Relevant bibliographies by topics / Sequence alignment Sequence analysis

Contents

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

Academic literature on the topic 'Sequence alignment Sequence analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Sequence alignment Sequence analysis"

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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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Ji, Guo Li, Jing Ci Yao, Zi Jiang Yang, and Cong Ting Ye. "LemK_MSA: A Multiple Sequence Alignment Method with Sequence Vectorization Based on Lempel-Ziv." Applied Mechanics and Materials 284-287 (January 2013): 3203–7. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3203.

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In this paper, we propose a method for multiple sequence alignment, LemK_MSA, which integrates Lempel-Ziv based sequence vectorization and k-means clustering analysis. LemK_MSA converts multiple sequence alignment into corresponding 10-dimensional vector alignment by 10 types of copy modes. Then it uses k-means algorithm and NJ algorithm to divide the sequences into several groups and calculate guide tree of each part with the vectors of the sequences. A complete guide tree for multiple sequence alignment could be constructed by merging guide tree of every group. Thus, the time efficiency of p
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Barton, Geoffrey J. "Protein Sequence Alignment Techniques." Acta Crystallographica Section D Biological Crystallography 54, no. 6 (1998): 1139–46. http://dx.doi.org/10.1107/s0907444998008324.

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The basic algorithms for alignment of two or more protein sequences are explained. Alternative methods for scoring substitutions and gaps (insertions and deletions) are described, as are global and local alignment methods. Multiple alignment techniques are explained, including methods for profile comparison. A summary is given of programs for the alignment and analysis of protein sequences, either from sequence alone, or from three-dimensional structure.
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Wilson, W. C. "Activity Pattern Analysis by Means of Sequence-Alignment Methods." Environment and Planning A: Economy and Space 30, no. 6 (1998): 1017–38. http://dx.doi.org/10.1068/a301017.

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The author describes a method of comparing sequences of characters, called sequence alignment or string matching, and illustrates its use in the analysis of daily activity patterns derived from time-use diaries. It allows definition of measures of similarity or distance between complete sequences, called global alignment, or the evaluation of the best fit of short sequences within long sequences, called local alignment. Alignments may be done pairwise to develop similarity or distance matrices that describe the relatedness of individuals in the set of sequences being examined. Pairwise alignme
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Ren, Jie, Xin Bai, Yang Young Lu, et al. "Alignment-Free Sequence Analysis and Applications." Annual Review of Biomedical Data Science 1, no. 1 (2018): 93–114. http://dx.doi.org/10.1146/annurev-biodatasci-080917-013431.

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Genome and metagenome comparisons based on large amounts of next-generation sequencing (NGS) data pose significant challenges for alignment-based approaches due to the huge data size and the relatively short length of the reads. Alignment-free approaches based on the counts of word patterns in NGS data do not depend on the complete genome and are generally computationally efficient. Thus, they contribute significantly to genome and metagenome comparison. Recently, novel statistical approaches have been developed for the comparison of both long and shotgun sequences. These approaches have been
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Cook, Jonathan P., and Malcolm A. McCrae. "Sequence analysis of the guanylyltransferase (VP3) of group A rotaviruses." Journal of General Virology 85, no. 4 (2004): 929–32. http://dx.doi.org/10.1099/vir.0.19629-0.

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The RNA segment encoding the guanylyltransferase (VP3) from 12 group A rotavirus isolates has been sequenced following RT-PCR and molecular cloning of the full-length amplicons produced. Alignment of the derived amino acid sequences including those of the four VP3 sequences available from GenBank revealed two levels of sequence divergence. Virus isolates from humans showed greater than 94 % sequence identity, whereas those isolated from different mammalian species showed as low as 79 % sequence identity. The exceptions were avian virus isolates, which diverged ∼45 % from those of mammalian ori
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Asare, James Owusu, Justice Kwame Appati, and Kwaku Darkwah. "Formulation and Analysis of Patterns in a Score Matrix for Global Sequence Alignment." International Journal of Mathematics and Mathematical Sciences 2020 (June 1, 2020): 1–9. http://dx.doi.org/10.1155/2020/3858057.

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Global sequence alignment is one of the most basic pairwise sequence alignment procedures used in molecular biology to understand the similarity that arises among the structure, function, or evolutionary relationship between two nucleotide sequences. The general algorithm associated with global sequence alignment is the dynamic programming algorithm of Needleman and Wunsch. In this paper, patterns are exploited in the score matrix of the Needleman–Wunsch algorithm. With the help of some examples, the general patterns realized are formulated as new a priori propositions and corollaries that are
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ESKIN, ELEAZAR, and SAGI SNIR. "INCORPORATING HOMOLOGUES INTO SEQUENCE EMBEDDINGS FOR PROTEIN ANALYSIS." Journal of Bioinformatics and Computational Biology 05, no. 03 (2007): 717–38. http://dx.doi.org/10.1142/s0219720007002734.

