Relevant bibliographies by topics / Sequence alignment

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sequence alignment'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 October 2024

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Journal articles on the topic "Sequence alignment"

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Staritzbichler, René, Edoardo Sarti, Emily Yaklich, Antoniya Aleksandrova, Marcus Stamm, Kamil Khafizov, and Lucy R. Forrest. "Refining pairwise sequence alignments of membrane proteins by the incorporation of anchors." PLOS ONE 16, no. 4 (April 30, 2021): e0239881. http://dx.doi.org/10.1371/journal.pone.0239881.

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The alignment of primary sequences is a fundamental step in the analysis of protein structure, function, and evolution, and in the generation of homology-based models. Integral membrane proteins pose a significant challenge for such sequence alignment approaches, because their evolutionary relationships can be very remote, and because a high content of hydrophobic amino acids reduces their complexity. Frequently, biochemical or biophysical data is available that informs the optimum alignment, for example, indicating specific positions that share common functional or structural roles. Currently, if those positions are not correctly matched by a standard pairwise sequence alignment procedure, the incorporation of such information into the alignment is typically addressed in an ad hoc manner, with manual adjustments. However, such modifications are problematic because they reduce the robustness and reproducibility of the aligned regions either side of the newly matched positions. Previous studies have introduced restraints as a means to impose the matching of positions during sequence alignments, originally in the context of genome assembly. Here we introduce position restraints, or “anchors” as a feature in our alignment tool AlignMe, providing an aid to pairwise global sequence alignment of alpha-helical membrane proteins. Applying this approach to realistic scenarios involving distantly-related and low complexity sequences, we illustrate how the addition of anchors can be used to modify alignments, while still maintaining the reproducibility and rigor of the rest of the alignment. Anchored alignments can be generated using the online version of AlignMe available at www.bioinfo.mpg.de/AlignMe/.
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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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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 documented using JSDOC and is available from http://www.bioinf.org.uk/software/jsav/.
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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 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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Aadland, Kelsey, and Bryan Kolaczkowski. "Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy." Genome Biology and Evolution 12, no. 9 (August 12, 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 sequence alignment can more strongly affect inferred ancestral sequences. Here, we show that errors in sequence alignment can produce errors in ASR across a range of realistic and simplified evolutionary scenarios. Importantly, sequence reconstruction errors can lead to errors in estimates of structural and functional properties of ancestral proteins, potentially undermining the reliability of analyses relying on ASR. We introduce an alignment-integrated ASR approach that combines information from many different sequence alignments. We show that integrating alignment uncertainty improves ASR accuracy and the accuracy of downstream structural and functional inferences, often performing as well as highly accurate structure-guided alignment. Given the growing evidence that sequence alignment errors can impact the reliability of ASR studies, we recommend that future studies incorporate approaches to mitigate the impact of alignment uncertainty. Probabilistic modeling of insertion and deletion events has the potential to radically improve ASR accuracy when the model reflects the true underlying evolutionary history, but further studies are required to thoroughly evaluate the reliability of these approaches under realistic conditions.
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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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FÜRER, MARTIN, and WEBB MILLER. "ALIGNMENT-TO-ALIGNMENT EDITING WITH “MOVE GAP” OPERATIONS." International Journal of Foundations of Computer Science 07, no. 01 (March 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 relevant values of r, d, i and j. We show that the problem of determining a shortest sequence of such operations that converts one given alignment to another is NP-hard and give a polynomial-time algorithm that always comes within a factor 5/4 of optimality. An application of these ideas to alignments of long DNA sequences is discussed.
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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 (October 1, 2011): 78–82. http://dx.doi.org/10.15373/2249555x/apr2012/24.

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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 (November 15, 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; and (2) the ability to perform fuzzy searches within the columns of sequence data in multiple sequence alignments to determine the distribution of sequence variants among the sequences. The intuitive interface focuses on the presentation of results in easily understood visualizations and providing the ability to annotate the sequences in a multiple alignment with analytic and user data.
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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 for graphical sequence alignment using HPC have been reported. Here, we present G-SAIP (Graphical Sequence Alignment in Parallel), a software capable of spawning multiple distributed processes on CPUs, over a supercomputing infrastructure to speed up the execution time for dot-plot generation up to 1.68× compared with other current fastest tools, improve the efficiency for comparative structural genomic analysis, phylogenetics because the benefits of pairwise alignments for comparison between genomes, repetitive structure identification, and assembly quality checking.
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Dissertations / Theses on the topic "Sequence alignment"

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Starrett, Dean. "Optimal Alignment of Multiple Sequence Alignments." Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/194840.

