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Academic literature on the topic 'Basic Local Alignment Search Tool'

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Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Basic Local Alignment Search Tool"

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Altschul, Stephen F., Warren Gish, Webb Miller, Eugene W. Myers, and David J. Lipman. "Basic local alignment search tool." Journal of Molecular Biology 215, no. 3 (October 1990): 403–10. http://dx.doi.org/10.1016/s0022-2836(05)80360-2.

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Mount, David W. "Using the Basic Local Alignment Search Tool (BLAST)." Cold Spring Harbor Protocols 2007, no. 7 (July 2007): pdb.top17. http://dx.doi.org/10.1101/pdb.top17.

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Wang, Hui, Leixiao Li, Chengdong Zhou, Hao Lin, and Dan Deng. "Spark-based Parallelization of Basic Local Alignment Search Tool." International Journal Bioautomation 24, no. 1 (March 2020): 87–98. http://dx.doi.org/10.7546/ijba.2020.24.1.000767.

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Eric, S. Donkor, T. K. D. Dayie Nicholas, and K. Adiku Theophilus. "Bioinformatics with basic local alignment search tool (BLAST) and fast alignment (FASTA)." Journal of Bioinformatics and Sequence Analysis 6, no. 1 (April 30, 2014): 1–6. http://dx.doi.org/10.5897/ijbc2013.0086.

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Konerding, D. "An Essential Guide to the Basic Local Alignment Search Tool: BLAST." Briefings in Bioinformatics 5, no. 1 (January 1, 2004): 93–94. http://dx.doi.org/10.1093/bib/5.1.93.

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O’Driscoll, Aisling, Vladislav Belogrudov, John Carroll, Kai Kropp, Paul Walsh, Peter Ghazal, and Roy D. Sleator. "HBLAST: Parallelised sequence similarity – A Hadoop MapReducable basic local alignment search tool." Journal of Biomedical Informatics 54 (April 2015): 58–64. http://dx.doi.org/10.1016/j.jbi.2015.01.008.

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Chen, Ying, Weicai Ye, Yongdong Zhang, and Yuesheng Xu. "High speed BLASTN: an accelerated MegaBLAST search tool." Nucleic Acids Research 43, no. 16 (2015): 7762–68. http://dx.doi.org/10.1093/nar/gkv784.

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Abstract Sequence alignment is a long standing problem in bioinformatics. The Basic Local Alignment Search Tool (BLAST) is one of the most popular and fundamental alignment tools. The explosive growth of biological sequences calls for speedup of sequence alignment tools such as BLAST. To this end, we develop high speed BLASTN (HS-BLASTN), a parallel and fast nucleotide database search tool that accelerates MegaBLAST—the default module of NCBI-BLASTN. HS-BLASTN builds a new lookup table using the FMD-index of the database and employs an accurate and effective seeding method to find short stretches of identities (called seeds) between the query and the database. HS-BLASTN produces the same alignment results as MegaBLAST and its computational speed is much faster than MegaBLAST. Specifically, our experiments conducted on a 12-core server show that HS-BLASTN can be 22 times faster than MegaBLAST and exhibits better parallel performance than MegaBLAST. HS-BLASTN is written in C++ and the related source code is available at https://github.com/chenying2016/queries under the GPLv3 license.
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Nowicki, Marek, Davit Bzhalava, and Piotr BaŁa. "Massively Parallel Implementation of Sequence Alignment with Basic Local Alignment Search Tool Using Parallel Computing in Java Library." Journal of Computational Biology 25, no. 8 (August 2018): 871–81. http://dx.doi.org/10.1089/cmb.2018.0079.

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Matsuda, Fumio, Hiroshi Tsugawa, and Eiichiro Fukusaki. "Method for Assessing the Statistical Significance of Mass Spectral Similarities Using Basic Local Alignment Search Tool Statistics." Analytical Chemistry 85, no. 17 (August 14, 2013): 8291–97. http://dx.doi.org/10.1021/ac401564v.

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Guo, Xinyu, Hong Wang, and Vijay Devabhaktuni. "A Systolic Array-Based FPGA Parallel Architecture for the BLAST Algorithm." ISRN Bioinformatics 2012 (September 4, 2012): 1–11. http://dx.doi.org/10.5402/2012/195658.

