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Journal articles on the topic 'Genomic search'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Montanari, Piero, Ilaria Bartolini, Paolo Ciaccia, Marco Patella, Stefano Ceri, and Marco Masseroli. "Pattern Similarity Search in Genomic Sequences." IEEE Transactions on Knowledge and Data Engineering 28, no. 11 (November 1, 2016): 3053–67. http://dx.doi.org/10.1109/tkde.2016.2595582.

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2

Wei-Mou, Zheng. "Genomic Signal Search by Dynamic Programming." Communications in Theoretical Physics 39, no. 6 (June 15, 2003): 761–64. http://dx.doi.org/10.1088/0253-6102/39/6/761.

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3

Fernandez, Javier D., Maurizio Lenzerini, Marco Masseroli, Francesco Venco, and Stefano Ceri. "Ontology-Based Search of Genomic Metadata." IEEE/ACM Transactions on Computational Biology and Bioinformatics 13, no. 2 (March 1, 2016): 233–47. http://dx.doi.org/10.1109/tcbb.2015.2495179.

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4

Howe, Kevin L., Premanand Achuthan, James Allen, Jamie Allen, Jorge Alvarez-Jarreta, M. Ridwan Amode, Irina M. Armean, et al. "Ensembl 2021." Nucleic Acids Research 49, no. D1 (November 2, 2020): D884—D891. http://dx.doi.org/10.1093/nar/gkaa942.

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Abstract The Ensembl project (https://www.ensembl.org) annotates genomes and disseminates genomic data for vertebrate species. We create detailed and comprehensive annotation of gene structures, regulatory elements and variants, and enable comparative genomics by inferring the evolutionary history of genes and genomes. Our integrated genomic data are made available in a variety of ways, including genome browsers, search interfaces, specialist tools such as the Ensembl Variant Effect Predictor, download files and programmatic interfaces. Here, we present recent Ensembl developments including two new website portals. Ensembl Rapid Release (http://rapid.ensembl.org) is designed to provide core tools and services for genomes as soon as possible and has been deployed to support large biodiversity sequencing projects. Our SARS-CoV-2 genome browser (https://covid-19.ensembl.org) integrates our own annotation with publicly available genomic data from numerous sources to facilitate the use of genomics in the international scientific response to the COVID-19 pandemic. We also report on other updates to our annotation resources, tools and services. All Ensembl data and software are freely available without restriction.

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Cannon, Ethalinda K. S., Scott M. Birkett, Bremen L. Braun, Sateesh Kodavali, Douglas M. Jennewein, Alper Yilmaz, Valentin Antonescu, et al. "POPcorn: An Online Resource Providing Access to Distributed and Diverse Maize Project Data." International Journal of Plant Genomics 2011 (December 27, 2011): 1–10. http://dx.doi.org/10.1155/2011/923035.

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The purpose of the online resource presented here, POPcorn (Project Portal for corn), is to enhance accessibility of maize genetic and genomic resources for plant biologists. Currently, many online locations are difficult to find, some are best searched independently, and individual project websites often degrade over time—sometimes disappearing entirely. The POPcorn site makes available (1) a centralized, web-accessible resource to search and browse descriptions of ongoing maize genomics projects, (2) a single, stand-alone tool that uses web Services and minimal data warehousing to search for sequence matches in online resources of diverse offsite projects, and (3) a set of tools that enables researchers to migrate their data to the long-term model organism database for maize genetic and genomic information: MaizeGDB. Examples demonstrating POPcorn’s utility are provided herein.

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Garczarek, Laurence, Ulysse Guyet, Hugo Doré, Gregory K. Farrant, Mark Hoebeke, Loraine Brillet-Guéguen, Antoine Bisch, et al. "Cyanorak v2.1: a scalable information system dedicated to the visualization and expert curation of marine and brackish picocyanobacteria genomes." Nucleic Acids Research 49, no. D1 (October 30, 2020): D667—D676. http://dx.doi.org/10.1093/nar/gkaa958.

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Abstract Cyanorak v2.1 (http://www.sb-roscoff.fr/cyanorak) is an information system dedicated to visualizing, comparing and curating the genomes of Prochlorococcus, Synechococcus and Cyanobium, the most abundant photosynthetic microorganisms on Earth. The database encompasses sequences from 97 genomes, covering most of the wide genetic diversity known so far within these groups, and which were split into 25,834 clusters of likely orthologous groups (CLOGs). The user interface gives access to genomic characteristics, accession numbers as well as an interactive map showing strain isolation sites. The main entry to the database is through search for a term (gene name, product, etc.), resulting in a list of CLOGs and individual genes. Each CLOG benefits from a rich functional annotation including EggNOG, EC/K numbers, GO terms, TIGR Roles, custom-designed Cyanorak Roles as well as several protein motif predictions. Cyanorak also displays a phyletic profile, indicating the genotype and pigment type for each CLOG, and a genome viewer (Jbrowse) to visualize additional data on each genome such as predicted operons, genomic islands or transcriptomic data, when available. This information system also includes a BLAST search tool, comparative genomic context as well as various data export options. Altogether, Cyanorak v2.1 constitutes an invaluable, scalable tool for comparative genomics of ecologically relevant marine microorganisms.

