Relevant bibliographies by topics / Sequence alignment algorithms

Academic literature on the topic 'Sequence alignment algorithms'

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Published: 14 March 2023

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Journal articles on the topic "Sequence alignment algorithms"

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

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known 3D structure. For proteins, alignment algorithms depend on information about amino acid substitutions that allows for matching sequences that are similar, but not exact. When primary sequence percent identity falls below about 25%, algorithms often fail to identify proteins that may have similar 3D structure. We have created a hydrophobicity scale and a matching dynamic programming algorithm called TMATCH (unpublished report) that is able to match proteins with remote homologs with similar secondary/tertiary structure, even with very low primary sequence matches. In this paper, we describe how we arrived at the hydrophobic scale, how it provides much more information than percent identity matches and some of the implications for better alignments and understanding protein structure.
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Cavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 15, 2020): 1097. http://dx.doi.org/10.12688/f1000research.6348.2.

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known 3D structure. For proteins, alignment algorithms depend on information about amino acid substitutions that allows for matching sequences that are similar, but not exact. When primary sequence percent identity falls below about 25%, algorithms often fail to identify proteins that may have similar 3D structure. We have created a hydrophobicity scale and a matching dynamic programming algorithm called TMATCH (preprint report) that is able to match proteins with remote homologs with similar secondary/tertiary structure, even with very low primary sequence matches. In this paper, we describe how we arrived at the hydrophobic scale, how it provides much more information than percent identity matches and some of the implications for better alignments and understanding protein structure.
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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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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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BACKOFEN, ROLF, and SEBASTIAN WILL. "LOCAL SEQUENCE-STRUCTURE MOTIFS IN RNA." Journal of Bioinformatics and Computational Biology 02, no. 04 (December 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 local alignment with respect to sequence and structure information. While local alignment is superior to global alignment for identifying local similarities, no general local sequence-structure alignment algorithms are currently known. We suggest a new general definition of locality for sequence-structure alignments that is biologically motivated and efficiently tractable. To show the former, we discuss locality of RNA and prove that the defined locality means connectivity by atomic and non-atomic bonds. To show the latter, we present an efficient algorithm for the newly defined pairwise local sequence-structure alignment (lssa) problem for RNA. For molecules of lengthes n and m, the algorithm has worst-case time complexity of O(n2·m2· max (n,m)) and a space complexity of only O(n·m). An implementation of our algorithm is available at . Its runtime is competitive with global sequence-structure alignment.
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Rautiainen, Mikko, Veli Mäkinen, and Tobias Marschall. "Bit-parallel sequence-to-graph alignment." Bioinformatics 35, no. 19 (March 9, 2019): 3599–607. http://dx.doi.org/10.1093/bioinformatics/btz162.

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Abstract Motivation Graphs are commonly used to represent sets of sequences. Either edges or nodes can be labeled by sequences, so that each path in the graph spells a concatenated sequence. Examples include graphs to represent genome assemblies, such as string graphs and de Bruijn graphs, and graphs to represent a pan-genome and hence the genetic variation present in a population. Being able to align sequencing reads to such graphs is a key step for many analyses and its applications include genome assembly, read error correction and variant calling with respect to a variation graph. Results We generalize two linear sequence-to-sequence algorithms to graphs: the Shift-And algorithm for exact matching and Myers’ bitvector algorithm for semi-global alignment. These linear algorithms are both based on processing w sequence characters with a constant number of operations, where w is the word size of the machine (commonly 64), and achieve a speedup of up to w over naive algorithms. For a graph with |V| nodes and |E| edges and a sequence of length m, our bitvector-based graph alignment algorithm reaches a worst case runtime of O(|V|+⌈mw⌉|E| log w) for acyclic graphs and O(|V|+m|E| log w) for arbitrary cyclic graphs. We apply it to five different types of graphs and observe a speedup between 3-fold and 20-fold compared with a previous (asymptotically optimal) alignment algorithm. Availability and implementation https://github.com/maickrau/GraphAligner Supplementary information Supplementary data are available at Bioinformatics online.
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CHIN, FRANCIS Y. L., N. L. HO, T. W. LAM, and PRUDENCE W. H. WONG. "EFFICIENT CONSTRAINED MULTIPLE SEQUENCE ALIGNMENT WITH PERFORMANCE GUARANTEE." Journal of Bioinformatics and Computational Biology 03, no. 01 (February 2005): 1–18. http://dx.doi.org/10.1142/s0219720005000977.