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A design of systolic array-based Field Programmable Gate Array (FPGA) parallel architecture for Basic Local Alignment Search Tool (BLAST) Algorithm is proposed. BLAST is a heuristic biological sequence alignment algorithm which has been used by bioinformatics experts. In contrast to other designs that detect at most one hit in one-clock-cycle, our design applies a Multiple Hits Detection Module which is a pipelining systolic array to search multiple hits in a single-clock-cycle. Further, we designed a Hits Combination Block which combines overlapping hits from systolic array into one hit. These implementations completed the first and second step of BLAST architecture and achieved significant speedup comparing with previously published architectures.
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Dissertations / Theses on the topic "Basic Local Alignment Search Tool"

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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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Book chapters on the topic "Basic Local Alignment Search Tool"

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Oliveira, Demian, Fernando Braz, Bruno Ferreira, Alessandra Faria-Campos, and Sérgio Campos. "Using Binary Decision Diagrams (BDDs) for Memory Optimization in Basic Local Alignment Search Tool (BLAST)." In Advances in Bioinformatics and Computational Biology, 65–72. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12418-6_9.

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"Basic Local Alignment Search Tool (BLAST)." In Bioinformatics and Functional Genomics, 87–125. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/047145916x.ch4.

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"Blast (basic local alignment search tool)." In Encyclopedia of Genetics, Genomics, Proteomics and Informatics, 221. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6754-9_1879.

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Vidyasagar, M. "Blast Theory." In Hidden Markov Processes. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691133157.003.0009.

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This chapter deals with BLAST theory. BLAST (Basic Local Alignment Search Tool) is a widely used statistical method for finding similarities between sequences of symbols from finite alphabets. While the theory is completely general, the most widely used applications are to comparing sequences of nucleotides and sequences of amino acids. The fundamental objective of BLAST theory is to align sequences as well as possible, and then make a determination as to the level of statistical significance of the alignment. Thus one computes a “maximal segmental score” of the alignment between the two sequences, and tests to see whether the maximal segmental score could have been obtained purely as a matter of chance. The chapter presents the main results of BLAST theory, focusing on the moment generating function and application of the results. It also presents the proofs of the main results.
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Bailey, Timothy L. "MEME, MAST, and Meta-MEME: New Tools for Motif Discovery in Protein Sequences." In Pattern Discovery in Biomolecular Data. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195119404.003.0008.

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We are in the midst of an explosive increase in the number of DNA and protein sequences available for study, as various genome projects come on line. This wealth of information offers important opportunities for understanding many biological processes and developing new plant and animal models, and ultimately drugs, for human diseases, in addition to other applications of modern biotechnology. Unfortunately, sequences are accumulating at a pace that strains present methods for extracting significant biological information from them. A consequence of this explosion in the sequence databases is that there is much interest and effort in developing tools that can efficiently and automatically extract the relevant biological information in sequence data and make it available for use in biology and medicine. In this chapter, we describe one such method that we have developed based on algorithms from artificial intelligence research. We call this software tool MEME (Multiple Expectation-maximization for Motif Elicitation). It has the attractive property that it is an “unsupervised” discovery tool: it can identify motifs, such as regulatory sites in DNA and functional domains in proteins, from large or small groups of unaligned sequences. As we show below, motifs are a rich source of information about a dataset; they can be used to discover other homologs in a database, to identify protein subsets that contain one or more motifs, and to provide information for mutagenesis studies to elucidate structure and function in the protein family as well as its evolution. Learning tools are used to extract higher level biological patterns from lower level DNA and protein sequence data. In contrast, search tools such as BLAST (Basic Local Alignment Search Tool) take a given higher level pattern and find all items in a database that possess the pattern. Searching for items that have a certain pattern is a problem intrinsically easier than discovering what the pattern is from items that possess it. The patterns considered here are motifs, which for DNA data can be subsequences that interact with transcription factors, polymerases, and other proteins.
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Sá Teles de Oliveira Molina, Juliana, Andreia Moreira dos Santos Carmo, Gabriel Lopes Pereira, Leticia Abrantes de Andrade, Felipe Trovalim Jordão, Rodrigo Buzinaro Suzuki, Luana Prado Rolim de Oliveira, Aline Diniz Cabral, and Márcia Aparecida Sperança. "Novel Single Hematophagous Insect RNA Detection Method Supports Its Use as Sentinels to Survey Flaviviruses Circulation." In Dengue Fever in a One Health Perspective. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.92071.