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7

Dutton, Gail. "CRISPR Retooled as a Genomic Search Engine." Genetic Engineering & Biotechnology News 38, no. 20 (November 15, 2018): 6–7. http://dx.doi.org/10.1089/gen.38.20.05.

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8

Wilson, Tyler J., and Steven X. Ge. "ArraySearch: A Web-Based Genomic Search Engine." Comparative and Functional Genomics 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/650842.

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Recent advances in microarray technologies have resulted in a flood of genomics data. This large body of accumulated data could be used as a knowledge base to help researchers interpret new experimental data. ArraySearch finds statistical correlations between newly observed gene expression profiles and the huge source of well-characterized expression signatures deposited in the public domain. A search query of a list of genes will return experiments on which the genes are significantly up- or downregulated collectively. Searches can also be conducted using gene expression signatures from new experiments. This resource will empower biological researchers with a statistical method to explore expression data from their own research by comparing it with expression signatures from a large public archive.

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9

Chan, Sarah. "In search of a post-genomic bioethics." History of the Human Sciences 31, no. 1 (February 2018): 116–23. http://dx.doi.org/10.1177/0952695117729119c.

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10

Kopanos, Christos, Vasilis Tsiolkas, Alexandros Kouris, Charles E. Chapple, Monica Albarca Aguilera, Richard Meyer, and Andreas Massouras. "VarSome: the human genomic variant search engine." Bioinformatics 35, no. 11 (October 30, 2018): 1978–80. http://dx.doi.org/10.1093/bioinformatics/bty897.

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11

de Souza, Natalie. "Genomic dark matter: the search goes extreme." Nature Methods 7, no. 6 (June 2010): 424. http://dx.doi.org/10.1038/nmeth0610-424.

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12

Kimani, Jane W., Koh-ichiro Yoshiura, Min Shi, Astanand Jugessur, Danilo Moretti-Ferreira, Kaare Christensen, and Jeffrey C. Murray. "Search for Genomic Alterations in Monozygotic Twins Discordant for Cleft Lip and/or Palate." Twin Research and Human Genetics 12, no. 5 (October 1, 2009): 462–68. http://dx.doi.org/10.1375/twin.12.5.462.

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AbstractPhenotypically discordant monozygotic twins offer the possibility of gene discovery through delineation of molecular abnormalities in one member of the twin pair. One proposed mechanism of discordance is postzygotically occurring genomic alterations resulting from mitotic recombination and other somatic changes. Detection of altered genomic fragments can reveal candidate gene loci that can be verified through additional analyses. We investigated this hypothesis using array comparative genomic hybridization; the 50K and 250K Affymetrix GeneChip® SNP arrays and an Illumina custom array consisting of 1,536 SNPs, to scan for genomic alterations in a sample of monozygotic twin pairs with discordant cleft lip and/or palate phenotypes. Paired analysis for deletions, amplifications and loss of heterozygosity, along with sequence verification of SNPs with discordant genotype calls did not reveal any genomic discordance between twin pairs in lymphocyte DNA samples. Our results demonstrate that postzygotic genomic alterations are not a common cause of monozygotic twin discordance for isolated cleft lip and/or palate. However, rare or balanced genomic alterations, tissue-specific events and small aberrations beyond the detection level of our experimental approach cannot be ruled out. The stability of genomes we observed in our study samples also suggests that detection of discordant events in other monozygotic twin pairs would be remarkable and of potential disease significance.

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Wan, Peng, and Dongsheng Che. "A Computational Framework for Tracing the Origins of Genomic Islands in Prokaryotes." International Scholarly Research Notices 2014 (October 28, 2014): 1–9. http://dx.doi.org/10.1155/2014/732857.

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Genomic islands (GIs) are chunks of genomic fragments that are acquired from nongenealogical organisms through horizontal gene transfer (HGT). Current researches on studying donor-recipient relationships for HGT are limited at a gene level. As more GIs have been identified and verified, the way of studying donor-recipient relationships can be better modeled by using GIs rather than individual genes. In this paper, we report the development of a computational framework for detecting origins of GIs. The main idea of our computational framework is to identify GIs in a query genome, search candidate genomes that contain genomic regions similar to those GIs in the query genome by BLAST search, and then filter out some candidate genomes if those similar genomic regions are also alien (detected by GI detection tools). We have applied our framework in finding the GI origins for Mycobacterium tuberculosis H37Rv, Herminiimonas arsenicoxydans, and three Thermoanaerobacter species. The predicted results were used to establish the donor-recipient network relationships and visualized by Gephi. Our studies have shown that donor genomes detected by our computational approach were mainly consistent with previous studies. Our framework was implemented with Perl and executed on Windows operating system.

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Abhinav, K. V., Ebenezer Samuel, and M. Vijayan. "Archeal lectins: An identification through a genomic search." Proteins: Structure, Function, and Bioinformatics 84, no. 1 (November 16, 2015): 21–30. http://dx.doi.org/10.1002/prot.24949.