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An essential tool in biology is the alignment of multiple sequences. Biologists use multiple sequence alignments for tasks such as predicting protein structure and function, reconstructing phylogenetic trees, and finding motifs. Constructing high-quality multiple alignments is computationally hard, both in theory and in practice, and is typically done using heuristic methods. The majority of state-of-the-art multiple alignment programs employ a form and polish strategy, where in the construction phase, an initial multiple alignment is formed by progressively merging smaller alignments, starting with single sequences. Then in a local-search phase, the resulting alignment is polished by repeatedly splitting it into smaller alignments and re-merging. This merging of alignments, the basic computational problem in the construction and local-search phases of the best multiple alignment heuristics, is called the Aligning Alignments Problem. Under the sum-of-pairs objective for scoring multiple alignments, this problem may seem to be a simple extension of two-sequence alignment. It is proven here, however, that with affine gap costs (which are recognized as necessary to get biologically-informative alignments) the problem is NP-complete when gaps are counted exactly. Interestingly, this form of multiple alignment is polynomial-time solvable when we relax the exact count, showing that exact gap counts themselves are inherently hard in multiple sequence alignment. Unlike general multiple alignment however, we show that Aligning Alignments with affine gap costs and exact counts is tractable in practice, by demonstrating an effective algorithm and a fast implementation. Our software AlignAlign is both time- and space-efficient on biological data. Computational experiments on biological data show instances derived from standard benchmark suites can be optimally aligned with surprising efficiency, and experiments on simulated data show the time and space both scale well.
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Chia, Nicholas Lee-Ping. "Sequence alignment." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1154616122.

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Al, Ghamdi Manal. "Video sequence alignment." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/9056/.

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The task of aligning multiple audio visual sequences with similar contents needs careful synchronisation in both spatial and temporal domains. It is a challenging task due to a broad range of contents variations, background clutter, occlusions, and other factors. This thesis is concerned with aligning video contents by characterising the spatial and temporal information embedded in the high-dimensional space. To that end a three- stage framework is developed, involving space-time representation of video clips with local linear coding, followed by their alignment in the manifold embedded space. The first two stages present a video representation techniques based on local feature extraction and linear coding methods. Firstly, the scale invariant feature transform (SIFT) is extended to extract interest points not only from the spatial plane but also from the planes along the space-time axis. Locality constrained coding is then incorporated to project each descriptor into a local coordinate system produced by a pooling technique. Human action classification benchmarks are adopted to evaluate these two stages, comparing their performance against existing techniques. The results shows that space-time extension of SIFT with a linear coding scheme outperforms most of the state-of-the-art approaches on the action classification task owing to its ability to represent complex events in video sequences. The final stage presents a manifold learning algorithm with spatio-temporal constraints to embed a video clip in a lower dimensional space while preserving the intrinsic geometry of the data. The similarities observed between frame sequences are captured by defining two types of correlation graphs: an intra-correlation graph within a single video sequence and an inter-correlation graph between two sequences. A video retrieval and ranking tasks are designed to evaluate the manifold learning stage. The experimental outcome shows that the approach outperforms the conventional techniques in defining similar video contents and capture the spatio-temporal correlations between them.
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Sammeth, Michael. "Integrated multiple sequence alignment." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=98148767X.

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Powell, David Richard 1973. "Algorithms for sequence alignment." Monash University, School of Computer Science and Software Engineering, 2001. http://arrow.monash.edu.au/hdl/1959.1/8051.

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Birney, Ewan. "Sequence alignment in bioinformatics." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621653.

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Fleissner, Roland. "Sequence alignment and phylogenetic inference." Berlin : Logos Verlag, 2004. http://diss.ub.uni-duesseldorf.de/ebib/diss/file?dissid=769.

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Fleissner, Roland. "Sequence alignment and phylogenetic inference." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=971844704.

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Auer, Jens. "Metaheuristic Multiple Sequence Alignment Optimisation." Thesis, University of Skövde, School of Humanities and Informatics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-899.

