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Journal articles on the topic 'Homology search'

To see the other types of publications on this topic, follow the link: Homology search.

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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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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3

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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4

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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5

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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6

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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7

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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8

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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10

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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11

Renkawitz, Jörg, Claudio A. Lademann, and Stefan Jentsch. "γH2AX spreading linked to homology search." Cell Cycle 12, no. 16 (August 15, 2013): 2526–27. http://dx.doi.org/10.4161/cc.25836.

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12

GRUNDY, WILLIAM NOBLE. "Homology Detection via Family Pairwise Search." Journal of Computational Biology 5, no. 3 (January 1998): 479–91. http://dx.doi.org/10.1089/cmb.1998.5.479.

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13

Kolbe, D. L., and S. R. Eddy. "Fast filtering for RNA homology search." Bioinformatics 27, no. 22 (September 28, 2011): 3102–9. http://dx.doi.org/10.1093/bioinformatics/btr545.

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14

Choi, K. P., F. Zeng, and L. Zhang. "Good spaced seeds for homology search." Bioinformatics 20, no. 7 (February 5, 2004): 1053–59. http://dx.doi.org/10.1093/bioinformatics/bth037.

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15

Mironov, Andrey A., and Nickcolay N. Alexandrov. "Statistical method for rapid homology search." Nucleic Acids Research 16, no. 11 (1988): 5169–73. http://dx.doi.org/10.1093/nar/16.11.5169.

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16

Csuros, Miklos, and Bin Ma. "Rapid Homology Search with Neighbor Seeds." Algorithmica 48, no. 2 (May 8, 2007): 187–202. http://dx.doi.org/10.1007/s00453-007-0062-y.

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17

Haber, James E. "DNA Repair: The Search for Homology." BioEssays 40, no. 5 (March 30, 2018): 1700229. http://dx.doi.org/10.1002/bies.201700229.

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18

Ochoterena, Helga, Alexander Vrijdaghs, Erik Smets, and Regine Claßen-Bockhoff. "The Search for Common Origin: Homology Revisited." Systematic Biology 68, no. 5 (February 23, 2019): 767–80. http://dx.doi.org/10.1093/sysbio/syz013.

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AbstractUnderstanding the evolution of biodiversity on Earth is a central aim in biology. Currently, various disciplines of science contribute to unravel evolution at all levels of life, from individual organisms to species and higher ranks, using different approaches and specific terminologies. The search for common origin, traditionally called homology, is a connecting paradigm of all studies related to evolution. However, it is not always sufficiently taken into account that defining homology depends on the hierarchical level studied (organism, population, and species), which can cause confusion. Therefore, we propose a framework to define homologies making use of existing terms, which refer to homology in different fields, but restricting them to an unambiguous meaning and a particular hierarchical level. We propose to use the overarching term “homology” only when “morphological homology,” “vertical gene transfer,” and “phylogenetic homology” are confirmed. Consequently, neither phylogenetic nor morphological homology is equal to homology. This article is intended for readers with different research backgrounds. We challenge their traditional approaches, inviting them to consider the proposed framework and offering them a new perspective for their own research.

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19

Takabatake, Kazuki, Kazuki Izawa, Motohiro Akikawa, Keisuke Yanagisawa, Masahito Ohue, and Yutaka Akiyama. "Improved Large-Scale Homology Search by Two-Step Seed Search Using Multiple Reduced Amino Acid Alphabets." Genes 12, no. 9 (September 21, 2021): 1455. http://dx.doi.org/10.3390/genes12091455.

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Metagenomic analysis, a technique used to comprehensively analyze microorganisms present in the environment, requires performing high-precision homology searches on large amounts of sequencing data, the size of which has increased dramatically with the development of next-generation sequencing. NCBI BLAST is the most widely used software for performing homology searches, but its speed is insufficient for the throughput of current DNA sequencers. In this paper, we propose a new, high-performance homology search algorithm that employs a two-step seed search strategy using multiple reduced amino acid alphabets to identify highly similar subsequences. Additionally, we evaluated the validity of the proposed method against several existing tools. Our method was faster than any other existing program for ≤120,000 queries, while DIAMOND, an existing tool, was the fastest method for >120,000 queries.

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20

Curwen, Valery A., Gary W. Williams, and Jonathan B. L. Bard. "GHOST: a gene homology online search tool." Trends in Genetics 16, no. 7 (July 2000): 321–23. http://dx.doi.org/10.1016/s0168-9525(00)02045-x.