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15

Buhler, Jeremy, Uri Keich, and Yanni Sun. "Designing seeds for similarity search in genomic DNA." Journal of Computer and System Sciences 70, no. 3 (May 2005): 342–63. http://dx.doi.org/10.1016/j.jcss.2004.12.003.

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16

Oliveira, Guilherme, Nilton B. Rodrigues, Alvaro J. Romanha, and Diana Bahia. "Genome and genomics of schistosomes." Canadian Journal of Zoology 82, no. 2 (February 1, 2004): 375–90. http://dx.doi.org/10.1139/z03-220.

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Schistosomes infect over 200 million people and 600 million are at risk. Genomics and post-genomic studies of schistosomes will contribute greatly to developing new reagents for diagnostic purposes and new vaccines that are of interest to the biotechnology industry. In this review, the most recent advances in these fields as well as new projects and future perspectives will de described. A vast quantity of data is publicly available, including short cDNA and genomic sequences, complete large genomic fragments, and the mitochondrial genomes of three species of the genus Schistosoma. The physical structure of the genome is being studied by physically mapping large genomic fragments and characterizing the highly abundant repetitive DNA elements. Bioinformatic manipulations of the data have already been carried out, mostly dealing with the functional analysis of the genes described. Specific search tools have also been developed. Sequence variability has been used to better understand the phylogeny of the species and for population studies, and new polymorphic genomic markers are currently being developed. The information generated has been used for the development of post-genomic projects. A small microarray detected genes that were differentially expressed between male and female worms. The identification of two-dimensional spots by mass spectrometry has also been demonstrated.

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Dash, Sajal, Sarthok Rasique Rahman, Heather M. Hines, and Wu-chun Feng. "iBLAST: Incremental BLAST of new sequences via automated e-value correction." PLOS ONE 16, no. 4 (April 22, 2021): e0249410. http://dx.doi.org/10.1371/journal.pone.0249410.

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Search results from local alignment search tools use statistical scores that are sensitive to the size of the database to report the quality of the result. For example, NCBI BLAST reports the best matches using similarity scores and expect values (i.e., e-values) calculated against the database size. Given the astronomical growth in genomics data throughout a genomic research investigation, sequence databases grow as new sequences are continuously being added to these databases. As a consequence, the results (e.g., best hits) and associated statistics (e.g., e-values) for a specific set of queries may change over the course of a genomic investigation. Thus, to update the results of a previously conducted BLAST search to find the best matches on an updated database, scientists must currently rerun the BLAST search against the entire updated database, which translates into irrecoverable and, in turn, wasted execution time, money, and computational resources. To address this issue, we devise a novel and efficient method to redeem past BLAST searches by introducing iBLAST. iBLAST leverages previous BLAST search results to conduct the same query search but only on the incremental (i.e., newly added) part of the database, recomputes the associated critical statistics such as e-values, and combines these results to produce updated search results. Our experimental results and fidelity analyses show that iBLAST delivers search results that are identical to NCBI BLAST at a substantially reduced computational cost, i.e., iBLAST performs (1 + δ)/δ times faster than NCBI BLAST, where δ represents the fraction of database growth. We then present three different use cases to demonstrate that iBLAST can enable efficient biological discovery at a much faster speed with a substantially reduced computational cost.

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18

Du, Chunguang, Edward Buckler, and Spencer Muse. "Development of a Maize Molecular Evolutionary Genomic Database." Comparative and Functional Genomics 4, no. 2 (2003): 246–49. http://dx.doi.org/10.1002/cfg.282.

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PANZEA is the first public database for studying maize genomic diversity. It was initiated as a repository of genomic diversity for an NSF Plant Genome project on ‘Maize Evolutionary Genomics’. PANZEA is hosted at the Bioinformatics Research Center, North Carolina State University, and is open to the public (http://statgen.ncsu.edu/panzea). PANZEA is designed to capture the interrelationships between germplasm, molecular diversity, phenotypic diversity and genome structure. It has the ability to store, integrate and visualize DNA sequence, enzymatic, SSR (simple sequence repeat) marker, germplasm and phenotypic data. The relational data model is selected and implemented in Oracle. An automated DNA sequence data submission tool has been created that allows project researchers to remotely submit their DNA sequence data directly to PANZEA. On-line database search forms and reports have been created to allow users to search or download germplasm, DNA sequence, gene/locus data and much more, directly from the web.

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19

Canakoglu, Arif, and Marco Masseroli. "Genomic and proteomic data integration for comprehensive biodata search." EMBnet.journal 18, B (November 9, 2012): 89. http://dx.doi.org/10.14806/ej.18.b.561.

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20

Barker, D., E. Wright, K. Nguyen, L. Cannon, P. Fain, D. Goldgar, D. T. Bishop, J. Carey, J. Kivlin, and H. Willard. "A genomic search for linkage of neurofibromatosis to RFLPs." Journal of Medical Genetics 24, no. 9 (September 1, 1987): 536–38. http://dx.doi.org/10.1136/jmg.24.9.536.