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The constrained multiple sequence alignment problem is to align a set of sequences of maximum length n subject to a given constrained sequence, which arises from some knowledge of the structure of the sequences. This paper presents new algorithms for this problem, which are more efficient in terms of time and space (memory) than the previous algorithms,15 and with a worst-case guarantee on the quality of the alignment. Saving the space requirement by a quadratic factor is particularly significant as the previous O(n4)-space algorithm has limited application due to its huge memory requirement. Experiments on real data sets confirm that our new algorithms show improvements in both alignment quality and resource requirements.
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Zhu, J., J. S. Liu, and C. E. Lawrence. "Bayesian adaptive sequence alignment algorithms." Bioinformatics 14, no. 1 (February 1, 1998): 25–39. http://dx.doi.org/10.1093/bioinformatics/14.1.25.

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NARIMANI, ZAHRA, HAMID BEIGY, and HASSAN ABOLHASSANI. "A NEW GENETIC ALGORITHM FOR MULTIPLE SEQUENCE ALIGNMENT." International Journal of Computational Intelligence and Applications 11, no. 04 (December 2012): 1250023. http://dx.doi.org/10.1142/s146902681250023x.

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Multiple sequence alignment (MSA) is one of the basic and important problems in molecular biology. MSA can be used for different purposes including finding the conserved motifs and structurally important regions in protein sequences and determine evolutionary distance between sequences. Aligning several sequences cannot be done in polynomial time and therefore heuristic methods such as genetic algorithms can be used to find approximate solutions of MSA problems. Several algorithms based on genetic algorithms have been developed for this problem in recent years. Most of these algorithms use very complicated, problem specific and time consuming mutation operators. In this paper, we propose a new algorithm that uses a new way of population initialization and simple mutation and recombination operators. The strength of the proposed GA is using simple mutation operators and also a special recombination operator that does not have problems of similar recombination operators in other GAs. The experimental results show that the proposed algorithm is capable of finding good MSAs in contrast to existing methods, while it uses simple operators with low computational complexity.
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Long, Hai Xia, Li Hua Wu, and Yu Zhang. "Multiple Sequence Alignment Based on Profile Hidden Markov Model and Quantum-Behaved Particle Swarm Optimization with Selection Method." Advanced Materials Research 282-283 (July 2011): 7–12. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.7.

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Multiple sequence alignment (MSA) is an NP-complete and important problem in bioinformatics. Currently, profile hidden Markov model (HMM) is widely used for multiple sequence alignment. In this paper, Quantum-behaved Particle Swarm Optimization with selection operation (SQPSO) is presented, which is used to train profile HMM. Furthermore, an integration algorithm based on the profile HMM and SQPSO for the MSA is constructed. The approach is examined by using multiple nucleotides and protein sequences and compared with other algorithms. The results of the comparisons show that the HMM trained with SQPSO and QPSO yield better alignments than other most commonly used HMM training methods such as Baum–Welch and PSO.
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Dissertations / Theses on the topic "Sequence alignment algorithms"

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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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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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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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Ho, Ngai-lam, and 何毅林. "Algorithms on constrained sequence alignment." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30201949.

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Alimehr, Leila. "The Performance of Sequence Alignment Algorithms." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-200289.

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This thesis deals with sequence alignment algorithms. The sequence alignment is a mutual arrange of two or more sequences in order to study their similarity and dissimilarity. Four decades after the seminal work by Needleman and Wunsch in 1970, these methods still need more explorations. We start out with a review of a sequence alignment, and its generalization to multiple alignments, although the focus of this thesis is on the evaluation of the new alignment algorithms. The research presented here in has stepped into the different algorithms that are in terms of the dynamic programming. In the study of sequence alignment algorithms, two powerful techniques have been invented. According to the simulations, the new algorithms are shown to be extremely efficient for the comparing DNA sequences. All the sequence alignment algorithmsare compared in terms of the distance. We use the programming language R for the implementation and simulation of the algorithms discussed in this thesis.
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Nguyen, Ken D. "Multiple Biolgical Sequence Alignment: Scoring Functions, Algorithms, and Evaluations." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/cs_diss/62.

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Aligning multiple biological sequences such as protein sequences or DNA/RNA sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences' structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes. In this dissertation, we have designed a new scoring method for use in multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. This new technique provides a reliable scoring scheme with low computational complexity. In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our new three multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge-bases and sequence consistency to produce biological meaningful sequence alignments. To improve the speed of the multiple sequence alignment, we have developed a parallel algorithm that can be deployed on reconfigurable computer models. Analytically, our parallel algorithm is the fastest progressive multiple sequence alignment algorithm.
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Jiang, Tianwei. "Sequence alignment : algorithm development and applications /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?ECED%202009%20JIANG.

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Lu, Yue. "Improving the quality of multiple sequence alignment." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3111.

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Isa, Mohammad Nazrin. "High performance reconfigurable architectures for biological sequence alignment." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/7721.

