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Relevant bibliographies by topics / Sequence alignment (Bioinformatics) Genetic algorithms

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Academic literature on the topic 'Sequence alignment (Bioinformatics) Genetic algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "Sequence alignment (Bioinformatics) Genetic algorithms"

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Rautiainen, Mikko, Veli Mäkinen, and Tobias Marschall. "Bit-parallel sequence-to-graph alignment." Bioinformatics 35, no. 19 (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

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Gupta, Ruchi, and Pankaj Agarwal. "SOGA: space oriented genetic algorithm for multiple sequence alignment." International Journal of Engineering & Technology 7, no. 4.5 (2018): 481. http://dx.doi.org/10.14419/ijet.v7i4.5.21138.

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Multiple sequence alignment is one of the recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although several applicable techniques were observed in this re- search, from traditional method such as dynamic programming to the extent of widely used stochastic optimization method such as Simu- lated Annealing and motif finding for solving this problem, their use is limited by the computing demands which are necessary for ex- ploring such a large and complex search space. This paper presen

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Zhang, Ching, and Andrew K. C. Wong. "A genetic algorithm for multiple molecular sequence alignment." Bioinformatics 13, no. 6 (1997): 565–81. http://dx.doi.org/10.1093/bioinformatics/13.6.565.

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Silva, Fernando José Mateus da, Juan Manuel Sánchez Pérez, Juan Antonio Gómez Pulido, and Miguel A. Vega Rodríguez. "Parallel Niche Pareto AlineaGA – an Evolutionary Multiobjective approach on Multiple Sequence Alignment." Journal of Integrative Bioinformatics 8, no. 3 (2011): 57–72. http://dx.doi.org/10.1515/jib-2011-174.

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Summary Multiple sequence alignment is one of the most recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although exact methods exist for solving this problem, their use is limited by the computing demands which are necessary for exploring such a large and complex search space. Genetic Algorithms are adaptive search methods which perform well in large and complex spaces. Parallel Genetic Algorithms, not only increase the speed up of the search, but also improve its efficiency, present

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Rehman, Hafiz Asadul, Kashif Zafar, Ayesha Khan, and Abdullah Imtiaz. "Multiple sequence alignment using enhanced bird swarm align algorithm." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 1097–114. http://dx.doi.org/10.3233/jifs-210055.

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Discovering structural, functional and evolutionary information in biological sequences have been considered as a core research area in Bioinformatics. Multiple Sequence Alignment (MSA) tries to align all sequences in a given query set to provide us ease in annotation of new sequences. Traditional methods to find the optimal alignment are computationally expensive in real time. This research presents an enhanced version of Bird Swarm Algorithm (BSA), based on bio inspired optimization. Enhanced Bird Swarm Align Algorithm (EBSAA) is proposed for multiple sequence alignment problem to determine

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Chang, Xian, Jordan Eizenga, Adam M. Novak, Jouni Sirén, and Benedict Paten. "Distance indexing and seed clustering in sequence graphs." Bioinformatics 36, Supplement_1 (2020): i146—i153. http://dx.doi.org/10.1093/bioinformatics/btaa446.

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Abstract Motivation Graph representations of genomes are capable of expressing more genetic variation and can therefore better represent a population than standard linear genomes. However, due to the greater complexity of genome graphs relative to linear genomes, some functions that are trivial on linear genomes become much more difficult in genome graphs. Calculating distance is one such function that is simple in a linear genome but complicated in a graph context. In read mapping algorithms such distance calculations are fundamental to determining if seed alignments could belong to the same

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Jayakumar, Jayapriya, and Michael Arock. "Cellular Automata-Based PSO Algorithm for Aligning Multiple Molecular Sequences." International Journal of Applied Evolutionary Computation 7, no. 1 (2016): 1–15. http://dx.doi.org/10.4018/ijaec.2016010101.

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In Bioinformatics, sequence analysis is the basic important concept that provides information for structural and functional analysis. Multiple Sequence Alignment (MSA) is a keystone problem in the sequence analysis that is used for constructing phylogenetic tree, finding motif, gene expression, etc. Basically, all biological computation issues are NP-complete problems. In this paper, a novel approach using Cellular Automata (CA) and Particle Swarm Optimization (PSO) techniques are proposed for MSA problem. Both of these techniques handle NP-complete problems very skillfully. For experimental a

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Saha, Subrata, Jethro Johnson, Soumitra Pal, George M. Weinstock, and Sanguthevar Rajasekaran. "MSC: a metagenomic sequence classification algorithm." Bioinformatics 35, no. 17 (2019): 2932–40. http://dx.doi.org/10.1093/bioinformatics/bty1071.