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21

Asharov, Gilad, Shai Halevi, Yehuda Lindell, and Tal Rabin. "Privacy-Preserving Search of Similar Patients in Genomic Data." Proceedings on Privacy Enhancing Technologies 2018, no. 4 (October 1, 2018): 104–24. http://dx.doi.org/10.1515/popets-2018-0034.

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Abstract The growing availability of genomic data holds great promise for advancing medicine and research, but unlocking its full potential requires adequate methods for protecting the privacy of individuals whose genome data we use. One example of this tension is running Similar Patient Query on remote genomic data: In this setting a doctor that holds the genome of his/her patient may try to find other individuals with “close” genomic data, and use the data of these individuals to help diagnose and find effective treatment for that patient’s conditions. This is clearly a desirable mode of operation. However, the privacy exposure implications are considerable, and so we would like to carry out the above “closeness” computation in a privacy preserving manner. In this work we put forward a new approach for highly efficient secure computation for computing an approximation of the Similar Patient Query problem. We present contributions on two fronts. First, an approximation method that is designed with the goal of achieving efficient private computation. Second, further optimizations of the two-party protocol. Our tests indicate that the approximation method works well, it returns the exact closest records in 98% of the queries and very good approximation otherwise. As for speed, our protocol implementation takes just a few seconds to run on databases with thousands of records, each of length thousands of alleles, and it scales almost linearly with both the database size and the length of the sequences in it. As an example, in the datasets of the recent iDASH competition, after a one-time preprocessing of around 12 seconds, it takes around a second to find the nearest five records to a query, in a size-500 dataset of length- 3500 sequences. This is 2-3 orders of magnitude faster than using state-of-the-art secure protocols with existing edit distance algorithms.

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Zonaed Siddiki, A. M. A. M., Gous Miah, Md Sirazul Islam, Mahadia Kumkum, Meheadi Hasan Rumi, Abdul Baten, and Mohammad Alamgir Hossain. "Goat Genomic Resources: The Search for Genes Associated with Its Economic Traits." International Journal of Genomics 2020 (August 21, 2020): 1–13. http://dx.doi.org/10.1155/2020/5940205.

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Goat plays a crucial role in human livelihoods, being a major source of meat, milk, fiber, and hides, particularly under adverse climatic conditions. The goat genomics related to the candidate gene approach is now being used to recognize molecular mechanisms that have different expressions of growth, reproductive, milk, wool, and disease resistance. The appropriate literature on this topic has been reviewed in this article. Several genetic characterization attempts of different goats have reported the existence of genotypic and morphological variations between different goat populations. As a result, different whole-genome sequences along with annotated gene sequences, gene function, and other genomic information of different goats are available in different databases. The main objective of this review is to search the genes associated with economic traits in goats. More than 271 candidate genes have been discovered in goats. Candidate genes influence the physiological pathway, metabolism, and expression of phenotypes. These genes have different functions on economically important traits. Some genes have pleiotropic effect for expression of phenotypic traits. Hence, recognizing candidate genes and their mutations that cause variations in gene expression and phenotype of an economic trait can help breeders look for genetic markers for specific economic traits. The availability of reference whole-genome assembly of goats, annotated genes, and transcriptomics makes comparative genomics a useful tool for systemic genetic upgradation. Identification and characterization of trait-associated sequence variations and gene will provide powerful means to give positive influences for future goat breeding program.

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Collins, Lesley J., Thomas J. Macke, and David Penny. "Searching for ncRNAs in eukaryotic genomes: Maximizing biological input with RNAmotif." Journal of Integrative Bioinformatics 1, no. 1 (December 1, 2004): 64–79. http://dx.doi.org/10.1515/jib-2004-6.

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Summary Non-coding RNAs (ncRNAs) contain both characteristic secondary-structure and short sequence motifs. However, “complex” ncRNAs (RNA bound to proteins in ribonucleoprotein complexes) can be hard to identify in genomic sequence data. Programs able to search for ncRNAs were previously limited to ncRNA molecules that either align very well or have highly conserved secondary-structure. The RNAmotif program uses additional information to find ncRNA gene candidates through the design of an appropriate “descriptor” to model sequence motifs, secondary-structure and protein/RNA binding information. This enables searches of those ncRNAs that contain variable secondary-structure and limited sequence motif information. Applying the biologically-based concept of “positive and negative controls” to the RNAmotif search technique, we can now go beyond the testing phase to successfully search real genomes, complete with their background noise and related molecules. Descriptors are designed for two “complex” ncRNAs, the U5snRNA (from the spliceosome) and RNaseP RNA, which successfully uncover these sequences from some eukaryotic genomes. We include explanations about the construction of the input “descriptors” from known biological information, to allow searches for other ncRNAs. RNAmotif maximizes the input of biological knowledge into a search for an ncRNA gene and now allows the investigation of some of the hardest-to-find, yet important, genes in some very interesting eukaryotic organisms.

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CAI, Min-Hua. "Application of Laser Capture Microdissection in Plant Genomic Re-search." HEREDITAS 28, no. 10 (2006): 1325. http://dx.doi.org/10.1360/yc-006-1325.

