Статті в журналах: "Genomic resources"

1

Gustafson, J. Perry, Patrick E. McGuire, and Calvin O. Qualset. "Genomic Resources." Genetics 168, no. 2 (October 2004): 583–84. http://dx.doi.org/10.1534/genetics.104.036731.

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Rusk, Nicole. "Functional genomic resources." Nature Methods 9, no. 1 (December 28, 2011): 35. http://dx.doi.org/10.1038/nmeth.1820.

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3

Li, Xiu-Qing, Rebecca Griffiths, David De Koeyer, Charlotte Rothwell, Vicki Gustafson, Sharon Regan, and Barry Flinn. "Functional genomic resources for potato." Canadian Journal of Plant Science 88, no. 4 (July 1, 2008): 573–81. http://dx.doi.org/10.4141/cjps07048.

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Considerable functional genomic resources have been developed by the potato research community in the past decade, including expressed sequence tag (EST) libraries, SAGE libraries, microarrays, molecular-function maps, and mutant populations. This article reviews the types, characteristics, strengths, limitations, and appropriate applications of these resources for genomic research and discusses perspectives on future directions. This wide selection of resources available to potato researchers complements efforts to sequence the entire genome and advances made in the development of saturated genetic maps. Key words: Solanum, potato, genomics, expressed sequence tag, microarray, longSAGE, data mining

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4

Fairley, Susan, Ernesto Lowy-Gallego, Emily Perry, and Paul Flicek. "The International Genome Sample Resource (IGSR) collection of open human genomic variation resources." Nucleic Acids Research 48, no. D1 (October 4, 2019): D941—D947. http://dx.doi.org/10.1093/nar/gkz836.

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Abstract To sustain and develop the largest fully open human genomic resources the International Genome Sample Resource (IGSR) (https://www.internationalgenome.org) was established. It is built on the foundation of the 1000 Genomes Project, which created the largest openly accessible catalogue of human genomic variation developed from samples spanning five continents. IGSR (i) maintains access to 1000 Genomes Project resources, (ii) updates 1000 Genomes Project resources to the GRCh38 human reference assembly, (iii) adds new data generated on 1000 Genomes Project cell lines, (iv) shares data from samples with a similarly open consent to increase the number of samples and populations represented in the resources and (v) provides support to users of these resources. Among recent updates are the release of variation calls from 1000 Genomes Project data calculated directly on GRCh38 and the addition of high coverage sequence data for the 2504 samples in the 1000 Genomes Project phase three panel. The data portal, which facilitates web-based exploration of the IGSR resources, has been updated to include samples which were not part of the 1000 Genomes Project and now presents a unified view of data and samples across almost 5000 samples from multiple studies. All data is fully open and publicly accessible.

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Šafář, J., J. Janda, J. Bartoš, M. Kubaláková, P. Kovářová, J. Číhalíková, H. Šimková, et al. "Development of BAC resources for genomic research on wheat." Czech Journal of Genetics and Plant Breeding 41, Special Issue (July 31, 2012): 202. http://dx.doi.org/10.17221/6173-cjgpb.

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Havey, Michael, Kenneth Sink, Maria Jenderek, and Christopher Town. "Genomic Resources for Asparagales." Aliso 22, no. 1 (2006): 305–10. http://dx.doi.org/10.5642/aliso.20062201.25.

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Antin, Parker B., and Jay H. Konieczka. "Genomic resources for chicken." Developmental Dynamics 232, no. 4 (2005): 877–82. http://dx.doi.org/10.1002/dvdy.20339.

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8

Overby, Casey, John Connolly, Christopher Chute, Joshua Denny, Robert Freimuth, Andrea Hartzler, Ingrid Holm, et al. "Practical considerations for implementing genomic information resources." Applied Clinical Informatics 07, no. 03 (July 2016): 870–82. http://dx.doi.org/10.4338/aci-2016-04-ra-0060.