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21

Cameron, Michael, Yaniv Bernstein, and Hugh E. Williams. "Clustered Sequence Representation for Fast Homology Search." Journal of Computational Biology 14, no. 5 (June 2007): 594–614. http://dx.doi.org/10.1089/cmb.2007.r005.

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22

Xu, Jinbo, Daniel Brown, Ming Li, and Bin Ma. "Optimizing Multiple Spaced Seeds for Homology Search." Journal of Computational Biology 13, no. 7 (September 2006): 1355–68. http://dx.doi.org/10.1089/cmb.2006.13.1355.

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23

Wilburn, Grey W., and Sean R. Eddy. "Remote homology search with hidden Potts models." PLOS Computational Biology 16, no. 11 (November 30, 2020): e1008085. http://dx.doi.org/10.1371/journal.pcbi.1008085.

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Most methods for biological sequence homology search and alignment work with primary sequence alone, neglecting higher-order correlations. Recently, statistical physics models called Potts models have been used to infer all-by-all pairwise correlations between sites in deep multiple sequence alignments, and these pairwise couplings have improved 3D structure predictions. Here we extend the use of Potts models from structure prediction to sequence alignment and homology search by developing what we call a hidden Potts model (HPM) that merges a Potts emission process to a generative probability model of insertion and deletion. Because an HPM is incompatible with efficient dynamic programming alignment algorithms, we develop an approximate algorithm based on importance sampling, using simpler probabilistic models as proposal distributions. We test an HPM implementation on RNA structure homology search benchmarks, where we can compare directly to exact alignment methods that capture nested RNA base-pairing correlations (stochastic context-free grammars). HPMs perform promisingly in these proof of principle experiments.

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24

Wheeler, T. J., and S. R. Eddy. "nhmmer: DNA homology search with profile HMMs." Bioinformatics 29, no. 19 (July 9, 2013): 2487–89. http://dx.doi.org/10.1093/bioinformatics/btt403.

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25

Margelevičius, Mindaugas, Mindaugas Laganeckas, and Česlovas Venclovas. "COMA server for protein distant homology search." Bioinformatics 26, no. 15 (June 6, 2010): 1905–6. http://dx.doi.org/10.1093/bioinformatics/btq306.

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26

Brown, D. G. "Optimizing Multiple Seeds for Protein Homology Search." IEEE/ACM Transactions on Computational Biology and Bioinformatics 2, no. 1 (January 2005): 29–38. http://dx.doi.org/10.1109/tcbb.2005.13.

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27

Louxin Zhang. "Superiority of Spaced Seeds for Homology Search." IEEE/ACM Transactions on Computational Biology and Bioinformatics 4, no. 3 (July 2007): 496–505. http://dx.doi.org/10.1109/tcbb.2007.1013.

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28

Ma, B., J. Tromp, and M. Li. "PatternHunter: faster and more sensitive homology search." Bioinformatics 18, no. 3 (March 1, 2002): 440–45. http://dx.doi.org/10.1093/bioinformatics/18.3.440.

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29

Margelevičius, Mindaugas. "Bayesian nonparametrics in protein remote homology search." Bioinformatics 32, no. 18 (April 22, 2016): 2744–52. http://dx.doi.org/10.1093/bioinformatics/btw213.

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30

Jiang, Xianyang, Peiheng Zhang, Xinchun Liu, and Stephen S. T. Yau. "Survey on index based homology search algorithms." Journal of Supercomputing 40, no. 2 (March 23, 2007): 185–212. http://dx.doi.org/10.1007/s11227-006-0041-0.

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31

Joshi, Adwait Govind, Upadhyayula Surya Raghavender, and Ramanathan Sowdhamini. "Improved performance of sequence search algorithms in remote homology detection." F1000Research 2 (March 22, 2013): 93. http://dx.doi.org/10.12688/f1000research.2-93.v1.

