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Relevant bibliographies by topics / Homology search

Academic literature on the topic 'Homology search'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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Journal articles on the topic "Homology search"

1

Alam, I., A. Dress, M. Rehmsmeier, and G. Fuellen. "Comparative homology agreement search: An effective combination of homology-search methods." Proceedings of the National Academy of Sciences 101, no. 38 (September 14, 2004): 13814–19. http://dx.doi.org/10.1073/pnas.0405612101.

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2

Cui, Xuefeng, Tomáš Vinař, Broňa Brejová, Dennis Shasha, and Ming Li. "Homology search for genes." Bioinformatics 23, no. 13 (July 1, 2007): i97—i103. http://dx.doi.org/10.1093/bioinformatics/btm225.

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Hollich, V., and E. L. L. Sonnhammer. "PfamAlyzer: domain-centric homology search." Bioinformatics 23, no. 24 (October 31, 2007): 3382–83. http://dx.doi.org/10.1093/bioinformatics/btm521.

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Mak, D., Y. Gelfand, and G. Benson. "Indel seeds for homology search." Bioinformatics 22, no. 14 (July 15, 2006): e341-e349. http://dx.doi.org/10.1093/bioinformatics/btl263.

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Hung, Jui-Hung, and Zhiping Weng. "Sequence Alignment and Homology Search." Cold Spring Harbor Protocols 2016, no. 11 (August 29, 2016): pdb.top093070. http://dx.doi.org/10.1101/pdb.top093070.

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Tong, Jing, Ruslan I. Sadreyev, Jimin Pei, Lisa N. Kinch, and Nick V. Grishin. "Using homology relations within a database markedly boosts protein sequence similarity search." Proceedings of the National Academy of Sciences 112, no. 22 (May 18, 2015): 7003–8. http://dx.doi.org/10.1073/pnas.1424324112.

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Inference of homology from protein sequences provides an essential tool for analyzing protein structure, function, and evolution. Current sequence-based homology search methods are still unable to detect many similarities evident from protein spatial structures. In computer science a search engine can be improved by considering networks of known relationships within the search database. Here, we apply this idea to protein-sequence–based homology search and show that it dramatically enhances the search accuracy. Our new method, COMPADRE (COmparison of Multiple Protein sequence Alignments using Database RElationships) assesses the relationship between the query sequence and a hit in the database by considering the similarity between the query and hit’s known homologs. This approach increases detection quality, boosting the precision rate from 18% to 83% at half-coverage of all database homologs. The increased precision rate allows detection of a large fraction of protein structural relationships, thus providing structure and function predictions for previously uncharacterized proteins. Our results suggest that this general approach is applicable to a wide variety of methods for detection of biological similarities. The web server is available at prodata.swmed.edu/compadre.

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Sybenga, J. "Homologous chromosome pairing in meiosis of higher eukaryotes—still an enigma?" Genome 63, no. 10 (October 2020): 469–82. http://dx.doi.org/10.1139/gen-2019-0154.

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Meiosis is the basis of the generative reproduction of eukaryotes. The crucial first step is homologous chromosome pairing. In higher eukaryotes, micrometer-scale chromosomes, micrometer distances apart, are brought together by nanometer DNA sequences, at least a factor of 1000 size difference. Models of homology search, homologue movement, and pairing at the DNA level in higher eukaryotes are primarily based on studies with yeast where the emphasis is on the induction and repair of DNA double-strand breaks (DSB). For such a model, the very large nuclei of most plants and animals present serious problems. Homology search without DSBs cannot be explained by models based on DSB repair. The movement of homologues to meet each other and make contact at the molecular level is not understood. These problems are discussed and the conclusion is that at present practically nothing is known of meiotic homologue pairing in higher eukaryotes up to the formation of the synaptonemal complex, and that new, necessarily speculative models must be developed. Arguments are given that RNA plays a central role in homology search and a tentative model involving RNA in homology search is presented. A role of actin in homologue movement is proposed. The primary role of DSBs in higher eukaryotes is concluded to not be in pairing but in the preparation of Holliday junctions, ultimately leading to chromatid exchange.