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The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restarting

a local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA. After that, ILS will be evaluated and compared to other MSA algorithms by BAliBASE (Thomson et al., 1999), a set of manually refined alignments used in most recent publications of algorithms and in at least two MSA algorithm surveys. The runtime-behaviour will be evaluated using runtime-distributions.

The quality of alignments produced by ILS is at least as good as the best algorithms available and significantly superiour to previously published Metaheuristics for MSA, Tabu Search and Genetic Algorithm (SAGA). On the average, ILS performed best in five out of eight test cases, second for one test set and third for the remaining two. A drawback of all iterative methods for MSA is the long runtime needed to produce good alignments. ILS needs considerably less runtime than Tabu Search and SAGA, but can not compete with progressive or consistency based methods, e. g. ClustalW or T-COFFEE.

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Arvestad, Lars. "Algorithms for biological sequence alignment." Doctoral thesis, KTH, Numerisk analys och datalogi, NADA, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-2905.

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Books on the topic "Sequence alignment"

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Katoh, Kazutaka, ed. Multiple Sequence Alignment. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1036-7.

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Russell, David J., ed. Multiple Sequence Alignment Methods. Totowa, NJ: Humana Press, 2014. http://dx.doi.org/10.1007/978-1-62703-646-7.

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Russell, David James. Multiple sequence alignment methods. New York: Humana Press, 2014.

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DeBlasio, Dan, and John Kececioglu. Parameter Advising for Multiple Sequence Alignment. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64918-4.

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1972-, Rosenberg Michael S., ed. Sequence alignment: Methods, models, concepts, and strategies. Berkeley: University of California Press, 2009.

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Louxin, Zhang, ed. Sequence comparison: Theory and methods. London: Springer, 2009.

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Sharma, Kal Renganathan. Bioinformatics. New York: McGraw-Hill, 2008.

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Nguyen, Ken, Xuan Guo, and Yi Pan. Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Applications. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119273769.

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Chou, Wan-Sheng. Distributed computing system for bioinformatics: Sequence alignment with Smith-Waterman. [s.l: The Author], 2005.

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Perrey, Sören W. Fast approximation to the NP-hard problem of multiple sequence alignment. Palmerston North, N.Z: Faculty of Information and Mathematical Sciences, Massey University, 1996.

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Book chapters on the topic "Sequence alignment"

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Axelson-Fisk, Marina. "Sequence Alignment." In Comparative Gene Finding, 107–74. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6693-1_3.

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Axelson-Fisk, Marina. "Sequence Alignment." In Comparative Gene Finding, 89–155. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-104-2_3.

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Erciyes, K. "Sequence Alignment." In Computational Biology, 111–33. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24966-7_6.

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Yau, Stephen S. T., Xin Zhao, Kun Tian, and Hongyu Yu. "Sequence Alignment." In Interdisciplinary Applied Mathematics, 27–42. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48295-3_3.

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Atri, Benu, and Olivier Lichtarge. "Sequence Alignment." In Bioinformatics: Sequences, Structures, Phylogeny, 47–69. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1562-6_3.

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Gupta, Manoj Kumar, Gayatri Gouda, N. Rajesh, Ravindra Donde, S. Sabarinathan, Pallabi Pati, Sushil Kumar Rathore, Ramakrishna Vadde, and Lambodar Behera. "Sequence Alignment." In Bioinformatics in Rice Research, 129–62. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3993-7_7.

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Tiwary, Basant K. "Sequence Alignment." In Bioinformatics and Computational Biology, 53–63. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4241-8_4.

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Xia, Xuhua. "Sequence Alignment." In Bioinformatics and the Cell, 33–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90684-3_2.

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Pirovano, Walter, and Jaap Heringa. "Multiple Sequence Alignment." In Bioinformatics, 143–61. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-159-2_7.

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Singh, Gautam B. "Multiple Sequence Alignment." In Fundamentals of Bioinformatics and Computational Biology, 143–58. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11403-3_7.

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Conference papers on the topic "Sequence alignment"

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Carceroni, R. L., F. L. C. Padua, G. A. M. R. Santos, and K. N. Kutulakos. "Linear sequence-to-sequence alignment." In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/cvpr.2004.1315106.

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Dinh, Andrew, Daniel Brill, Yaohang Li, and Wu He. "Malware Sequence Alignment." In 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom). IEEE, 2016. http://dx.doi.org/10.1109/bdcloud-socialcom-sustaincom.2016.96.