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The protein sequence space is vast and diverse, spanning across different families. Biologically meaningful relationships exist between proteins at superfamily level. However, it is highly challenging to establish convincing relationships at the superfamily level by means of simple sequence searches. It is necessary to design a rigorous sequence search strategy to establish remote homology relationships and achieve high coverage. We have used iterative profile-based methods, along with constraints of sequence motifs, to specify search directions. We address the importance of multiple start points (queries) to achieve high coverage at protein superfamily level. We have devised strategies to employ a structural regime to search sequence space with good specificity and sensitivity. We employ two well-known sequence search methods, PSI-BLAST and PHI-BLAST, with multiple queries and multiple patterns to enhance homologue identification at the structural superfamily level. The study suggests that multiple queries improve sensitivity, while a pattern-constrained iterative sequence search becomes stringent at the initial stages, thereby driving the search in a specific direction and also achieves high coverage. This data mining approach has been applied to the entire structural superfamily database.

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32

Joshi, Adwait Govind, Upadhyayula Surya Raghavender, and Ramanathan Sowdhamini. "Improved performance of sequence search approaches in remote homology detection." F1000Research 2 (July 16, 2014): 93. http://dx.doi.org/10.12688/f1000research.2-93.v2.

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The protein sequence space is vast and diverse, spanning across different families. Biologically meaningful relationships exist between proteins at superfamily level. However, it is highly challenging to establish convincing relationships at the superfamily level by means of simple sequence searches. It is necessary to design a rigorous sequence search strategy to establish remote homology relationships and achieve high coverage. We have used iterative profile-based methods, along with constraints of sequence motifs, to specify search directions. We address the importance of multiple start points (queries) to achieve high coverage at protein superfamily level. We have devised strategies to employ a structural regime to search sequence space with good specificity and sensitivity. We employ two well-known sequence search methods, PSI-BLAST and PHI-BLAST, with multiple queries and multiple patterns to enhance homologue identification at the structural superfamily level. The study suggests that multiple queries improve sensitivity, while a pattern-constrained iterative sequence search becomes stringent at the initial stages, thereby driving the search in a specific direction and also achieves high coverage. This data mining approach has been applied to the entire structural superfamily database.

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33

Kim, Hee-Jeong, Xiang-Shun Cui, Eun-Jung Kim, Wun-Jae Kim, and Nam-Hyung Kim. "New porcine microRNA genes found by homology search." Genome 49, no. 10 (October 2006): 1283–86. http://dx.doi.org/10.1139/g06-120.

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MicroRNAs (miRNAs) repress target genes at the post-transcriptional level and play important roles in development and cell lineage decisions. In vertebrates, however, both the targets of miRNAs and their expression profiles during development are poorly understood. Thus far, 326 human miRNAs, 249 mouse miRNAs, and 195 rat miRNAs have been identified. Even though the pig is a promising animal model in regenerative biology, as well as in livestock production, only 54 pig miRNAs have been identified. Here, we report the identification of 58 new pig miRNAs. We conducted a homology search using either human or mouse miRNAs genes queried against the pig genome. The new pig miRNAs consist of 23 miRNAs showing homology to mouse, 21 showing homology to human, and 16 showing homology to both human and mouse. The expression profile of 6 miRNAs were analyzed by Northern blot analysis isolated from porcine tissues: ovary, heart, kidney, liver, and pancreas.

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34

LI, MING, BIN MA, DEREK KISMAN, and JOHN TROMP. "PATTERNHUNTER II: HIGHLY SENSITIVE AND FAST HOMOLOGY SEARCH." Journal of Bioinformatics and Computational Biology 02, no. 03 (September 2004): 417–39. http://dx.doi.org/10.1142/s0219720004000661.

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Extending the single optimized spaced seed of PatternHunter20 to multiple ones, PatternHunter II simultaneously remedies the lack of sensitivity of Blastn and the lack of speed of Smith–Waterman, for homology search. At Blastn speed, PatternHunter II approaches Smith–Waterman sensitivity, bringing homology search methodology research back to a full circle.

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35

Adzuma, Kenji. "No Sliding during Homology Search by RecA Protein." Journal of Biological Chemistry 273, no. 47 (November 20, 1998): 31565–73. http://dx.doi.org/10.1074/jbc.273.47.31565.

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36

Smith, Scott F. "Homology search with binary and trinary scoring matrices." International Journal of Bioinformatics Research and Applications 2, no. 2 (2006): 119. http://dx.doi.org/10.1504/ijbra.2006.009763.

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37

Pevzner, P. A. "Filtration efficiency in rapid homology search statistical algorithms." Biopolymers and Cell 6, no. 6 (November 20, 1990): 7–13. http://dx.doi.org/10.7124/bc.000299.