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SummaryTo understand opinions and perceptions on the state of information resources specifically targeted to genomics, and approaches to delivery in clinical practice.We conducted a survey of genomic content use and its clinical delivery from representatives across eight institutions in the electronic Medical Records and Genomics (eMERGE) network and two institutions in the Clinical Sequencing Exploratory Research (CSER) consortium in 2014.Eleven responses representing distinct projects across ten sites showed heterogeneity in how content is being delivered, with provider-facing content primarily delivered via the electronic health record (EHR) (n=10), and paper/pamphlets as the leading mode for patient-facing content (n=9). There was general agreement (91%) that new content is needed for patients and providers specific to genomics, and that while aspects of this content could be shared across institutions there remain site-specific needs (73% in agreement).This work identifies a need for the improved access to and expansion of information resources to support genomic medicine, and opportunities for content developers and EHR vendors to partner with institutions to develop needed resources, and streamline their use – such as a central content site in multiple modalities while implementing approaches to allow for site-specific customization. Citation: Rasmussen LV, Overby CL, Connolly J, Chute CG, Denny JC, Freimuth RR, Hartzler AL, Holm IA, Manzi S, Pathak J, Peissig PL, Smith M, Williams MS, Shirts BH, Stoffel EM, Tarczy-Hornoch P, Rohrer Vitek CR, Wolf WA, Starren J. Practical considerations for implementing genomic information resources – experiences from eMERGE and CSER.

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9

Bryan, Glenn J., and Ingo Hein. "Genomic Resources and Tools for Gene Function Analysis in Potato." International Journal of Plant Genomics 2008 (December 18, 2008): 1–9. http://dx.doi.org/10.1155/2008/216513.

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Potato, a highly heterozygous tetraploid, is undergoing an exciting phase of genomics resource development. The potato research community has established extensive genomic resources, such as large expressed sequence tag (EST) data collections, microarrays and other expression profiling platforms, and large-insert genomic libraries. Moreover, potato will now benefit from a global potato physical mapping effort, which is serving as the underlying resource for a full potato genome sequencing project, now well underway. These tools and resources are having a major impact on potato breeding and genetics. The genome sequence will provide an invaluable comparative genomics resource for cross-referencing to the other Solanaceae, notably tomato, whose sequence is also being determined. Most importantly perhaps, a potato genome sequence will pave the way for the functional analysis of the large numbers of potato genes that await discovery. Potato, being easily transformable, is highly amenable to the investigation of gene function by biotechnological approaches. Recent advances in the development of Virus Induced Gene Silencing (VIGS) and related methods will facilitate rapid progress in the analysis of gene function in this important crop.

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10

Howe, Kevin L., Bruno Contreras-Moreira, Nishadi De Silva, Gareth Maslen, Wasiu Akanni, James Allen, Jorge Alvarez-Jarreta, et al. "Ensembl Genomes 2020—enabling non-vertebrate genomic research." Nucleic Acids Research 48, no. D1 (October 10, 2019): D689—D695. http://dx.doi.org/10.1093/nar/gkz890.

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Abstract Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the context of the Ensembl project (http://www.ensembl.org). Together, the two resources provide a consistent set of interfaces to genomic data across the tree of life, including reference genome sequence, gene models, transcriptional data, genetic variation and comparative analysis. Data may be accessed via our website, online tools platform and programmatic interfaces, with updates made four times per year (in synchrony with Ensembl). Here, we provide an overview of Ensembl Genomes, with a focus on recent developments. These include the continued growth, more robust and reproducible sets of orthologues and paralogues, and enriched views of gene expression and gene function in plants. Finally, we report on our continued deeper integration with the Ensembl project, which forms a key part of our future strategy for dealing with the increasing quantity of available genome-scale data across the tree of life.

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11

Liu, Fuyun, Yuli Li, Hongwei Yu, Lingling Zhang, Jingjie Hu, Zhenmin Bao, and Shi Wang. "MolluscDB: an integrated functional and evolutionary genomics database for the hyper-diverse animal phylum Mollusca." Nucleic Acids Research 49, no. D1 (October 22, 2020): D988—D997. http://dx.doi.org/10.1093/nar/gkaa918.