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Tremolada, S., S. Delbue, M. Ferraresso, C. Carloni, F. Elia, S. Larocca, E. Bortolani, and P. Ferrante. "Search for Genomic Sequences of Microbial Agents in Atherosclerotic Plaques." International Journal of Immunopathology and Pharmacology 24, no. 1 (January 2011): 243–46. http://dx.doi.org/10.1177/039463201102400130.

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Xuan, Z., W. R. McCombie, and M. Q. Zhang. "GFScan: A Gene Family Search Tool at Genomic DNA Level." Genome Research 12, no. 7 (June 18, 2002): 1142–49. http://dx.doi.org/10.1101/gr.220102.

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Xuan, Z. "GFScan: A Gene Family Search Tool at Genomic DNA Level." Genome Research 12, no. 7 (June 18, 2002): 1142–49. http://dx.doi.org/10.1101/gr.220102.

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Bradley, Phelim, Henk C. den Bakker, Eduardo P. C. Rocha, Gil McVean, and Zamin Iqbal. "Ultrafast search of all deposited bacterial and viral genomic data." Nature Biotechnology 37, no. 2 (February 2019): 152–59. http://dx.doi.org/10.1038/s41587-018-0010-1.

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Camp, Nicola J., James M. Farnham, and Lisa A. Cannon Albright. "Genomic search for prostate cancer predisposition loci in Utah pedigrees." Prostate 65, no. 4 (December 1, 2005): 365–74. http://dx.doi.org/10.1002/pros.20287.

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Zhang, Yongqing, Xiaoyi Cao, and Sheng Zhong. "GeNemo: a search engine for web-based functional genomic data." Nucleic Acids Research 44, W1 (April 20, 2016): W122—W127. http://dx.doi.org/10.1093/nar/gkw299.

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Kros, Johan M., Ernst J. Delwel, Rob T. de Jong, Herve L. Tanghe, Peter R. van Run, Kees Vissers, and Janneke C. Alers. "Desmoplastic infantile astrocytoma and ganglioglioma: a search for genomic characteristics." Acta Neuropathologica 104, no. 2 (March 29, 2002): 144–48. http://dx.doi.org/10.1007/s00401-002-0534-8.

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Whitt, Karen J., McKenna Hughes, Elizabeth (Betsy) S. Hopkins, and Ann Maradiegue. "The Gene Pool: The Ethics of Genetics in Primary Care." Annual Review of Nursing Research 34, no. 1 (January 2016): 119–54. http://dx.doi.org/10.1891/0739-6686.34.119.

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Aim: The purpose of this integrative review is to critically analyze the research literature regarding ethical principles that surround the integration of genetics and genomics in primary care clinical practice. Background: Advanced practice nurses (APRNs) play an important role in the provision of primary care services, in the areas of obstetrics, pediatrics, family practice, and internal medicine. Advances in genetic and genomic science are infiltrating these day-to-day health-care systems and becoming an integral part of health-care delivery. It is imperative for primary care providers to understand the ethical, legal, and social implications of genetics and genomics. Methods: A comprehensive multistep search of CINAHL, MEDLINE, Academic Search Premier, PsycINFO, Web of Science, and Scopus databases was conducted to identify primary research articles published from 2003 to 2015 that evaluated ethical issues related to genetics and genomics in U. S. primary care practice. A sample of 26 primary research articles met the inclusion criteria. Whittemore and Knafl's (2005) revised framework for integrative reviews was used to guide the analysis and assess the quality of the studies. Key findings from the studies are discussed according to Beauchamp and Childress's (2009) ethical principles of autonomy, beneficence, nonmaleficence, and justice. Results: Research conducted to date is mainly qualitative and descriptive and the analysis revealed several ethical challenges to implementing genetics and genomics in primary care settings. Conclusion: The review suggests that there are several implications for research, education, and the development of primary care practice that support APRNs delivering genetic and genomic care while incorporating knowledge of ethical principles. More research needs to be conducted that evaluates the actual genetic/genomic ethical issues encountered by primary care providers.

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Heckel, D. G., L. J. Gahan, J. C. Daly, and S. Trowell. "A genomic approach to understanding Heliothis and Helicoverpa resistance to chemical and biological insecticides." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1376 (October 29, 1998): 1713–22. http://dx.doi.org/10.1098/rstb.1998.0323.

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Genomics is the comparative study of the structure and function of entire genomes. Although the complete sequencing of the genome of any insect pest is far in the future, a genomic approach can be useful in the study of mechanisms of insecticide resistance. We describe this strategy for Heliothis and Helicoverpa , two of the most destructive genera of pest moths (Lepidoptera) worldwide. Genome–wide linkage mapping provides the location of major and minor resistance genes. Positional cloning identifies novel resistance genes, even when the mechanisms are poorly understood, as with resistance to Bacillus thuringiensis toxins. Anchor loci provide the reference points for comparing the genomes and the genetic architecture of resistance mechanisms among related species. Collectively, these tools enable the description of the evolutionary response of related, but independent, genomes to the common selective pressure of insecticides in the environment. They also provide information that is useful for targeted management of specific resistance genes, and may even speed the search for families of novel insecticidal targets in Lepidoptera.