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Anthropogenic actions, including deforestation, disorganized urbanization, and globalization, contribute to emergence and reemergence of arboviruses worldwide, where Flavivirus is the most prevalent, and its continuous monitoring can help in preventive control strategies. Thus, the aim of this study was to detect flavivirus RNA in single hematophagous insects, which are used as sentinels. Total RNA was extracted from six Aedes aegypti stored since 2003 and from 100 Culicidae and collected through CDC trap in a public park of a Brazilian Northwest city of São Paulo State. Flavivirus was detected through RT/PCR targeting 230–250 bp of the RNA polymerase coding sequence (NS5). PCR amplicons were sequenced by Sanger method, used in comparative analysis over Basic Local Alignment Search Tool (BLAST) in GenBank, and subjected to Neighbor-Joining phylogenetic analyses. Efficiency of Flavivirus diagnosis was confirmed by detection of Dengue virus serotype 2 in Ae. aegypti. From the 100 collected insects, 19 were positive for Culex flavivirus (CxFV). NS5 partial sequence phylogenetic analysis clustered all CxFV in one branch separated from vertebrate flaviviruses, being applicable to the identification of Flavivirus species. The dipteran RNA extraction methodology described in this work supports detection of flaviviruses in single insects maintained in 80% ethanol, which can be used to constant arbovirus surveillance.
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Kagendo, Dorothy, Eric Muchiri, Peter Gitonga, and Esther Muthoni. "Interlinks between Wildlife and Domestic Cycles of Echinococcus spp. in Kenya." In Managing Wildlife in a Changing World [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94612.

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Effective conservation and management of wildlife in the current changing world, call for incorporation of infectious zoonotic diseases surveillance systems, among other interventions. One of such diseases is echinococcosis, a zoonotic disease caused by Echinococcus species. This disease exists in two distinct life cycle patterns, the domestic and wildlife cycles. To investigate possible inter-links between these cycles in Kenya, 729 fecal samples from wild carnivores and 406 from domestic dogs (Canis lupus familiaris) collected from Maasai Mara and Samburu National Reserves were analyzed. Taeniid eggs were isolated by zinc chloride sieving-flotation method and subjected to polymerase chain reaction of nicotinamide adenine dehydrogenase subunit 1 (NAD1). Subsequent amplicons were sequenced, edited and analyzed with GENtle VI.94 program. The samples were further subjected to molecular identification of specific host species origin. All sequences obtained were compared with those in Gene-bank using Basic Local Alignment Search Tool (BLAST). The study found that there were 74 taeniid positive samples, 53 from wild carnivores and 21 from domestic dogs. In wildlife, mixed infections with Echinococcus and Taenia species were identified and these included E. granulosus sensu stricto, E. felidis, T. canadensis G6/7, Taenia hydatigena, T. multiceps, and T. saginata. Domestic dogs harbored Echinococcus and Taenia species similar to wild carnivores including E. granulosus G1–3, E. felidis, T. multiceps, T. hydatigena, and T. madoquae. Taenia species of nine taeniid eggs were not identified. Majority of genotypes were found in hyena (Crocuta crocuta) fecal samples. Distribution of Echinococcus and Taenia spp. varied with hosts. Mixed infections of Echinococcus spp, T. multiceps and T. hydatigena in a single animal were common. There seemed to be existence of interactions between the two cycles, although public health consequences are unknown. The presence of T. saginata in hyena suggests scavenging of human fecal matter by the animal. In addition, presence of T. multiceps, T hydatigena, T madoquae and T. saginata in the two cycles suggested possible human exposure to these parasites. The results are important in drawing up of strategies and policies towards prevention and control of Echinococcosis and other Taenia related parasitic infections, especially in endemic areas given their potential risk to public and socio- economic livelihood.
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Conference papers on the topic "Basic Local Alignment Search Tool"

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Chang, Chih-Yu, Yu-Cheng Li, Nae-Chyun Chen, Xiao-Xuan Huang, and Yi-Chang Lu. "A special processor design for Nucleotide Basic Local Alignment Search Tool with a new Banded two-hit method." In 2016 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC). IEEE, 2016. http://dx.doi.org/10.1109/norchip.2016.7792921.

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