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: 25 July 2025

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Journal articles on the topic "Sequence alignment"

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

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The alignment of primary sequences is a fundamental step in the analysis of protein structure, function, and evolution, and in the generation of homology-based models. Integral membrane proteins pose a significant challenge for such sequence alignment approaches, because their evolutionary relationships can be very remote, and because a high content of hydrophobic amino acids reduces their complexity. Frequently, biochemical or biophysical data is available that informs the optimum alignment, for example, indicating specific positions that share common functional or structural roles. Currently
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Pervez, Muhammad Tariq, Hayat Ali Shah, Masroor Ellahi Babar, Nasir Naveed, and Muhammad Shoaib. "SAliBASE: A Database of Simulated Protein Alignments." Evolutionary Bioinformatics 15 (January 2019): 117693431882108. http://dx.doi.org/10.1177/1176934318821080.

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Simulated alignments are alternatives to manually constructed multiple sequence alignments for evaluating performance of multiple sequence alignment tools. The importance of simulated sequences is recognized because their true evolutionary history is known, which is very helpful for reconstructing accurate phylogenetic trees and alignments. However, generating simulated alignments require expertise to use bioinformatics tools and consume several hours for reconstructing even a few hundreds of simulated sequences. It becomes a tedious job for an end user who needs a few datasets of variety of s
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Martin, Andrew C. R. "Viewing multiple sequence alignments with the JavaScript Sequence Alignment Viewer (JSAV)." F1000Research 3 (October 23, 2014): 249. http://dx.doi.org/10.12688/f1000research.5486.1.

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The JavaScript Sequence Alignment Viewer (JSAV) is designed as a simple-to-use JavaScript component for displaying sequence alignments on web pages. The display of sequences is highly configurable with options to allow alternative coloring schemes, sorting of sequences and ’dotifying’ repeated amino acids. An option is also available to submit selected sequences to another web site, or to other JavaScript code. JSAV is implemented purely in JavaScript making use of the JQuery and JQuery-UI libraries. It does not use any HTML5-specific options to help with browser compatibility. The code is doc
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Arenas-Díaz, Edgar D., Helga Ochoterena, and Katya Rodríguez-Vázquez. "Multiple Sequence Alignment Using a Genetic Algorithm and GLOCSA." Journal of Artificial Evolution and Applications 2009 (August 27, 2009): 1–10. http://dx.doi.org/10.1155/2009/963150.

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Algorithms that minimize putative synapomorphy in an alignment cannot be directly implemented since trivial cases with concatenated sequences would be selected because they would imply a minimum number of events to be explained (e.g., a single insertion/deletion would be required to explain divergence among two sequences). Therefore, indirect measures to approach parsimony need to be implemented. In this paper, we thoroughly present a Global Criterion for Sequence Alignment (GLOCSA) that uses a scoring function to globally rate multiple alignments aiming to produce matrices that minimize the n
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Aadland, Kelsey, and Bryan Kolaczkowski. "Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy." Genome Biology and Evolution 12, no. 9 (2020): 1549–65. http://dx.doi.org/10.1093/gbe/evaa164.

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Abstract Ancestral sequence reconstruction (ASR) uses an alignment of extant protein sequences, a phylogeny describing the history of the protein family and a model of the molecular-evolutionary process to infer the sequences of ancient proteins, allowing researchers to directly investigate the impact of sequence evolution on protein structure and function. Like all statistical inferences, ASR can be sensitive to violations of its underlying assumptions. Previous studies have shown that, whereas phylogenetic uncertainty has only a very weak impact on ASR accuracy, uncertainty in the protein se
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Lakshmi, Naga Jayaprada.Gavarraju, and Karteeka Pavan K. "Pairwise Sequence Alignment by Differential Evolutionary Algorithm with New Mutation Strategy." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 445–53. https://doi.org/10.35940/ijeat.B3136.129219.

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Sequence alignment is a significant facet in the bio-informatics research field for the molecular sequence analysis. Arrangement of two biological sequences by maximizing the similarities between the sequences by incorporating and adjusting gaps is Pairwise Sequence Alignment (PSA). Arrangement of multiple sequences is Multiple Sequence Alignment (MSA). Though Dynamic programming can produce optimal sequence alignment for PSA it suffers from a problem when multiple optimal paths are present and trace back is required. Back tracking becomes complex and it is also not suitable for MSA. So many m
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Blackshields, Gordon, Iain M. Wallace, Mark Larkin, and Desmond G. Higgins. "Analysis and Comparison of Benchmarks for Multiple Sequence Alignment." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 6, no. 4 (2006): 321–39. https://doi.org/10.3233/isb-00245.