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Somervuo, Panu, and Liisa Holm. "SANSparallel: interactive homology search against Uniprot." Nucleic Acids Research 43, W1 (April 8, 2015): W24—W29. http://dx.doi.org/10.1093/nar/gkv317.

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9

Ilie, L., and S. Ilie. "Multiple spaced seeds for homology search." Bioinformatics 23, no. 22 (September 5, 2007): 2969–77. http://dx.doi.org/10.1093/bioinformatics/btm422.

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Mukherjee, Sujoy, Józef H. Przytycki, Marithania Silvero, Xiao Wang, and Seung Yeop Yang. "Search for Torsion in Khovanov Homology." Experimental Mathematics 27, no. 4 (May 11, 2017): 488–97. http://dx.doi.org/10.1080/10586458.2017.1320242.

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Dissertations / Theses on the topic "Homology search"

1

Syed, Intikhab Alam Syed Intikhab Alam Syed Intikhab Alam. "Integrative approaches to protein homology search." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975814540.

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Alam, Intikhab. "Integrative approaches to protein homology search." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975814540.

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3

Cameron, Michael, and mcam@mc-mc net. "Efficient Homology Search for Genomic Sequence Databases." RMIT University. Computer Science and Information Technology, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070509.162443.

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Genomic search tools can provide valuable insights into the chemical structure, evolutionary origin and biochemical function of genetic material. A homology search algorithm compares a protein or nucleotide query sequence to each entry in a large sequence database and reports alignments with highly similar sequences. The exponential growth of public data banks such as GenBank has necessitated the development of fast, heuristic approaches to homology search. The versatile and popular blast algorithm, developed by researchers at the US National Center for Biotechnology Information (NCBI), uses a four-stage heuristic approach to efficiently search large collections for analogous sequences while retaining a high degree of accuracy. Despite an abundance of alternative approaches to homology search, blast remains the only method to offer fast, sensitive search of large genomic collections on modern desktop hardware. As a result, the tool has found widespread use with millions of queries posed each day. A significant investment of computing resources is required to process this large volume of genomic searches and a cluster of over 200 workstations is employed by the NCBI to handle queries posed through the organisation's website. As the growth of sequence databases continues to outpace improvements in modern hardware, blast searches are becoming slower each year and novel, faster methods for sequence comparison are required. In this thesis we propose new techniques for fast yet accurate homology search that result in significantly faster blast searches. First, we describe improvements to the final, gapped alignment stages where the query and sequences from the collection are aligned to provide a fine-grain measure of similarity. We describe three new methods for aligning sequences that roughly halve the time required to perform this computationally expensive stage. Next, we investigate improvements to the first stage of search, where short regions of similarity between a pair of sequences are identified. We propose a novel deterministic finite automaton data structure that is significantly smaller than the codeword lookup table employed by ncbi-blast, resulting in improved cache performance and faster search times. We also discuss fast methods for nucleotide sequence comparison. We describe novel approaches for processing sequences that are compressed using the byte packed format already utilised by blast, where four nucleotide bases from a strand of DNA are stored in a single byte. Rather than decompress sequences to perform pairwise comparisons, our innovations permit sequences to be processed in their compressed form, four bases at a time. Our techniques roughly halve average query evaluation times for nucleotide searches with no effect on the sensitivity of blast. Finally, we present a new scheme for managing the high degree of redundancy that is prevalent in genomic collections. Near-duplicate entries in sequence data banks are highly detrimental to retrieval performance, however existing methods for managing redundancy are both slow, requiring almost ten hours to process the GenBank database, and crude, because they simply purge highly-similar sequences to reduce the level of internal redundancy. We describe a new approach for identifying near-duplicate entries that is roughly six times faster than the most successful existing approaches, and a novel approach to managing redundancy that reduces collection size and search times but still provides accurate and comprehensive search results. Our improvements to blast have been integrated into our own version of the tool. We find that our innovations more than halve average search times for nucleotide and protein searches, and have no signifcant effect on search accuracy. Given the enormous popularity of blast, this represents a very significant advance in computational methods to aid life science research.