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Abstract Mollusca represents the second largest animal phylum but remains poorly explored from a genomic perspective. While the recent increase in genomic resources holds great promise for a deep understanding of molluscan biology and evolution, access and utilization of these resources still pose a challenge. Here, we present the first comprehensive molluscan genomics database, MolluscDB (http://mgbase.qnlm.ac), which compiles and integrates current molluscan genomic/transcriptomic resources and provides convenient tools for multi-level integrative and comparative genomic analyses. MolluscDB enables a systematic view of genomic information from various aspects, such as genome assembly statistics, genome phylogenies, fossil records, gene information, expression profiles, gene families, transcription factors, transposable elements and mitogenome organization information. Moreover, MolluscDB offers valuable customized datasets or resources, such as gene coexpression networks across various developmental stages and adult tissues/organs, core gene repertoires inferred for major molluscan lineages, and macrosynteny analysis for chromosomal evolution. MolluscDB presents an integrative and comprehensive genomics platform that will allow the molluscan community to cope with ever-growing genomic resources and will expedite new scientific discoveries for understanding molluscan biology and evolution.

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12

Coddington, Jonathan. "Global Genomic Resources for Biodiversity Research." Biodiversity Information Science and Standards 2 (July 19, 2018): e28440. http://dx.doi.org/10.3897/biss.2.28440.

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Genomic science is revolutionizing and accelerating biodiversity research. For collections-based institutions to continue to lead and support biodiversity research, they must adapt to this new reality. Simultaneously, “big data” is accumulating so rapidly that we have unprecedented capacity to plan strategically to use genomics to advance basic and applied science on multiple fronts. For example, seven “big data” sources (GBIF, ~1B records; BHL, ~3.6M records; NCBI, ~220M records; OToL, 1.9M records; BOLD, ~6.3M records, EOL, ~99K records, and GGBN, ~2M records) collectively offer more than 1.2B records on biodiversity. At the scale of species (~2M described, multiple millions undescribed), these data are still too sparse to permit comprehensive conclusions. At the scale of families (i.e. deeper clades of life), the situation is far more promising: about 9,911 families are known, and relatively few are discovered each year. This suggests that at the family rank (and above), our knowledge of life on Earth is reasonably complete. Approximately 160,000 genera are known, but certainly many new genera await discovery and description, although fewer than new species, and more than new families. Genomics is the fastest way to “bin” species into more inclusive lineages such as genera and families, and is certainly faster than traditional alpha taxonomy. Synergistically, these “big data” answer four important questions at deeper clade levels: What is it? Where is it? What do we know about it? What do we know about its genome? The converse of what we know is what we do not know, another meaning of “dark taxa.” We can use the distribution and density of big data at deeper clade levels (families, genera) quantitatively to analyze “dark taxa,” and therefore to optimize strategically knowledge and preservation of biodiversity at a global scale. Technicalities of the quantitative prioritization scheme are debatable, but some initial, simple scoring systems can help to prioritize lineages for collection and genetic research so as to most efficiently “illuminate” regions in the tree of life that that are neither preserved, imaged, geolocated, studied, nor known genomically. This analysis presents criteria and goals for collaborating to build a global genomic collection to maximize efficient acquisition of biodiversity genomic knowledge.

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OIKAWA, Hideaki. "Chemistry Based on Genomic Resources." TRENDS IN THE SCIENCES 16, no. 5 (2011): 66–69. http://dx.doi.org/10.5363/tits.16.5_66.

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Walter, Ronald B., Zhenlin Ju, Al Martinez, Chris Amemiya, and Paul B. Samollow. "Genomic Resources for Xiphophorus Research." Zebrafish 3, no. 1 (March 2006): 11–22. http://dx.doi.org/10.1089/zeb.2006.3.11.

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KUMAR, J., A. PRATAP, R. K. SOLANKI, D. S. GUPTA, A. GOYAL, S. K. CHATURVEDI, N. NADARAJAN, and S. KUMAR. "Genomic resources for improving food legume crops." Journal of Agricultural Science 150, no. 3 (June 30, 2011): 289–318. http://dx.doi.org/10.1017/s0021859611000554.

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SUMMARYFood legumes are the main source of dietary protein for a large part of the world's population, and also play an important role in maintaining soil fertility through nitrogen fixation. However, legume yields and production are often limited by large genotype×environment (G×E) interactions that influence the expression of agronomically important, complex quantitative traits. Consequently, genetic improvement has been slower than expected. Molecular marker technology enables genetic dissection of such complex traits, allowing breeders to identify genomic regions on the chromosome that have main effects or interactive effects. A number of genomic resources have been developed in several legume species during the last two decades, and provide a platform for exploiting marker technology. The present paper reviews the available genomic resources in food legumes: linkage maps, high-throughput sequencing technologies, expression sequence tag (EST) databases, genome sequences, DNA chips, targeting induced local lesions in genomes (TILLING), bacterial artificial chromosome (BAC) libraries and others. It also describes how these resources are being used to tag and map genes/quantitative trait loci (QTLs) for domesticated and other agronomically important traits. This information is important to genetic improvement efforts aiming at improving food and nutrition security worldwide.