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Statistical and learning techniques are becoming increasingly popular for different tasks in bioinformatics. Many of the most powerful statistical and learning techniques are applicable to points in a Euclidean space but not directly applicable to discrete sequences such as protein sequences. One way to apply these techniques to protein sequences is to embed the sequences into a Euclidean space and then apply these techniques to the embedded points. In this work we introduce a biologically motivated sequence embedding, the homology kernel, which takes into account intuitions from local alignme
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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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Lebsir, Rabah, Abdesslem Layeb, and Tahi Fariza. "A Greedy Clustering Algorithm for Multiple Sequence Alignment." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–17. http://dx.doi.org/10.4018/ijcini.20211001.oa41.

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This paper presents a strategy to tackle the Multiple Sequence Alignment (MSA) problem, which is one of the most important tasks in the biological sequence analysis. Its role is to align the sequences in their entirety to derive relationships and common characteristics between a set of protein or nucleotide sequences. The MSA problem was proved to be an NP-Hard problem. The proposed strategy incorporates a new idea based on the well-known divide and conquer paradigm. This paper presents a novel method of clustering sequences as a preliminary step to improve the final alignment; this decomposit
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Dissertations / Theses on the topic "Sequence alignment Sequence analysis"

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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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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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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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Arner, Erik. "Solving repeat problems in shotgun sequencing /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-996-3/.

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Rausch, Tobias [Verfasser]. "Dissecting multiple sequence alignment methods : the analysis, design and development of generic multiple sequence alignment components in SeqAn / Tobias Rausch." Berlin : Freie Universität Berlin, 2010. http://d-nb.info/1024541460/34.

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Kim, Eagu. "Inverse Parametric Alignment for Accurate Biological Sequence Comparison." Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/193664.

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For as long as biologists have been computing alignments of sequences, the question of what values to use for scoring substitutions and gaps has persisted. In practice, substitution scores are usually chosen by convention, and gap penalties are often found by trial and error. In contrast, a rigorous way to determine parameter values that are appropriate for aligning biological sequences is by solving the problem of Inverse Parametric Sequence Alignment. Given examples of biologically correct reference alignments, this is the problem of finding parameter values that make the examples score as
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Maydt, Jochen. "Analysis of recombination in molecular sequence data." Aachen Shaker, 2008. http://d-nb.info/993318045/04.

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Herman, Joseph L. "Multiple sequence analysis in the presence of alignment uncertainty." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:88a56d9f-a96e-48e3-b8dc-a73f3efc8472.

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Sequence alignment is one of the most intensely studied problems in bioinformatics, and is an important step in a wide range of analyses. An issue that has gained much attention in recent years is the fact that downstream analyses are often highly sensitive to the specific choice of alignment. One way to address this is to jointly sample alignments along with other parameters of interest. In order to extend the range of applicability of this approach, the first chapter of this thesis introduces a probabilistic evolutionary model for protein structures on a phylogenetic tree; since protein stru
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Bell, Lachlan Hamilton. "Sequence analysis of enzymes of the shikimate pathway : development of a novel multiple sequence alignment algorithm." Thesis, University of Glasgow, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307193.

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Hossain, A. S. Md Mukarram. "Scalable tools for high-throughput viral sequence analysis." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/276228.

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Viral sequence data are increasingly being used to estimate evolutionary and epidemiological parameters to understand the dynamics of viral diseases. This thesis focuses on developing novel and improved computational methods for high-throughput analysis of large viral sequence datasets. I have developed a novel computational pipeline, Pipelign, to detect potentially unrelated sequences from groups of viral sequences during sequence alignment. Pipelign detected a large number of unrelated and mis-annotated sequences from several viral sequence datasets collected from GenBank. I subsequently dev
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Books on the topic "Sequence alignment Sequence analysis"

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Larson, Stefan M. A comprehensive sequence alignment analysis and structure comparison of the SH3 domain family. National Library of Canada, 1999.

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Sheng wu ji suan: Sheng wu xu lie de fen xi fang fa yu ying yong. Ke xue chu ban she, 2010.

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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 James. Multiple sequence alignment methods. Humana Press, 2014.

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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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Gribskov, Michael, and John Devereux, eds. Sequence Analysis Primer. Palgrave Macmillan UK, 1991. http://dx.doi.org/10.1007/978-1-349-21355-9.

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Bioinformatics: Sequence alignment and Markov models. McGraw-Hill, 2009.

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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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Swindell, Simon R., ed. Sequence Data Analysis Guidebook. Humana Press, 1997. http://dx.doi.org/10.1385/0896033589.

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Delahaye, Jean-Paul. Sequence transformations. Springer-Verlag, 1988.

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Book chapters on the topic "Sequence alignment Sequence analysis"

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Yap, Tieng K., Ophir Frieder, and Robert L. Martino. "Multiprocessor Sequence Alignment." In High Performance Computational Methods for Biological Sequence Analysis. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1391-5_5.

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Schuler, Gregory D. "Sequence Alignment and Database Searching." In Methods of Biochemical Analysis. John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470110607.ch7.