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The most popular way of comparing the performance of multiple sequence alignment programs is to use empirical testing on sets of test sequences. Several such test sets now exist, each with potential strengths and weaknesses. We apply several different alignment packages to 6 benchmark datasets, and compare their relative performances. HOMSTRAD, a collection of alignments of homologous proteins, is regularly used as a benchmark for sequence alignment though it is not designed as such, and lacks annotation of reliable regions within the alignment. We introduce this annotation into HOMSTRAD using
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FÜRER, MARTIN, and WEBB MILLER. "ALIGNMENT-TO-ALIGNMENT EDITING WITH “MOVE GAP” OPERATIONS." International Journal of Foundations of Computer Science 07, no. 01 (1996): 23–41. http://dx.doi.org/10.1142/s012905419600004x.

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An alignment of k given sequences is a k-rowed matrix frequently used by molecular biologists to display correspondences between entries from each sequence. Under one approach, an alignment is represented by a matrix of ‘x’ and ’-’ characters, where each x in row r indicates the position of an entry of sequence r. It is sometimes efficient to store only the run-length encoding of each row of this bit-matrix. A natural class of commands for editing one such row into another consists of operations of the form: “Move the d dashes that begin at position i of row r to position j of that row,” for r
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WANG, YI, and KUO-BIN LI. "MULTIPLE SEQUENCE ALIGNMENT USING AN EXHAUSTIVE AND GREEDY ALGORITHM." Journal of Bioinformatics and Computational Biology 03, no. 02 (2005): 243–55. http://dx.doi.org/10.1142/s021972000500103x.

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We describe an exhaustive and greedy algorithm for improving the accuracy of multiple sequence alignment. A simple progressive alignment approach is employed to provide initial alignments. The initial alignment is then iteratively optimized against an objective function. For any working alignment, the optimization involves three operations: insertions, deletions and shuffles of gaps. The optimization is exhaustive since the algorithm applies the above operations to all eligible positions of an alignment. It is also greedy since only the operation that gives the best improving objective score w
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Tu, Shin-Lin, Jeannette Staheli, Colum McClay, Kathleen McLeod, Timothy Rose, and Chris Upton. "Base-By-Base Version 3: New Comparative Tools for Large Virus Genomes." Viruses 10, no. 11 (2018): 637. http://dx.doi.org/10.3390/v10110637.

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Base-By-Base is a comprehensive tool for the creation and editing of multiple sequence alignments that is coded in Java and runs on multiple platforms. It can be used with gene and protein sequences as well as with large viral genomes, which themselves can contain gene annotations. This report describes new features added to Base-By-Base over the last 7 years. The two most significant additions are: (1) The recoding and inclusion of “consensus-degenerate hybrid oligonucleotide primers” (CODEHOP), a popular tool for the design of degenerate primers from a multiple sequence alignment of proteins
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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, startin
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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.
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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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<p>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
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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. 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. 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. Humana Press, 2014.

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DeBlasio, Dan, and John Kececioglu. Parameter Advising for Multiple Sequence Alignment. 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. University of California Press, 2009.

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

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Sharma, Kal Renganathan. Bioinformatics. McGraw-Hill, 2008.

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Nguyen, Ken, Xuan Guo, and Yi Pan. Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Applications. 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. The Author], 2005.

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Perrey, Sören W. Fast approximation to the NP-hard problem of multiple sequence alignment. 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. 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. 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. 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. 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. 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, et al. "Sequence Alignment." In Bioinformatics in Rice Research. 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. 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. 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. 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. 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. 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. 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. 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. 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. Defense Technical Information Center, 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), 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. 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 ef
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Butler, R., T. Butler, I. Foster, et al. Generating alignments of genetic sequences. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6081785.

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Shah, Nameeta, Olivier Couronne, Len A. Pennacchio, et al. Phylo-VISTA: An Interactive Visualization Tool for Multiple DNA Sequence Alignments. Office of Scientific and Technical Information (OSTI), 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), 2012. http://dx.doi.org/10.2172/1047247.

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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), 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), 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, 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 mut
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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, 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
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