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Carlborg, Örjan, Leif Andersson, and Brian Kinghorn. "The Use of a Genetic Algorithm for Simultaneous Mapping of Multiple Interacting Quantitative Trait Loci." Genetics 155, no. 4 (August 1, 2000): 2003–10. http://dx.doi.org/10.1093/genetics/155.4.2003.

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AbstractHere we describe a general method for improving computational efficiency in simultaneous mapping of multiple interacting quantitative trait loci (QTL). The method uses a genetic algorithm to search for QTL in the genome instead of an exhaustive enumerative (“step-by-step”) search. It can be used together with any method of QTL mapping based on a genomic search, since it only provides a more efficient way to search the genome for QTL. The computational demand decreases by a factor of ~130 when using genetic algorithm-based mapping instead of an exhaustive enumerative search for two QTL in a genome size of 2000 cM using a resolution of 1 cM. The advantage of using a genetic algorithm increases further for larger genomes, higher resolutions, and searches for more QTL. We show that a genetic algorithm-based search has efficiency higher than or equal to a search method conditioned on previously identified QTL for all epistatic models tested and that this efficiency is comparable to that of an exhaustive search for multiple QTL. The genetic algorithm is thus a powerful and computationally tractable alternative to the exhaustive enumerative search for simultaneous mapping of multiple interacting QTL. The use of genetic algorithms for simultaneous mapping of more than two QTL and for determining empirical significance thresholds using permutation tests is also discussed.

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De Lorenzis, Elisa, Giancarlo Albo, Fabrizio Longo, Carolina Bebi, Luca Boeri, and Emanuele Montanari. "Current Knowledge on Genomic Profiling of Upper Tract Urothelial Carcinoma." Genes 12, no. 3 (February 25, 2021): 333. http://dx.doi.org/10.3390/genes12030333.

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Recent research in next-generation sequencing characterized the genomic landscape of urothelial cancer. However, the majority of the studies focused on bladder cancer (BC). Upper urinary tract urothelial carcinomas (UTUC) and BC share some histological characteristics, but, considering the differences in terms of embryologic precursors, epidemiology, genetics, medical and surgical management and response to therapy, UTUC and BC should be considered as two distinct diseases. Our objective is to analyze through a literature search the latest updates and the current knowledge about the genomics of UTUC. We also evaluate genetic differences between BC and UTUC and the potential implications for systemic therapy. Molecular subtyping and variant histology and their correlation with response to chemotherapy were also explored. In summary, the most frequent genomic variations in UTUC included FGFR3, chromatin remodeling genes, TP53/MDM2 and other tumor suppressors/oncogenes. The genomics of UTUC, integrated with clinical data, could drive the selection of patients who could benefit from targeted therapy or off-label treatment. Routine implementation of tumor genomic characterization in UTUC patients should therefore be contemplated and evaluated prospectively.

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Feng, Jianglin, Aakrosh Ratan, and Nathan C. Sheffield. "Augmented Interval List: a novel data structure for efficient genomic interval search." Bioinformatics 35, no. 23 (May 31, 2019): 4907–11. http://dx.doi.org/10.1093/bioinformatics/btz407.

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Abstract Motivation Genomic data is frequently stored as segments or intervals. Because this data type is so common, interval-based comparisons are fundamental to genomic analysis. As the volume of available genomic data grows, developing efficient and scalable methods for searching interval data is necessary. Results We present a new data structure, the Augmented Interval List (AIList), to enumerate intersections between a query interval q and an interval set R. An AIList is constructed by first sorting R as a list by the interval start coordinate, then decomposing it into a few approximately flattened components (sublists), and then augmenting each sublist with the running maximum interval end. The query time for AIList is O(log2N+n+m), where n is the number of overlaps between R and q, N is the number of intervals in the set R and m is the average number of extra comparisons required to find the n overlaps. Tested on real genomic interval datasets, AIList code runs 5–18 times faster than standard high-performance code based on augmented interval-trees, nested containment lists or R-trees (BEDTools). For large datasets, the memory-usage for AIList is 4–60% of other methods. The AIList data structure, therefore, provides a significantly improved fundamental operation for highly scalable genomic data analysis. Availability and implementation An implementation of the AIList data structure with both construction and search algorithms is available at http://ailist.databio.org. Supplementary information Supplementary data are available at Bioinformatics online.

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Kulyyassov, Arman, and Ruslan Kalendar. "In Silico Estimation of the Abundance and Phylogenetic Significance of the Composite Oct4-Sox2 Binding Motifs within a Wide Range of Species." Data 5, no. 4 (November 29, 2020): 111. http://dx.doi.org/10.3390/data5040111.