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Schuler, Gregory D. "Sequence Alignment and Database Searching." In Methods of Biochemical Analysis. John Wiley & Sons, Inc., 2002. http://dx.doi.org/10.1002/0471223921.ch8.

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Thompson, Steven M. "Multiple Sequence Alignment and Analysis." In Introduction to Bioinformatics. Humana Press, 2003. http://dx.doi.org/10.1007/978-1-59259-335-4_33.

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Barton, Geoffrey J. "The AMPS Package for Multiple Protein Sequence Alignment." In Computer Analysis of Sequence Data. Humana Press, 1994. http://dx.doi.org/10.1385/0-89603-276-0:327.

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Barton, Geoffrey J., Robert B. Russell, and Craig D. Livingstone. "Prediction of Protein Structure from Multiple Sequence Alignment." In Methods in Protein Sequence Analysis. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-1603-7_27.

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Baxevanis, Andreas D. "Practical Aspects of Multiple Sequence Alignment." In Methods of Biochemical Analysis. John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470110607.ch8.

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Higgins, Desmond G. "Clustal V: Multiple Alignment of DNA and Protein Sequences." In Computer Analysis of Sequence Data. Humana Press, 1994. http://dx.doi.org/10.1385/0-89603-276-0:307.

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Tchoukalov, Tzvetan, Christian Monson, and Brian Roark. "Morphological Analysis by Multiple Sequence Alignment." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15754-7_80.

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Yu, Fuli, and Cristian Coarfa. "Sequence Alignment, Analysis, and Bioinformatic Pipelines." In Next Generation Sequencing. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7001-4_4.

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Conference papers on the topic "Sequence alignment Sequence analysis"

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Sanchez, Friman, Esther Salami, Alex Ramirez, and Mateo Valero. "Performance Analysis of Sequence Alignment Applications." In 2006 IEEE International Symposium on Workload Characterization. IEEE, 2006. http://dx.doi.org/10.1109/iiswc.2006.302729.

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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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Molina, Valentin, Gerardo Ceballos, and Hermann Davila. "ECG signal analysis using temporary dynamic sequence alignment." In 2013 XVIII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA). IEEE, 2013. http://dx.doi.org/10.1109/stsiva.2013.6644910.

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Raue, Federico, Wonmin Byeon, Thomas M. Breuel, and Marcus Liwicki. "Parallel sequence classification using recurrent neural networks and alignment." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333828.

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Zou, Quan, Mao-Zu Guo, Yang Liu, and Zhi-An Xing. "A Novel Comparative Sequence Analysis Method for ncRNA Secondary Structure Prediction without Multiple Sequence Alignment." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.446.

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Sánchez Castaño, Friman, Alex Ramirez, and Mateo Valero. "Quantitative analysis of sequence alignment applications on multiprocessor architectures." In the 6th ACM conference. ACM Press, 2009. http://dx.doi.org/10.1145/1531743.1531755.

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Huang, Conrad C., Walter R. Novak, Patricia C. Babbitt, Andrew I. Jewett, Thomas E. Ferrin, and Teri E. Klein. "Integrated Tools for Structural and Sequence Alignment and Analysis." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 1999. http://dx.doi.org/10.1142/9789814447331_0022.

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Schambach, Marc-Peter, and Sheikh Faisal Rashid. "Stabilize Sequence Learning with Recurrent Neural Networks by Forced Alignment." In 2013 12th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2013. http://dx.doi.org/10.1109/icdar.2013.257.

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Chaabane, Lamiche. "Resolving Multiple Sequence Alignment Problem Using an Intelligent Cooperative Algorithm." In 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS). IEEE, 2018. http://dx.doi.org/10.1109/pais.2018.8598481.

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Selvitopi, Oguz, Saliya Ekanayake, Giulia Guidi, Georgios A. Pavlopoulos, Ariful Azad, and Aydin Buluc. "Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices." In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2020. http://dx.doi.org/10.1109/sc41405.2020.00079.

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Reports on the topic "Sequence alignment Sequence analysis"

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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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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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Lefebvre, Robert Alexander, Brandon R. Langley, and Jordan P. Lefebvre. Workbench Analysis Sequence Processor. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1524895.

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Fox, George E., and Timmothy Haselman. Comparative Sequence Analysis of Structural RNA. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada232340.

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States, David J. Analysis and Annotation of Nucleic Acid Sequence. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/899629.

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David J. States. Analysis and Annotation of Nucleic Acid Sequence. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/826380.

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Shavlik, J. W. Applying machine learning techniques to DNA sequence analysis. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/5688406.

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Swain, A. D. Accident Sequence Evaluation Program: Human reliability analysis procedure. Office of Scientific and Technical Information (OSTI), 1987. http://dx.doi.org/10.2172/6370593.

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Shavlik, J. W., and M. O. Noordewier. Applying machine learning techniques to DNA sequence analysis. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7023074.

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Uberbacher, E. C., Y. Xu, M. B. Shah, V. Olman, M. Parang, and R. Mural. An editing environment for DNA sequence analysis and annotation. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/563243.

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