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38

Kisman, D., M. Li, B. Ma, and L. Wang. "tPatternHunter: gapped, fast and sensitive translated homology search." Bioinformatics 21, no. 4 (September 16, 2004): 542–44. http://dx.doi.org/10.1093/bioinformatics/bti035.

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39

Renkawitz, Jörg, Claudio A. Lademann, and Stefan Jentsch. "Mechanisms and principles of homology search during recombination." Nature Reviews Molecular Cell Biology 15, no. 6 (May 14, 2014): 369–83. http://dx.doi.org/10.1038/nrm3805.

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40

Miné-Hattab, Judith, and Rodney Rothstein. "Increased chromosome mobility facilitates homology search during recombination." Nature Cell Biology 14, no. 5 (April 8, 2012): 510–17. http://dx.doi.org/10.1038/ncb2472.

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41

Weiner, Allon, Nathan Zauberman, and Abraham Minsky. "Recombinational DNA repair in a cellular context: a search for the homology search." Nature Reviews Microbiology 7, no. 10 (October 2009): 748–55. http://dx.doi.org/10.1038/nrmicro2206.

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42

Meng, Xiandong, and Vipin Chaudhary. "Optimised fine and coarse parallelism for sequence homology search." International Journal of Bioinformatics Research and Applications 2, no. 4 (2006): 430. http://dx.doi.org/10.1504/ijbra.2006.011041.

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43

Preparata, Franco P., Louxin Zhang, and Kwok Pui Choi. "Quick, Practical Selection of Effective Seeds for Homology Search." Journal of Computational Biology 12, no. 9 (November 2005): 1137–52. http://dx.doi.org/10.1089/cmb.2005.12.1137.

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44

Dutreix, Marie, Renaud Fulconis, and Jean-Louis Viovy. "The Search for Homology: A Paradigm for Molecular Interactions?" Complexus 1, no. 2 (2003): 89–99. http://dx.doi.org/10.1159/000070465.

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45

Strelets, V. B., A. A. Ptitsyn, L. Milanesi, and H. A. Lim. "Data bank homology search algorithm with linear computation complexity." Bioinformatics 10, no. 3 (1994): 319–22. http://dx.doi.org/10.1093/bioinformatics/10.3.319.

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46

Frith, Martin C. "Gentle Masking of Low-Complexity Sequences Improves Homology Search." PLoS ONE 6, no. 12 (December 19, 2011): e28819. http://dx.doi.org/10.1371/journal.pone.0028819.

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47

Suzuki, Shuji, Takashi Ishida, Ken Kurokawa, and Yutaka Akiyama. "GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics." PLoS ONE 7, no. 5 (May 4, 2012): e36060. http://dx.doi.org/10.1371/journal.pone.0036060.

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48

Hung, Jui-Hung, and Zhiping Weng. "Sequence Alignment and Homology Search with BLAST and ClustalW." Cold Spring Harbor Protocols 2016, no. 11 (August 29, 2016): pdb.prot093088. http://dx.doi.org/10.1101/pdb.prot093088.

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49

Liao, Song-Mao, Yen-Cheng Chen, and Hung-Wen Li. "Studying RecA Homology Search Mechanism using Single-Molecule Methods." Biophysical Journal 102, no. 3 (January 2012): 281a. http://dx.doi.org/10.1016/j.bpj.2011.11.1551.

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50

Lee, Andrew J., Masayuki Endo, Jamie K. Hobbs, A. Giles Davies, and Christoph Wälti. "Micro-homology intermediates: RecA’s transient sampling revealed at the single molecule level." Nucleic Acids Research 49, no. 3 (January 21, 2021): 1426–35. http://dx.doi.org/10.1093/nar/gkaa1258.

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Abstract Recombinase A (RecA) is central to homologous recombination. However, despite significant advances, the mechanism with which RecA is able to orchestrate a search for homology remains elusive. DNA nanostructure-augmented high-speed AFM offers the spatial and temporal resolutions required to study the RecA recombination mechanism directly and at the single molecule level. We present the direct in situ observation of RecA-orchestrated alignment of homologous DNA strands to form a stable recombination product within a supporting DNA nanostructure. We show the existence of subtle and short-lived states in the interaction landscape, which suggests that RecA transiently samples micro-homology at the single RecA monomer-level throughout the search for sequence alignment. These transient interactions form the early steps in the search for sequence homology, prior to the formation of stable pairings at >8 nucleotide seeds. The removal of sequence micro-homology results in the loss of the associated transient sampling at that location.

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