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Richards, Stephen, Anna Childers, and Christopher Childers. "Editorial overview: Insect genomics: Arthropod genomic resources for the 21st century." Current Opinion in Insect Science 25 (February 2018): iv—vii. http://dx.doi.org/10.1016/j.cois.2018.02.015.

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Liu, J. "Genomic resources and their informatic analysis for comparative genomics in catfish." Aquaculture 272 (2007): S286. http://dx.doi.org/10.1016/j.aquaculture.2007.07.128.

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Diniz, Augusto L., Sávio S. Ferreira, Felipe ten-Caten, Gabriel R. A. Margarido, João M. dos Santos, Geraldo V. de S. Barbosa, Monalisa S. Carneiro, and Glaucia M. Souza. "Genomic resources for energy cane breeding in the post genomics era." Computational and Structural Biotechnology Journal 17 (2019): 1404–14. http://dx.doi.org/10.1016/j.csbj.2019.10.006.

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Wambugu, Peterson W., Marie-Noelle Ndjiondjop, and Robert Henry. "Advances in Molecular Genetics and Genomics of African Rice (Oryza glaberrima Steud)." Plants 8, no. 10 (September 26, 2019): 376. http://dx.doi.org/10.3390/plants8100376.

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African rice (Oryza glaberrima) has a pool of genes for resistance to diverse biotic and abiotic stresses, making it an important genetic resource for rice improvement. African rice has potential for breeding for climate resilience and adapting rice cultivation to climate change. Over the last decade, there have been tremendous technological and analytical advances in genomics that have dramatically altered the landscape of rice research. Here we review the remarkable advances in knowledge that have been witnessed in the last few years in the area of genetics and genomics of African rice. Advances in cheap DNA sequencing technologies have fuelled development of numerous genomic and transcriptomic resources. Genomics has been pivotal in elucidating the genetic architecture of important traits thereby providing a basis for unlocking important trait variation. Whole genome re-sequencing studies have provided great insights on the domestication process, though key studies continue giving conflicting conclusions and theories. However, the genomic resources of African rice appear to be under-utilized as there seems to be little evidence that these vast resources are being productively exploited for example in practical rice improvement programmes. Challenges in deploying African rice genetic resources in rice improvement and the genomics efforts made in addressing them are highlighted.

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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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Tonkin, Emma, Kathleen Calzone, Jean Jenkins, Dale Lea, and Cynthia Prows. "Genomic Education Resources for Nursing Faculty." Journal of Nursing Scholarship 43, no. 4 (October 28, 2011): 330–40. http://dx.doi.org/10.1111/j.1547-5069.2011.01415.x.

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Kreppel, L. "Genomic database resources for Dictyostelium discoideum." Nucleic Acids Research 30, no. 1 (January 1, 2002): 84–86. http://dx.doi.org/10.1093/nar/30.1.84.

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Kane, Nolan C., John M. Burke, Laura Marek, Gerald Seiler, Felicity Vear, Gregory Baute, Steven J. Knapp, Patrick Vincourt, and Loren H. Rieseberg. "Sunflower genetic, genomic and ecological resources." Molecular Ecology Resources 13, no. 1 (October 8, 2012): 10–20. http://dx.doi.org/10.1111/1755-0998.12023.

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Vize, Peter D., and Aaron M. Zorn. "Xenopus genomic data and browser resources." Developmental Biology 426, no. 2 (June 2017): 194–99. http://dx.doi.org/10.1016/j.ydbio.2016.03.030.

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de Magalhaes, J. P. "HAGR: the Human Ageing Genomic Resources." Nucleic Acids Research 33, Database issue (December 17, 2004): D537—D543. http://dx.doi.org/10.1093/nar/gki017.