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

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Churchill, Gary A. "Monte Carlo sequence alignment." In the first annual international conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/267521.267534.

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KSCHISCHO, MAIK, and MICHAEL LÄSSIG. "Finite-temperature Sequence Alignment." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 1999. http://dx.doi.org/10.1142/9789814447331_0060.

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Seo, Jong-kyu, Hae-sung Tak, and Hwan-gue Cho. "Multi-level sequence alignment." In the 8th International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2557977.2558053.

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Shruthi, K., Akshat Gupta, and D. Pavitra. "Sequence Alignment using PSoC." In 2018 4th International Conference for Convergence in Technology (I2CT). IEEE, 2018. http://dx.doi.org/10.1109/i2ct42659.2018.9058082.

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

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

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Choujaa, Driss, and Naranker Dulay. "Routine classification through sequence alignment." In the seventeen ACM international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1631272.1631401.

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Reports on the topic "Sequence alignment"

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Rangwala, Huzefa, and George Karypis. Incremental Window-based Protein Sequence Alignment Algorithms. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada444856.

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Yee, M. L., and D. C. Craft. Fast DNA sequence alignment using optical computing. Office of Scientific and Technical Information (OSTI), November 1996. http://dx.doi.org/10.2172/414348.

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Steffenson, B. J., I. Mayrose, Gary J. Muehlbauer, and A. Sharon. ing and comparative sequence analysis of powdery mildew and leaf rust resistance gene complements in wild barley. Israel: United States-Israel Binational Agricultural Research and Development Fund, 2021. http://dx.doi.org/10.32747/2021.8134173.bard.

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Our overall, long-term goal is to exploit the genetic diversity present in cereal wild relatives for the development of cultivars with durable disease resistance. Our specific objectives for this proposal were to: 1) Utilize Association Genetics Resistance Gene Enrichment Sequencing (AgRenSeq) to identify and clone powdery mildew and leaf rust resistance gene complements in wild barley and 2) Conduct comparative sequence analyses of the cloned resistance genes to elucidate the basis of their specificity and evolution. The deployment of resistant cultivars is the most effective, economically efficient, and environmentally sound means of controlling plant diseases, especially in small grain cereals. The systems selected for study in this proposal are barley (Hordeum vulgare ssp. vulgare, Hvv), its wild progenitor (Hordeum vulgare ssp. spontaneum, Hvs) and the powdery mildew (Blumeria graminis f. sp. hordei, Bgs) and leaf rust (Puccinia hordei, Ph) pathogens. We compiled a diverse panel of Hvs accessions (the Wild Barley Diversity Collection or WBDC; N = 314) from across its native range and evaluated it to 40 isolates of Bgs and 12 isolates of Ph. We obtained genomic DNA sequences enriched for Nucleotide Binding Site-Leucine Rich Repeat (NLR) type resistance genes for 203 WBDC accessions, plus cultivar Morex for which the first reference genome sequence of barley was based. We assembled the 250 bp Illumina sequencing reads into contigs using CLC assembly cell. From this effort, we successfully assembled the sequences of 201 WBDC accessions plus Morex and used NLR Parser to identify contigs containing NLR genes. AgRenSeq was then used to identify k-mers (short oligonucleotide sequences of length k) that were associated with resistance to each isolate of the two pathogens. This analysis was performed individually for all WBDC accessions and each individual pathogen race (9,898 host accession x pathogen race combinations). We visualized the results from these analyses in Manhattan plots and identified 311 and 144 peaks for powdery mildew and leaf rust resistance, respectively. The next step in the analysis was to identify the contigs associated with the peaks in the Manhattan plots. BLAST (Basic Local Alignment Search Tool) searches were employed to identify closely related contigs in other WBDC accessions or in Morex. We identified two candidate R genes that were only present in resistant WBDC accessions. One of these was present in seven WBDC lines and was associated with resistance to four leaf rust isolates. BLAST analysis of this gene revealed that it was Rph15, one of the most widely effective leaf rust resistance genes reported in Hordeum. This gene was cloned and functionally validated in association with our Australian colleagues (Cheng et al., 2021). We are currently in the process of cloning six of other resistance genes: four for powdery mildew and two for leaf rust. As the contigs do not contain much of the promoter sequences, we have employed a genome walking approach to identify 2,500 bp of promoter sequence. To speed up and simplify the cloning of resistance genes from the WBDC, the PI established the International Wild Barley Sequencing Consortium (IWBSC; https://iwbsc.umn.edu/) comprised of over 60 researchers from 14 different countries and raised over $150,000 through crowdfunding to pay for 10X depth sequence coverage. Genome-wide association study (GWAS) of whole genome sequencing (WGS) data identified extremely strong and clear signals of association for several resistance genes which will facilitate gene cloning in concert with a wild barley pan-genome currently under construction. The cloning of multiple resistance gene can facilitate the development of durably resistant cultivars by inserting, through transgenesis, cassettes of multiple resistance genes.
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Butler, R., T. Butler, I. Foster, N. Karonis, R. Olson, R. Overbeek, N. Pfluger, M. Price, and S. Tuecke. Generating alignments of genetic sequences. Office of Scientific and Technical Information (OSTI), June 1989. http://dx.doi.org/10.2172/6081785.