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High-throughput sequencing technologies have greatly accelerated the progress of genomics, transcriptomics, and metagenomics. Currently, a large amount of genomic data from various organisms is being generated, the volume of which is increasing every year. Therefore, the development of methods that allow the rapid search and analysis of DNA sequences is urgent. Here, we present a novel motif-based high-throughput sequence scoring method that generates genome information. We found and identified Utf1-like, Fgf4-like, and Hoxb1-like motifs, which are cis-regulatory elements for the pluripotency transcription factors Sox2 and Oct4 within the genomes of different eukaryotic organisms. The genome-wide analysis of these motifs was performed to understand the impact of their diversification on mammalian genome evolution. Utf1-like, Fgf4-like, and Hoxb1-like motif diversity was evaluated across genomes from multiple species.

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Fotopoulos, George, Ioannis Vathiotis, George C. Nikou, and Konstantinos Syrigos. "The Role of Genetics in Sporadic GEP-NETs: A Comprehensive Review of the Literature." Forum of Clinical Oncology 8, no. 1 (June 30, 2017): 1–5. http://dx.doi.org/10.1515/fco-2017-0001.

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AbstractNeuroendocrine tumors (NETs) are composed of a heterogeneous group of malignancies from neuroendocrine cell compartments, with roles in both the endocrine and the nervous system. The majority of NETs are gastroenteropancreatic (GEP) in origin, arising in the foregut, midgut, or hindgut. The genomic landscape of GEP-NETs has been scarcely studied in terms of genomic profiling.The following algorithm was followed using the keywords neuroendocrine, genomics, targeted therapy, personalized medicine, gastroenteropancreatic and NET. The search was performed in PubMed and ScienceDirect database. Our current knowledge of sporadic GEP-NETs genetics must be further advanced to elucidate the molecular basis and pathogenesis of the disease, improve the accuracy of diagnosis, and guide tailor-made therapies.

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Timmers, Luis Fernando, Ivani Pauli, Guy Barcellos, Kelen Rocha, Rafael Caceres, Walter de Azevedo Jr., and Milena Soares. "Genomic Databases and the Search of Protein Targets for Protozoan Parasites." Current Drug Targets 10, no. 3 (March 1, 2009): 240–45. http://dx.doi.org/10.2174/138945009787581195.

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Mosig, Axel, Katrin Sameith, and Peter Stadler. "Fragrep: An Efficient Search Tool for Fragmented Patterns in Genomic Sequences." Genomics, Proteomics & Bioinformatics 4, no. 1 (2006): 56–60. http://dx.doi.org/10.1016/s1672-0229(06)60017-x.

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Seno, Shigeto, Yoichi Takenaka, Chikatoshi Kai, Jun Kawai, Piero Carninci, Yoshihide Hayashizaki, and Hideo Matsuda. "A Method for Similarity Search of Genomic Positional Expression Using CAGE." PLoS Genetics 2, no. 4 (April 28, 2006): e44. http://dx.doi.org/10.1371/journal.pgen.0020044.

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Rigden, D. J., M. Herman, S. Gillies, and P. A. M. Michels. "Implications of a genomic search for autophagy-related genes in trypanosomatids." Biochemical Society Transactions 33, no. 5 (October 26, 2005): 972–74. http://dx.doi.org/10.1042/bst0330972.

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Autophagy is the process by which cellular components are directed to and degraded in the vacuole or lysosome and has been studied largely in yeasts. We present here an in silico genomic analysis of trypanosomatid autophagy aimed at highlighting similarities and differences with autophagy in other organisms. Less than half of the yeast autophagy-related proteins examined have certain putative orthologues in trypanosomatids. A cytosol-to-vacuole transport system is clearly lacking in these organisms. Other absences are even more unexpected and have implications for our understanding of the molecular mechanisms of autophagy. The results are consistent with taxon-specific addition of components to a core autophagy machinery during evolution.

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Li, Y., J. A. Hill, G. H. Yue, F. Chen, and L. Orban. "Extensive search does not identify genomic sex markers in Tetraodon nigroviridis." Journal of Fish Biology 61, no. 5 (November 2002): 1314–17. http://dx.doi.org/10.1111/j.1095-8649.2002.tb02475.x.

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Shimada, Tomohiro, Nobuyuki Fujita, Michihisa Maeda, and Akira Ishihama. "Systematic search for the Cra-binding promoters using genomic SELEX system." Genes to Cells 10, no. 9 (September 2005): 907–18. http://dx.doi.org/10.1111/j.1365-2443.2005.00888.x.

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Herman, M., D. J. Rigden, S. Gillies, and P. A. M. Michels. "Implications of a genomic search for autophagy-related genes in trypanosomatids." Biochemical Society Transactions 33, no. 5 (October 1, 2005): 972. http://dx.doi.org/10.1042/bst20050972.

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Capalbo, Carlo, Amelia Buffone, Annarita Vestri, Enrico Ricevuto, Christian Rinaldi, Massimo Zani, Sergio Ferraro, et al. "Does the Search for Large Genomic Rearrangements Impact BRCAPRO Carrier Prediction?" Journal of Clinical Oncology 25, no. 18 (June 20, 2007): 2632–34. http://dx.doi.org/10.1200/jco.2007.11.4330.