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4

Cooper, Gina Marie. "IMPROVING REMOTE HOMOLOGY DETECTION USING A SEQUENCE PROPERTY APPROACH." Wright State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=wright1251308636.

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5

Renkawitz, Jörg. "Monitoring homology search during DNA double-strand break repair in vivo." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-169454.

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Anstett, Benjamin [Verfasser], and Peter [Akademischer Betreuer] Becker. "Homology search guidance by the yeast recombination enhancer / Benjamin Anstett ; Betreuer: Peter Becker." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2017. http://d-nb.info/1132995329/34.

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Lee, Tsung-Lu. "BAXQLB̲LAST an enhanced BLAST bioinformatics homology search tool with batch and structured query support /." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1001161.

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Renkawitz, Jörg [Verfasser], and Stefan [Akademischer Betreuer] Jentsch. "Monitoring homology search during DNA double-strand break repair in vivo / Jörg Renkawitz. Betreuer: Stefan Jentsch." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2013. http://d-nb.info/1050648188/34.

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Freyhult, Eva. "A Study in RNA Bioinformatics : Identification, Prediction and Analysis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8305.

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Gajdoš, Pavel. "Vyhledávání homologních enzymů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255409.

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Tato práce se zabývá vyhledáváním homologních enzymů v proteinových databázích, jejímž cílem je navrhnout nástroj poskytující takové vyhledávání. Čtenář se seznámí se základní teorií týkající se proteinů, enzymů, homologie, ale také s existujícími nástroji pro vyhledávání homologních proteinů a enzymů. Dále je popsáno ohodnocení nalezených existujících nástrojů pro vyhledávání homologních enzymů. Pro potřeby vyhodnocení byla vytvořena datová sada spolu s algoritmem pro vyhodnocení vyýsledků jednotlivých nástrojů. Další částí práce je návrh a implementace nové metody pro vyhledávání homologních enzymů společně s jejím vyhodnocením. Jsou popsány dva algoritmy (One-by-One a MSA) pro vyhledávání homologních enzymů, jejichž porovnání ukazuje, že MSA algoritmus je zanedbatelně lepší z hlediska přesnosti než One-by-One algoritmus zatímco z hlediska rychlosti vítězí One-by-One algoritmus.

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Books on the topic "Homology search"

1

Hamilton, John Michael Uwe. Search for a plant homologue of the Saccharomyces cerevisiae cell cycle control gene CDC7. Manchester: University of Manchester, 1994.

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2

Gribskov, Michael, and John Devereux, eds. Sequence Analysis Primer. Oxford University Press, 1995. http://dx.doi.org/10.1093/oso/9780195098747.001.0001.

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Computerized sequence analysis is an integral part of biotechnological research, yet many biologists have received no formal training in this important technology. Sequence Analysis Primer offers the beginner the necessary background to enter this vital field and helps more seasoned researchers to fine-tune their approach. It covers basic data manipulation such as homology searches, stem-loop identification, and protein secondary structure prediction, and is compatible with most sequence analysis programs. A detailed example giving steps for characterizing a new gene sequence provides users with hands-on experience when combined with their current software. The book will be invaluable to researchers and students in molecular biology, genetics, biochemistry, microbiology, and biotechnology.

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3

Duncan, Nancy M. Evolutionary conservation of the Caenorhabditis elegans sex determining gene her-1: The search of a her-1 homologue in the related nematode, Caenorhabditis briggsae. 1993.

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Book chapters on the topic "Homology search"

1

Stamboulian, Mouses, and Nashat Mansour. "Scatter Search for Homology Modeling." In Lecture Notes in Computer Science, 66–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41000-5_7.

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Saitou, Naruya. "Homology Search and Multiple Alignment." In Introduction to Evolutionary Genomics, 325–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92642-1_15.