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Shah, Nameeta, Olivier Couronne, Len A. Pennacchio, Michael Brudno, Serafim Batzoglou, E. Wes Bethel, Edward M. Rubin, Bernd Hamann, and Inna Dubchak. Phylo-VISTA: An Interactive Visualization Tool for Multiple DNA Sequence Alignments. Office of Scientific and Technical Information (OSTI), April 2004. http://dx.doi.org/10.2172/832965.

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Gardner, S., and C. Jaing. Interim Report on Multiple Sequence Alignments and TaqMan Signature Mapping to Phylogenetic Trees. Office of Scientific and Technical Information (OSTI), March 2012. http://dx.doi.org/10.2172/1047247.

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7

Taylor, Ronald C. Automated insertion of sequences into a ribosomal RNA alignment: An application of computational linguistics in molecular biology. Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/10108317.

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Taylor, R. C. Automated insertion of sequences into a ribosomal RNA alignment: An application of computational linguistics in molecular biology. Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/6057182.

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Rafaeli, Ada, and Russell Jurenka. Molecular Characterization of PBAN G-protein Coupled Receptors in Moth Pest Species: Design of Antagonists. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7593390.bard.

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The proposed research was directed at determining the activation/binding domains and gene regulation of the PBAN-R’s thereby providing information for the design and screening of potential PBAN-R-blockers and to indicate possible ways of preventing the process from proceeding to its completion. Our specific aims included: (1) The identification of the PBAN-R binding domain by a combination of: (a) in silico modeling studies for identifying specific amino-acid side chains that are likely to be involved in binding PBAN with the receptor and; (b) bioassays to verify the modeling studies using mutant receptors, cell lines and pheromone glands (at tissue and organism levels) against selected, designed compounds to confirm if compounds are agonists or antagonists. (2) The elucidation ofthemolecular regulationmechanisms of PBAN-R by:(a) age-dependence of gene expression; (b) the effect of hormones and; (c) PBAN-R characterization in male hair-pencil complexes. Background to the topic Insects have several closely related G protein-coupled receptors (GPCRs) belonging to the pyrokinin/PBAN family, one with the ligand pheromone biosynthesis activating neuropeptide or pyrokinin-2 and another with diapause hormone or pyrokinin-1 as a ligand. We were unable to identify the diapause hormone receptor from Helicoverpa zea despite considerable effort. A third, related receptor is activated by a product of the capa gene, periviscerokinins. The pyrokinin/PBAN family of GPCRs and their ligands has been identified in various insects, such as Drosophila, several moth species, mosquitoes, Triboliumcastaneum, Apis mellifera, Nasoniavitripennis, and Acyrthosiphon pisum. Physiological functions of pyrokinin peptides include muscle contraction, whereas PBAN regulates pheromone production in moths plus other functions indicating the pleiotropic nature of these ligands. Based on the alignment of annotated genomic sequences, the primary and secondary structures of the pyrokinin/PBAN family of receptors have similarity with the corresponding structures of the capa or periviscerokinin receptors of insects and the neuromedin U receptors found in vertebrates. Major conclusions, solutions, achievements Evolutionary trace analysisof receptor extracellular domains exhibited several class-specific amino acid residues, which could indicate putative domains for activation of these receptors by ligand recognition and binding. Through site-directed point mutations, the 3rd extracellular domain of PBAN-R was shown to be critical for ligand selection. We identified three receptors that belong to the PBAN family of GPCRs and a partial sequence for the periviscerokinin receptor from the European corn borer, Ostrinianubilalis. Functional expression studies confirmed that only the C-variant of the PBAN-R is active. We identified a non-peptide agonist that will activate the PBAN-receptor from H. zea. We determined that there is transcriptional control of the PBAN-R in two moth species during the development of the pupa to adult, and we demonstrated that this transcriptional regulation is independent of juvenile hormone biosynthesis. This transcriptional control also occurs in male hair-pencil gland complexes of both moth species indicating a regulatory role for PBAN in males. Ultimate confirmation for PBAN's function in the male tissue was revealed through knockdown of the PBAN-R using RNAi-mediated gene-silencing. Implications, both scientific and agricultural The identification of a non-peptide agonist can be exploited in the future for the design of additional compounds that will activate the receptor and to elucidate the binding properties of this receptor. The increase in expression levels of the PBAN-R transcript was delineated to occur at a critical period of 5 hours post-eclosion and its regulation can now be studied. The mysterious role of PBAN in the males was elucidated by using a combination of physiological, biochemical and molecular genetics techniques.
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Rafaeli, Ada, Russell Jurenka, and Chris Sander. Molecular characterisation of PBAN-receptors: a basis for the development and screening of antagonists against Pheromone biosynthesis in moth pest species. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7695862.bard.