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Garmier, Marie, Laurent Gentzbittel, Jiangqi Wen, Kirankumar S. Mysore, and Pascal Ratet. "Medicago truncatula : Genetic and Genomic Resources." Current Protocols in Plant Biology 2, no. 4 (December 2017): 318–49. http://dx.doi.org/10.1002/cppb.20058.

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Kui, Ling, Zhe Zhang, Yangzi Wang, Yesheng Zhang, Shiming Li, Xiao Dong, Qiuju Xia, Jun Sheng, Jian Wang, and Yang Dong. "Genome Assembly and Analyses of the Macrofungus Macrocybe gigantea." BioMed Research International 2021 (January 18, 2021): 1–14. http://dx.doi.org/10.1155/2021/6656365.

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Macrocybe gigantea (M. gigantea) is a macrofungus genus that contains a big number of fairly fleshy gilled mushrooms with white spores. This macrofungus produces diverse bioactive compounds, antioxidants, and water-soluble polysaccharides. However, the genomic resources of this species remain unknown. Here, we assembled the genome of M. gigantea (41.23 Mb) into 336 scaffolds with a N50 size of 374,455 bp and compared it with the genomes of eleven other macrofungi. Comparative genomics study confirmed that M. gigantea belonged to the Macrocybe genus, a stand-alone genus different from the Tricholoma genus. In addition, we found that glycosyl hydrolase family 28 (GH28) in M. gigantea shared conserved motifs that were significantly different from their counterparts in Tricholoma. The genomic resource uncovered by this study will enhance our understanding of fungi biology, especially the differences in their growth rates and energy metabolism.

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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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Ryu, Borim, Soo-Yong Shin, Rong-Min Baek, Jeong-Whun Kim, Eunyoung Heo, Inchul Kang, Joshua SungWoo Yang, and Sooyoung Yoo. "Clinical Genomic Sequencing Reports in Electronic Health Record Systems Based on International Standards: Implementation Study." Journal of Medical Internet Research 22, no. 8 (August 10, 2020): e15040. http://dx.doi.org/10.2196/15040.

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Background To implement standardized machine-processable clinical sequencing reports in an electronic health record (EHR) system, the International Organization for Standardization Technical Specification (ISO/TS) 20428 international standard was proposed for a structured template. However, there are no standard implementation guidelines for data items from the proposed standard at the clinical site and no guidelines or references for implementing gene sequencing data results for clinical use. This is a significant challenge for implementation and application of these standards at individual sites. Objective This study examines the field utilization of genetic test reports by designing the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) for genomic data elements based on the ISO/TS 20428 standard published as the standard for genomic test reports. The goal of this pilot is to facilitate the reporting and viewing of genomic data for clinical applications. FHIR Genomics resources predominantly focus on transmitting or representing sequencing data, which is of less clinical value. Methods In this study, we describe the practical implementation of ISO/TS 20428 using HL7 FHIR Genomics implementation guidance to efficiently deliver the required genomic sequencing results to clinicians through an EHR system. Results We successfully administered a structured genomic sequencing report in a tertiary hospital in Korea based on international standards. In total, 90 FHIR resources were used. Among 41 resources for the required fields, 26 were reused and 15 were extended. For the optional fields, 28 were reused and 21 were extended. Conclusions To share and apply genomic sequencing data in both clinical practice and translational research, it is essential to identify the applicability of the standard-based information system in a practical setting. This prototyping work shows that reporting data from clinical genomics sequencing can be effectively implemented into an EHR system using the existing ISO/TS 20428 standard and FHIR resources.

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Wang, Jianbin. "Genomics of the Parasitic Nematode Ascaris and Its Relatives." Genes 12, no. 4 (March 28, 2021): 493. http://dx.doi.org/10.3390/genes12040493.

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Nematodes of the genus Ascaris are important parasites of humans and swine, and the phylogenetically related genera (Parascaris, Toxocara, and Baylisascaris) infect mammals of veterinary interest. Over the last decade, considerable genomic resources have been established for Ascaris, including complete germline and somatic genomes, comprehensive mRNA and small RNA transcriptomes, as well as genome-wide histone and chromatin data. These datasets provide a major resource for studies on the basic biology of these parasites and the host–parasite relationship. Ascaris and its relatives undergo programmed DNA elimination, a highly regulated process where chromosomes are fragmented and portions of the genome are lost in embryonic cells destined to adopt a somatic fate, whereas the genome remains intact in germ cells. Unlike many model organisms, Ascaris transcription drives early development beginning prior to pronuclear fusion. Studies on Ascaris demonstrated a complex small RNA network even in the absence of a piRNA pathway. Comparative genomics of these ascarids has provided perspectives on nematode sex chromosome evolution, programmed DNA elimination, and host–parasite coevolution. The genomic resources enable comparison of proteins across diverse species, revealing many new potential drug targets that could be used to control these parasitic nematodes.