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Igarashi, Yoji, Daisuke Mori, Susumu Mitsuyama, Kazutoshi Yoshitake, Hiroaki Ono, Tsuyoshi Watanabe, Yukiko Taniuchi, et al. "A Preliminary Metagenome Analysis Based on a Combination of Protein Domains." Proteomes 7, no. 2 (April 29, 2019): 19. http://dx.doi.org/10.3390/proteomes7020019.

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Metagenomic data have mainly been addressed by showing the composition of organisms based on a small part of a well-examined genomic sequence, such as ribosomal RNA genes and mitochondrial DNAs. On the contrary, whole metagenomic data obtained by the shotgun sequence method have not often been fully analyzed through a homology search because the genomic data in databases for living organisms on earth are insufficient. In order to complement the results obtained through homology-search-based methods with shotgun metagenomes data, we focused on the composition of protein domains deduced from the sequences of genomes and metagenomes, and we utilized them in characterizing genomes and metagenomes, respectively. First, we compared the relationships based on similarities in the protein domain composition with the relationships based on sequence similarities. We searched for protein domains of 325 bacterial species produced using the Pfam database. Next, the correlation coefficients of protein domain compositions between every pair of bacteria were examined. Every pairwise genetic distance was also calculated from 16S rRNA or DNA gyrase subunit B. We compared the results of these methods and found a moderate correlation between them. Essentially, the same results were obtained when we used partial random 100 bp DNA sequences of the bacterial genomes, which simulated raw sequence data obtained from short-read next-generation sequences. Then, we applied the method for analyzing the actual environmental data obtained by shotgun sequencing. We found that the transition of the microbial phase occurred because the seasonal change in water temperature was shown by the method. These results showed the usability of the method in characterizing metagenomic data based on protein domain compositions.

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Musa, Aliyu, Matthias Dehmer, Olli Yli-Harja, and Frank Emmert-Streib. "Exploiting Genomic Relations in Big Data Repositories by Graph-Based Search Methods." Machine Learning and Knowledge Extraction 1, no. 1 (November 22, 2018): 205–10. http://dx.doi.org/10.3390/make1010012.

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We are living at a time that allows the generation of mass data in almost any field of science. For instance, in pharmacogenomics, there exist a number of big data repositories, e.g., the Library of Integrated Network-based Cellular Signatures (LINCS) that provide millions of measurements on the genomics level. However, to translate these data into meaningful information, the data need to be analyzable. The first step for such an analysis is the deliberate selection of subsets of raw data for studying dedicated research questions. Unfortunately, this is a non-trivial problem when millions of individual data files are available with an intricate connection structure induced by experimental dependencies. In this paper, we argue for the need to introduce such search capabilities for big genomics data repositories with a specific discussion about LINCS. Specifically, we suggest the introduction of smart interfaces allowing the exploitation of the connections among individual raw data files, giving raise to a network structure, by graph-based searches.

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Wand, Nathaniel O., Darren A. Smith, Andrew A. Wilkinson, Ashleigh E. Rushton, Stephen J. W. Busby, Iain B. Styles, and Robert K. Neely. "DNA barcodes for rapid, whole genome, single-molecule analyses." Nucleic Acids Research 47, no. 12 (March 28, 2019): e68-e68. http://dx.doi.org/10.1093/nar/gkz212.

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Abstract We report an approach for visualizing DNA sequence and using these ‘DNA barcodes’ to search complex mixtures of genomic material for DNA molecules of interest. We demonstrate three applications of this methodology; identifying specific molecules of interest from a dataset containing gigabasepairs of genome; identification of a bacterium from such a dataset and, finally, by locating infecting virus molecules in a background of human genomic material. As a result of the dense fluorescent labelling of the DNA, individual barcodes of the order 40 kb pairs in length can be reliably identified. This means DNA can be prepared for imaging using standard handling and purification techniques. The recorded dataset provides stable physical and electronic records of the total genomic content of a sample that can be readily searched for a molecule or region of interest.

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Byron, Kevin, Jason T. L. Wang, and Dongrong Wen. "Genome-Wide Prediction of Coaxial Helical Stacking Using Random Forests and Covariance Models." International Journal on Artificial Intelligence Tools 23, no. 03 (May 28, 2014): 1460008. http://dx.doi.org/10.1142/s0218213014600082.

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Developing effective artificial intelligence tools to find motifs in DNA, RNA and proteins poses a challenging yet important problem in life science research. In this paper, we present a computational approach for finding RNA tertiary motifs in genomic sequences. Specifically, we predict genomic coordinate locations for coaxial helical stackings in 3-way RNA junctions. These predictions are provided by our tertiary motif search package, named CSminer, which utilizes two versatile methodologies: random forests and covariance models. A coaxial helical stacking tertiary motif occurs in a 3-way RNA junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Our CSminer tool first uses a genome-wide search method based on covariance models to find a genomic region that may potentially contain a coaxial helical stacking tertiary motif. CSminer then uses a random forests classifier to predict whether the genomic region indeed contains the tertiary motif. Experimental results demonstrate the effectiveness of our approach.

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