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The original objectives of the approved proposal included: (a) The determination of species- and tissue-specificity of the PBAN-R; (b) the elucidation of the role of juvenile hormone in gene regulation of the PBAN-R; (c) the identificationof the ligand binding domains in the PBAN-R and (d) the development of efficient screening assays in order to screen potential antagonists that will block the PBAN-R. Background to the topic: Moths constitute one of the major groups of pest insects in agriculture and their reproductive behavior is dependent on chemical communication. Sex-pheromone blends are utilised by a variety of moth species to attract conspecific mates. In most of the moth species sex-pheromone biosynthesis is under circadian control by the neurohormone, PBAN (pheromone-biosynthesis-activating neuropeptide). In order to devise ideal strategies for mating disruption/prevention, we proposed to study the interactions between PBAN and its membrane-bound receptor in order to devise potential antagonists. Major conclusions: Within the framework of the planned objectives we have confirmed the similarities between the two Helicoverpa species: armigera and zea. Receptor sequences of the two Helicoverpa spp. are 98% identical with most changes taking place in the C-terminal. Our findings indicate that PBAN or PBAN-like receptors are also present in the neural tissues and may represent a neurotransmitter-like function for PBAN-like peptides. Surprisingly the gene encoding the PBAN-receptor was also present in the male homologous tissue, but it is absent at the protein level. The presence of the receptor (at the gene- and protein-levels), and the subsequent pheromonotropic activity are age-dependent and up-regulated by Juvenile Hormone in pharate females but down-regulated by Juvenile Hormone in adult females. Lower levels of pheromonotropic activity were observed when challenged with pyrokinin-like peptides than with HezPBAN as ligand. A model of the 3D structure of the receptor was created using the X-ray structure of rhodopsin as a template after sequence alignment of the HezPBAN-R with several other GPCRs and computer simulated docking with the model predicted putative binding sites. Using in silico mutagenesis the predicted docking model was validated with experimental data obtained from expressed chimera receptors in Sf9 cells created by exchanging between the three extracellular loops of the HezPBAN-R and the Drosophila Pyrokinin-R (CG9918). The chimera receptors also indicated that the 3ʳᵈ extracellular loop is important for recognition of PBAN or Diapause hormone ligands. Implications: The project has successfully completed all the objectives and we are now in a position to be able to design and screen potential antagonists for pheromone production. The successful docking simulation-experiments encourage the use of in silico experiments for initial (high-throughput) screening of potential antagonists. However, the differential responses between the expressed receptor (Sf9 cells) and the endogenous receptor (pheromone glands) emphasize the importance of assaying lead compounds using several alternative bioassays (at the cellular, tissue and organism levels). The surprising discovery of the presence of the gene encoding the PBAN-R in the male homologous tissue, but its absence at the protein level, launches opportunities for studying molecular regulation pathways and the evolution of these GPCRs. Overall this research will advance research towards the goal of finding antagonists for this important class of receptors that might encompass a variety of essential insect functions.
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