations occur when an amino acid is substituted for another in a protein sequence. The set of likelihoods for amino acid substitutions is stored in a matrix and input to alignment algorithms. The quality of the resulting alignment is used to assess the similarity of two or more sequences and can vary according to assumptions modeled by the substitution matrix. Substitution strategies with minor parameter variations are often grouped together in families. For example, the BLOSUM and PAM matrix families are

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33

Isa, M. N., K. Benkrid, and T. Clayton. "Efficient architecture and scheduling technique for pairwise sequence alignment." ACM SIGARCH Computer Architecture News 40, no. 4 (2012): 26–31. http://dx.doi.org/10.1145/2411116.2411121.

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34

Yakovlev, P. A. "Fast Trie-Based Method for Multiple Pairwise Sequence Alignment." Doklady Mathematics 99, no. 1 (2019): 64–67. http://dx.doi.org/10.1134/s1064562419010198.

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35

Abedulridha, Sara Q., and Eman S. Al-Shamery. "Pairwise DNA Sequence Alignment Using Multi-Zone Genetic Algorithm." Journal of Computational and Theoretical Nanoscience 16, no. 3 (2019): 935–40. http://dx.doi.org/10.1166/jctn.2019.7978.

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36

Abbasi, M., L. Paquete, A. Liefooghe, M. Pinheiro, and P. Matias. "Improvements on bicriteria pairwise sequence alignment: algorithms and applications." Bioinformatics 29, no. 8 (2013): 996–1003. http://dx.doi.org/10.1093/bioinformatics/btt098.

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37

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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38

Tyson, Hugh. "Relationships, derived from optimum alignments, among amino acid sequences of plant peroxidases." Canadian Journal of Botany 70, no. 3 (1992): 543–56. http://dx.doi.org/10.1139/b92-069.

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The amino acid and (or) DNA sequences of 13 plant peroxidases (EC 1.11.1.7), which include isozymes within species, are currently available in data bases; all have similar lengths of approximately 300 amino acids. Sequence relationships among these 13, plus 2 microbial peroxidases of similar length, were examined. The 15 sequences were compared in all 105 pairwise combinations using optimum alignment procedures. Gap penalties were determined from analysis of penalty change effects. Distances between sequences generated by optimum alignments were analysed by clustering techniques to generate de

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39

Lee, Sung Jong, Keehyoung Joo, Sangjin Sim, Juyong Lee, In-Ho Lee, and Jooyoung Lee. "CRFalign: A Sequence-Structure Alignment of Proteins Based on a Combination of HMM-HMM Comparison and Conditional Random Fields." Molecules 27, no. 12 (2022): 3711. http://dx.doi.org/10.3390/molecules27123711.

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Sequence–structure alignment for protein sequences is an important task for the template-based modeling of 3D structures of proteins. Building a reliable sequence–structure alignment is a challenging problem, especially for remote homologue target proteins. We built a method of sequence–structure alignment called CRFalign, which improves upon a base alignment model based on HMM-HMM comparison by employing pairwise conditional random fields in combination with nonlinear scoring functions of structural and sequence features. Nonlinear scoring part is implemented by a set of gradient boosted regr

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Zhao, Xing-Ming, Yiu-Ming Cheung, and De-Shuang Huang. "A Novel Markov Pairwise Protein Sequence Alignment Method for Sequence 665 Comparison." Protein & Peptide Letters 12, no. 7 (2005): 665–69. http://dx.doi.org/10.2174/0929866054696190.

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Matsumoto, Yuki, and Shota Nakamura. "qualign: solving sequence alignment based on quadratic unconstrained binary optimisation." EMBnet.journal 28 (March 8, 2023): e1020. http://dx.doi.org/10.14806/ej.28.0.1020.

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Bioinformatics has, among others, the issue of solving complex computational problems with vast amounts of sequencing data. Recently, a new computing architecture, the annealing machine, has emerged that applies to actual problems and is available for practical use. This novel architecture can solve discrete optimisation problems by replacing algorithms designed under the von Neumann architecture. To perform computations on the annealing machine, quadratic unconstrained binary optimisation (QUBO) formulations should be constructed and optimised according to the application. In this study, we d

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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 (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,

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BACKOFEN, ROLF, and SEBASTIAN WILL. "LOCAL SEQUENCE-STRUCTURE MOTIFS IN RNA." Journal of Bioinformatics and Computational Biology 02, no. 04 (2004): 681–98. http://dx.doi.org/10.1142/s0219720004000818.

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Ribonuclic acid (RNA) enjoys increasing interest in molecular biology; despite this interest fundamental algorithms are lacking, e.g. for identifying local motifs. As proteins, RNA molecules have a distinctive structure. Therefore, in addition to sequence information, structure plays an important part in assessing the similarity of RNAs. Furthermore, common sequence-structure features in two or several RNA molecules are often only spatially local, where possibly large parts of the molecules are dissimilar. Consequently, we address the problem of comparing RNA molecules by computing an optimal

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44

Xia, Xuhua. "Post-Alignment Adjustment and Its Automation." Genes 12, no. 11 (2021): 1809. http://dx.doi.org/10.3390/genes12111809.

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Multiple sequence alignment (MSA) is the basis for almost all sequence comparison and molecular phylogenetic inferences. Large-scale genomic analyses are typically associated with automated progressive MSA without subsequent manual adjustment, which itself is often error-prone because of the lack of a consistent and explicit criterion. Here, I outlined several commonly encountered alignment errors that cannot be avoided by progressive MSA for nucleotide, amino acid, and codon sequences. Methods that could be automated to fix such alignment errors were then presented. I emphasized the utility o

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Havgaard, J. H., R. B. Lyngso, G. D. Stormo, and J. Gorodkin. "Pairwise local structural alignment of RNA sequences with sequence similarity less than 40%." Bioinformatics 21, no. 9 (2005): 1815–24. http://dx.doi.org/10.1093/bioinformatics/bti279.

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46

Song, Yong-Joon, Dong Jin Ji, Hyein Seo, Gyu-Bum Han, and Dong-Ho Cho. "Pairwise Heuristic Sequence Alignment Algorithm Based on Deep Reinforcement Learning." IEEE Open Journal of Engineering in Medicine and Biology 2 (2021): 36–43. http://dx.doi.org/10.1109/ojemb.2021.3055424.

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47

Khaled, H., R. El Gohary, N. L. Badr, and H. M. Faheem. "Accelerating pairwise DNA Sequence Alignment using the CUDA compatible GPU." International Journal of Computer Applications 84, no. 1 (2013): 25–31. http://dx.doi.org/10.5120/14542-2619.

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48

Comet, J. P., and J. Henry. "Pairwise sequence alignment using a PROSITE pattern-derived similarity score." Computers & Chemistry 26, no. 5 (2002): 421–36. http://dx.doi.org/10.1016/s0097-8485(02)00005-0.

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Garai, Gautam, and Biswanath Chowdhury. "A cascaded pairwise biomolecular sequence alignment technique using evolutionary algorithm." Information Sciences 297 (March 2015): 118–39. http://dx.doi.org/10.1016/j.ins.2014.11.009.

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Li, W., J. Wang, and J. A. Feng. "NdPASA: a pairwise sequence alignment server for distantly related proteins." Bioinformatics 21, no. 19 (2005): 3803–5. http://dx.doi.org/10.1093/bioinformatics/bti619.

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