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Bioinformatics and computational biology (BCB) is a rapidly developing multidisciplinary field which encompasses a wide range of domains, including genomic sequence alignments. It is a fundamental tool in molecular biology in searching for homology between sequences. Sequence alignments are currently gaining close attention due to their great impact on the quality aspects of life such as facilitating early disease diagnosis, identifying the characteristics of a newly discovered sequence, and drug engineering. With the vast growth of genomic data, searching for a sequence homology over huge databases (often measured in gigabytes) is unable to produce results within a realistic time, hence the need for acceleration. Since the exponential increase of biological databases as a result of the human genome project (HGP), supercomputers and other parallel architectures such as the special purpose Very Large Scale Integration (VLSI) chip, Graphic Processing Unit (GPUs) and Field Programmable Gate Arrays (FPGAs) have become popular acceleration platforms. Nevertheless, there are always trade-off between area, speed, power, cost, development time and reusability when selecting an acceleration platform. FPGAs generally offer more flexibility, higher performance and lower overheads. However, they suffer from a relatively low level programming model as compared with off-the-shelf microprocessors such as standard microprocessors and GPUs. Due to the aforementioned limitations, the need has arisen for optimized FPGA core implementations which are crucial for this technology to become viable in high performance computing (HPC). This research proposes the use of state-of-the-art reprogrammable system-on-chip technology on FPGAs to accelerate three widely-used sequence alignment algorithms; the Smith-Waterman with affine gap penalty algorithm, the profile hidden Markov model (HMM) algorithm and the Basic Local Alignment Search Tool (BLAST) algorithm. The three novel aspects of this research are firstly that the algorithms are designed and implemented in hardware, with each core achieving the highest performance compared to the state-of-the-art. Secondly, an efficient scheduling strategy based on the double buffering technique is adopted into the hardware architectures. Here, when the alignment matrix computation task is overlapped with the PE configuration in a folded systolic array, the overall throughput of the core is significantly increased. This is due to the bound PE configuration time and the parallel PE configuration approach irrespective of the number of PEs in a systolic array. In addition, the use of only two configuration elements in the PE optimizes hardware resources and enables the scalability of PE systolic arrays without relying on restricted onboard memory resources. Finally, a new performance metric is devised, which facilitates the effective comparison of design performance between different FPGA devices and families. The normalized performance indicator (speed-up per area per process technology) takes out advantages of the area and lithography technology of any FPGA resulting in fairer comparisons. The cores have been designed using Verilog HDL and prototyped on the Alpha Data ADM-XRC-5LX card with the Virtex-5 XC5VLX110-3FF1153 FPGA. The implementation results show that the proposed architectures achieved giga cell updates per second (GCUPS) performances of 26.8, 29.5 and 24.2 respectively for the acceleration of the Smith-Waterman with affine gap penalty algorithm, the profile HMM algorithm and the BLAST algorithm. In terms of speed-up improvements, comparisons were made on performance of the designed cores against their corresponding software and the reported FPGA implementations. In the case of comparison with equivalent software execution, acceleration of the optimal alignment algorithm in hardware yielded an average speed-up of 269x as compared to the SSEARCH 35 software. For the profile HMM-based sequence alignment, the designed core achieved speed-up of 103x and 8.3x against the HMMER 2.0 and the latest version of HMMER (version 3.0) respectively. On the other hand, the implementation of the gapped BLAST with the two-hit method in hardware achieved a greater than tenfold speed-up compared to the latest NCBI BLAST software. In terms of comparison against other reported FPGA implementations, the proposed normalized performance indicator was used to evaluate the designed architectures fairly. The results showed that the first architecture achieved more than 50 percent improvement, while acceleration of the profile HMM sequence alignment in hardware gained a normalized speed-up of 1.34. In the case of the gapped BLAST with the two-hit method, the designed core achieved 11x speed-up after taking out advantages of the Virtex-5 FPGA. In addition, further analysis was conducted in terms of cost and power performances; it was noted that, the core achieved 0.46 MCUPS per dollar spent and 958.1 MCUPS per watt. This shows that FPGAs can be an attractive platform for high performance computation with advantages of smaller area footprint as well as represent economic ‘green’ solution compared to the other acceleration platforms. Higher throughput can be achieved by redeploying the cores on newer, bigger and faster FPGAs with minimal design effort.
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Zhou, Rong. "Memory-efficient graph search applied to multiple sequence alignment." Diss., Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/etd/show.asp?etd=etd-06282005-015428.

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Books on the topic "Sequence alignment algorithms"

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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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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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DeBlasio, Dan, and John Kececioglu. Parameter Advising for Multiple Sequence Alignment. Springer, 2019.

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Parameter Advising for Multiple Sequence Alignment. Springer, 2018.