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3

Stojmirović, Aleksandar, Peter Andreae, Mike Boland, Thomas William Jordan, and Vladimir G. Pestov. "PFMFind: A System for Discovery of Peptide Homology and Function." In Similarity Search and Applications, 319–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41062-8_32.

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4

Murata, Mitsuo. "Application of PVM to Protein Homology Search." In Recent Advances in Parallel Virtual Machine and Message Passing Interface, 410–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11846802_60.

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5

Xu, Jinbo, Daniel G. Brown, Ming Li, and Bin Ma. "Optimizing Multiple Spaced Seeds for Homology Search." In Combinatorial Pattern Matching, 47–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27801-6_4.

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Yamaguchi, Yoshiki, Tsutomu Maruyama, and Akihiko Konagaya. "Three-Dimensional Dynamic Programming for Homology Search." In Field Programmable Logic and Application, 505–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30117-2_52.

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7

Marz, Manja, Stefanie Wehner, and Peter F. Stadler. "Homology Search for Small Structured Non-coding RNAs." In Handbook of RNA Biochemistry, 619–32. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2014. http://dx.doi.org/10.1002/9783527647064.ch29.

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8

Cameron, Michael, Yaniv Bernstein, and Hugh E. Williams. "Clustering Near-Identical Sequences for Fast Homology Search." In Lecture Notes in Computer Science, 175–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11732990_16.

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Yamaguchi, Yoshiki, Yosuke Miyajima, Tsutomu Maruyama, and Akihiko Konagaya. "High Speed Homology Search Using Run-Time Reconfiguration." In Lecture Notes in Computer Science, 281–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46117-5_30.

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Li, Weiming, Bin Ma, and Kaizhong Zhang. "Amino Acid Classification and Hash Seeds for Homology Search." In Bioinformatics and Computational Biology, 44–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00727-9_6.

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Conference papers on the topic "Homology search"

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LI, MING. "MODERN HOMOLOGY SEARCH." In Proceedings of the 19th International Conference. IMPERIAL COLLEGE PRESS, 2008. http://dx.doi.org/10.1142/9781848163324_0020.

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McVicar, Nathaniel, Walter L. Ruzzo, and Scott Hauck. "Accelerating ncRNA homology search with FPGAs." In the ACM/SIGDA international symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2435264.2435276.

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Chang, Jeffrey T., Soumya Raychaudhuri, and Russ B. Altman. "Including Biological Literature Improves Homology Search." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789814447362_0037.

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YAMAGUCHI, Yoshiki, Tsutomu MARUYAMA, and Akihiko KONAGAYA. "High Speed Homology Search with FPGAs." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799623_0025.

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Aljawad, Osama, Yanni Sun, Alex Liu, and Jikai Lei. "NcRNA homology search using Hamming distance seeds." In the 2nd ACM Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2147805.2147828.

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Lee, Hsiao Ping, Yin Te Tsai, and Chuan Yi Tang. "A seriate coverage filtration approach for homology search." In the 2004 ACM symposium. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/967900.967937.

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Doong, Shing-hwang. "Protein Homology Modeling with Heuristic Search for Sequence Alignment." In Proceedings of the 40th Annual Hawaii International Conference on System Sciences. IEEE, 2007. http://dx.doi.org/10.1109/hicss.2007.453.

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Ge, Hongwei, Liang Sun, and Jinghong Yu. "Enhancing protein homology batch search algorithm with sequence compression and clustering." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822809.

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Nishii, Takuma, Tomoyuki Hiroyasu, Masato Yoshimi, Mitsunori Miki, and Hisatake Yokouchi. "Similar subsequence retrieval from two time series data using homology search." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5641809.

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Khodabakhshi, Alireza Hadj, Mehdi Mirzazadeh, and Arvind Gupta. "An efficient data structure for applying multiple seeds in homology search." In 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering. IEEE, 2007. http://dx.doi.org/10.1109/bibe.2007.4375750.

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