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Abstract Motivation Metagenomics is the study of genetic materials directly sampled from natural habitats. It has the potential to reveal previously hidden diversity of microscopic life largely due to the existence of highly parallel and low-cost next-generation sequencing technology. Conventional approaches align metagenomic reads onto known reference genomes to identify microbes in the sample. Since such a collection of reference genomes is very large, the approach often needs high-end computing machines with large memory which is not often available to researchers. Alternative approaches fo

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Q. Abedulridha, Sara, and Eman S. Al-Shamery. "Optimal Pair DNA Sequence Alignment based on Matching Regions and Multi-Zone Genetic Algorithm." International Journal of Engineering & Technology 7, no. 4.19 (2018): 751. http://dx.doi.org/10.14419/ijet.v7i4.19.27993.

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DNA sequence alignment is an important and challenging task in Bioinformatics, which is used for finding the optimal arrangement between two sequences. In this paper, two methods are proposed in two stages to solve the pairwise sequence alignment problem. The first method is Matching Regions(MR) concerns on splitting the DNA into regions with adaptive interleaving windows to isolate the DNA tape into matched and non-matched regions. Additionally, a Multi-Zone Genetic Algorithm (MZGA) is proposed as an improved method in the second stage. It consists of segmenting a large non-matched region int

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Holmes, J. Bradley, Eric Moyer, Lon Phan, Donna Maglott, and Brandi Kattman. "SPDI: data model for variants and applications at NCBI." Bioinformatics 36, no. 6 (2019): 1902–7. http://dx.doi.org/10.1093/bioinformatics/btz856.

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Abstract Motivation Normalizing sequence variants on a reference, projecting them across congruent sequences and aggregating their diverse representations are critical to the elucidation of the genetic basis of disease and biological function. Inconsistent representation of variants among variant callers, local databases and tools result in discrepancies that complicate analysis. NCBI’s genetic variation resources, dbSNP and ClinVar, require a robust, scalable set of principles to manage asserted sequence variants. Results The SPDI data model defines variants as a sequence of four attributes:

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Dissertations / Theses on the topic "Sequence alignment (Bioinformatics) Genetic algorithms"

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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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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 dat

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Helal, Manal Computer Science &amp Engineering Faculty of Engineering UNSW. "Indexing and partitioning schemes for distributed tensor computing with application to multiple sequence alignment." Awarded by:University of New South Wales. Computer Science & Engineering, 2009. http://handle.unsw.edu.au/1959.4/44781.

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This thesis investigates indexing and partitioning schemes for high dimensional scientific computational problems. Building on the foundation offered by Mathematics of Arrays (MoA) for tensor-based computation, the ultimate contribution of the thesis is a unified partitioning scheme that works invariant of the dataset dimension and shape. Consequently, portability is ensured between different high performance machines, cluster architectures, and potentially computational grids. The Multiple Sequence Alignment (MSA) problem in computational biology has an optimal dynamic programming based solut

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Darbha, Sriram. "RNA Homology Searches Using Pair Seeding." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/1172.

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Due to increasing numbers of non-coding RNA (ncRNA) being discovered recently, there is interest in identifying homologs of a given structured RNA sequence. Exhaustive homology searching for structured RNA molecules using covariance models is infeasible on genome-length sequences. Hence, heuristic methods are employed, but they largely ignore structural information in the query. We present a novel method, which uses secondary structure information, to perform homology searches for a structured RNA molecule. We define the concept of a <em>pair seed</em> and theoretically model alignment

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Eaves, Hugh L. "Evaluating and Improving the Efficiency of Software and Algorithms for Sequence Data Analysis." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4295.

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With the ever-growing size of sequence data sets, data processing and analysis are an increasingly large portion of the time and money spent on nucleic acid sequencing projects. Correspondingly, the performance of the software and algorithms used to perform that analysis has a direct effect on the time and expense involved. Although the analytical methods are widely varied, certain types of software and algorithms are applicable to a number of areas. Targeting improvements to these common elements has the potential for wide reaching rewards. This dissertation research consisted of several

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Yin, Zhaoming. "Enhance the understanding of whole-genome evolution by designing, accelerating and parallelizing phylogenetic algorithms." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51875.

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The advent of new technology enhance the speed and reduce the cost for sequencing biological data. Making biological sense of this genomic data is a big challenge to the algorithm design as well as the high performance computing society. There are many problems in Bioinformatics, such as how new functional genes arise, why genes are organized into chromosomes, how species are connected through the evolutionary tree of life, or why arrangements are subject to change. Phylogenetic analyses have become essential to research on the evolutionary tree of life. It can help us to track the history of

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Zafalon, Geraldo Francisco Donegá. "Aplicação de estratégias híbridas em algoritmos de alinhamento múltiplo de sequências para ambientes de computação paralela e distribuída." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-28082015-120515/.

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A Bioinformática tem se desenvolvido de forma intensa nos últimos anos. A necessidade de se processar os grandes conjuntos de sequências, sejam de nucleotídeos ou de aminoácidos, tem estimulado o desenvolvimento de diversas técnicas algorítmicas, de modo a tratar este problema de maneira factível. Os algoritmos de alinhamento de alinhamento múltiplo de sequências assumiram um papel primordial, tornando a execução de alinhamentos de conjuntos com mais de duas sequencias uma tarefa viável computacionalmente. No entanto, com o aumento vertiginoso tanto da quantidade de sequencias em um determinad

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Ogata, Sabrina Oliveira. "Alinhamento de seqüências biológicas com o uso de algoritmos genéticos." Universidade Federal de São Carlos, 2005. https://repositorio.ufscar.br/handle/ufscar/5528.