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Pan, Yi, Xuan Guo, and Ken Nguyen. Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Evaluation. Wiley & Sons, Incorporated, John, 2016.

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Pan, Yi, Xuan Guo, and Ken Nguyen. Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Evaluation. Wiley & Sons, Limited, John, 2016.

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Pan, Yi, Xuan Guo, and Ken Nguyen. Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Evaluation. Wiley & Sons, Limited, John, 2016.

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Pan, Yi, Xuan Guo, and Ken Nguyen. Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Evaluation. Wiley & Sons, Incorporated, John, 2016.

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Book chapters on the topic "Sequence alignment algorithms"

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Higgs, Paul G., and Teresa K. Attwood. "Sequence Alignment Algorithms." In Bioinformatics and Molecular Evolution, 119–38. Malden, MA USA: Blackwell Publishing Ltd., 2013. http://dx.doi.org/10.1002/9781118697078.ch6.

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Xia, Xuhua. "Sequence Alignment Algorithms." In A Mathematical Primer of Molecular Phylogenetics, 15–68. Includes bibliographical references and index.: Apple Academic Press, 2020. http://dx.doi.org/10.1201/9780429425875-2.

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Brown, Daniel G. "A Survey of Seeding for Sequence Alignment." In Bioinformatics Algorithms, 117–42. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470253441.ch6.

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Bafna, Vineet, Eugene L. Lawler, and Pavel A. Pevzner. "Approximation algorithms for multiple sequence alignment." In Combinatorial Pattern Matching, 43–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58094-8_4.

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Botta, Marco, and Guido Negro. "Multiple Sequence Alignment with Genetic Algorithms." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 206–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14571-1_15.

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Reddy, Bharath, and Richard Fields. "Multiple Sequence Alignment Algorithms in Bioinformatics." In Lecture Notes in Networks and Systems, 89–98. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4016-2_9.

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Islam, T. M. Rezwanul, and Ian McQuillan. "CSA-X: Modularized Constrained Multiple Sequence Alignment." In Algorithms for Computational Biology, 143–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58163-7_10.

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Barton, Carl, Costas S. Iliopoulos, Ritu Kundu, Solon P. Pissis, Ahmad Retha, and Fatima Vayani. "Accurate and Efficient Methods to Improve Multiple Circular Sequence Alignment." In Experimental Algorithms, 247–58. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20086-6_19.

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Song, Bin, Feng-feng Zhou, and Guo-liang Chen. "Algorithms for Loosely Constrained Multiple Sequence Alignment." In Computational and Information Science, 213–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30497-5_34.

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Brown, Daniel G., and Alexander K. Hudek. "New Algorithms for Multiple DNA Sequence Alignment." In Lecture Notes in Computer Science, 314–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30219-3_27.

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Conference papers on the topic "Sequence alignment algorithms"

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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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Wise, Michael J. "Alignment algorithms revisited: Alignment algorithms for low similarity protein sequence comparisons." In 2003 European Control Conference (ECC). IEEE, 2003. http://dx.doi.org/10.23919/ecc.2003.7086563.

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Arenas-Díaz, Edgar David, Helga Ochoterena, and Katya Rodríguez-Vázquez. "Multiple sequence alignment using evolutionary algorithms." In the 11th Annual conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1569901.1570159.

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Iyengar, A. K. "Parallel characteristics of sequence alignment algorithms." In the 1989 ACM/IEEE conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/76263.76296.

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"RECONFIGURABLE COMPUTING IP CORES FOR MULTIPLE SEQUENCE ALIGNMENT." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003167402160221.

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"IMPROVEMENTS TO A MULTIPLE PROTEIN SEQUENCE ALIGNMENT TOOL." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003789202260233.

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Kaur, Harleen, and Lal Chand. "Biological sequence alignment using varied optimization algorithms." In 2016 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2016. http://dx.doi.org/10.1109/inventive.2016.7823293.

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Naznin, Farhana, Ruhul Sarker, Daryl Essam, Tuan Pham, and Xiaobo Zhou. "Two Hybrid Algorithms for Multiple Sequence Alignment." In 2009 INTERNATIONAL CONFERNECE ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-09). AIP, 2010. http://dx.doi.org/10.1063/1.3314271.

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Wang, Chih-Li, Qian Zhong, Szu-Ying Wang, and Vwani Roychowdhury. "Cover song identification by sequence alignment algorithms." In 2011 International Conference on Graphic and Image Processing. SPIE, 2011. http://dx.doi.org/10.1117/12.913429.

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Bucak, I. O., and V. Uslan. "An analysis of sequence alignment: Heuristic algorithms." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5626428.

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Reports on the topic "Sequence alignment algorithms"

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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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Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.

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Abstract:
Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.
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