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Made available in DSpace on 2016-06-02T20:21:33Z (GMT). No. of bitstreams: 1 DissSOO.pdf: 370558 bytes, checksum: eaf0bc7ee24eeffcd23041e4273aa013 (MD5) Previous issue date: 2005-03-14<br>Universidade Federal de Sao Carlos<br>The comparison of genome sequences from different organisms is one of the computational application most frequently used by molecular biologists. This operation serves as support for other processes as, for instance, the determination of the three- dimensional structure of the proteins. During the last years, a significative increase has been observed in the use of Gene

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Books on the topic "Sequence alignment (Bioinformatics) Genetic algorithms"

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Gautham, N. Bioinformatics: Databases and algorithms. Alpha Science International Ltd, 2006.

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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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Gautham, N. Bioinformatics: Databases and Algorithms. Alpha Science Intl Ltd, 2006.

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Book chapters on the topic "Sequence alignment (Bioinformatics) Genetic algorithms"

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

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Moss, Jonathan, and Colin G. Johnson. "An ant colony algorithm for multiple sequence alignment in bioinformatics." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 2003. http://dx.doi.org/10.1007/978-3-7091-0646-4_33.

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

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

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Anbarasu, L. A., P. Narayanasamy, and V. Sundararajan. "Multiple Sequence Alignment Using Parallel Genetic Algorithms." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48873-1_18.

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Seijas, Juan, Carmen Morató, Diego Andina, and Antonio Vega-Corona. "Improving the Efficiency of Multiple Sequence Alignment by Genetic Algorithms." In Artificial Neural Nets Problem Solving Methods. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44869-1_46.

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Ergezer, Halit, and Kemal Leblebicioğlu. "Refining the Progressive Multiple Sequence Alignment Score Using Genetic Algorithms." In Artificial Intelligence and Neural Networks. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11803089_21.

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Notredame, Cédric. "Using Genetic Algorithms for Pairwise and Multiple Sequence Alignments." In Evolutionary Computation in Bioinformatics. Elsevier, 2003. http://dx.doi.org/10.1016/b978-155860797-2/50007-x.

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Rocha, Miguel, and Pedro G. Ferreira. "Pairwise Sequence Alignment." In Bioinformatics Algorithms. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-812520-5.00006-7.

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Rocha, Miguel, and Pedro G. Ferreira. "Multiple Sequence Alignment." In Bioinformatics Algorithms. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-812520-5.00008-0.

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Conference papers on the topic "Sequence alignment (Bioinformatics) Genetic algorithms"

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Naznin, Farhana, Ruhul Sarker, and Daryl Essam. "DGA: Decomposition with genetic algorithm for multiple sequence alignment." In 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2010. http://dx.doi.org/10.1109/cibcb.2010.5510595.

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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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Thapar, Vishal, and Sanguthevar Rajasekaran. "Sampling Based Meta-algorithms for Accurate Multiple Sequence Alignment." In 2008 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 2008. http://dx.doi.org/10.1109/bibm.2008.51.

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Chen, Yang, Jinglu Hu, Kotaro Hirasawa, and Songnian Yu. "Multiple Sequence Alignment Based on Genetic Algorithms with Reserve Selection." In 2008 IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2008. http://dx.doi.org/10.1109/icnsc.2008.4525460.

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Williams, Alex C., Hyrum D. Carroll, John F. Wallin, et al. "Identification of Ancient Greek Papyrus Fragments Using Genetic Sequence Alignment Algorithms." In 2014 IEEE 10th International Conference on e-Science (e-Science). IEEE, 2014. http://dx.doi.org/10.1109/escience.2014.14.

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Ben Othman, Mohamed Tahar, and Gamil Abdel-Azim. "Multiple sequence alignment based on genetic algorithms with new chromosomes representation." In MELECON 2012 - 2012 16th IEEE Mediterranean Electrotechnical Conference. IEEE, 2012. http://dx.doi.org/10.1109/melcon.2012.6196603.

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"APPLYING CONCEPTUAL MODELING TO ALIGNMENT TOOLS ONE STEP TOWARDS THE AUTOMATION OF DNA SEQUENCE ANALYSIS." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003142001370142.

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Xiong, Kaiqi, Sang C. Suh, Jack Y. Yang, Mary Qu Yang, and Hamid Arabnia. "Next Generation Sequence Analysis Using Genetic Algorithms on Multi-core Technology." In 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing. IEEE, 2009. http://dx.doi.org/10.1109/ijcbs.2009.104.

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"MASon: MILLION ALIGNMENTS IN SECONDS - A Platform Independent Pairwise Sequence Alignment Library for Next Generation Sequencing Data." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003775701950201.

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