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Yasuike, Motoshige, Jong Leong, Stuart G. Jantzen, Kristian R. von Schalburg, Frank Nilsen, Simon R. M. Jones, and Ben F. Koop. "Genomic Resources for Sea Lice: Analysis of ESTs and Mitochondrial Genomes." Marine Biotechnology 14, no. 2 (July 12, 2011): 155–66. http://dx.doi.org/10.1007/s10126-011-9398-z.

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Gardner, Brittany, Michelle Doose, Janeth I. Sanchez, Andrew N. Freedman, and Janet S. de Moor. "Distribution of Genomic Testing Resources by Oncology Practice and Rurality: A Nationally Representative Study." JCO Precision Oncology, no. 5 (June 2021): 1060–68. http://dx.doi.org/10.1200/po.21.00109.

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PURPOSE Oncologists are increasingly using molecular profiling to inform personalized patient treatment decisions. Despite its promising utility, the integration of genomic testing into diverse clinical health care settings across geographic settings has been understudied. METHODS We used data from the National Survey of Precision Medicine in Cancer Treatment, a nationally representative sample of practicing US oncologists, to assess the availability of six genomic testing resources, including on-site pathology, contracts with outside laboratories, on-site genetic counselors, internal policies or protocols for using genomic and biomarker testing, electronic medical record alerts, and genomic or molecular tumor boards. We used multivariate logistic regression models to examine differences in the availability of each genomic testing resource by practice type and rurality while adjusting for payer mix and patient volume. RESULTS A larger proportion of multispecialty group and academic practices had genomic testing resources available compared with solo and nonacademic practices. Electronic medical record alerts were the least available resource, whereas contracts with outside laboratories were the most available resource. Compared with urban practices, there were significantly fewer practices located in rural areas that had on-site pathology, on-site genetic counselors, protocols for genomic tests, and molecular tumor boards. CONCLUSION Genomic testing resources varied by practice type and geography among a nationally representative sample of practicing oncologists. This variation has important implications for the development of interventions and policies to support the more equitable delivery of precision oncology to patients with cancer.

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Smith, Timothy P. "226 Genomics in animal agriculture: current technologies and applications." Journal of Animal Science 97, Supplement_3 (December 2019): 55–56. http://dx.doi.org/10.1093/jas/skz258.113.

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Abstract The early impact of genomic research on animal agriculture was relatively modest, as it proved difficult to translate quantitative trait loci mapping to industrial application. Fortunately, developments in technology have facilitated the application of genomics to animal agriculture, which has led to more substantial impacts on many commercially produced animal species. A brief look back on the history of genomic research will be presented, followed by an overview of recent developments in genomic technologies. Examples of application of genomic research, focusing on beef cattle and comparative genomics with other bovinae specie, and the current status of some new genomic resources emerging for sheep, pigs, and goats, will also be presented.

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Soanes, Darren M., Wendy Skinner, John Keon, John Hargreaves, and Nicholas J. Talbot. "Genomics of Phytopathogenic Fungi and the Development of Bioinformatic Resources." Molecular Plant-Microbe Interactions® 15, no. 5 (May 2002): 421–27. http://dx.doi.org/10.1094/mpmi.2002.15.5.421.

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Genomic resources available to researchers studying phytopathogenic fungi are limited. Here, we briefly review the genomic and bioinformatic resources available and the current status of fungal genomics. We also describe a relational database containing sequences of expressed sequence tags (ESTs) from three phytopathogenic fungi, Blumeria graminis, Magnaporthe grisea, and Mycosphaerella graminicola, and the methods and underlying principles required for its construction. The database contains significant annotation for each EST sequence and is accessible at http://cogeme.ex.ac.uk . An easy-to-use interface allows the user to identify gene sequences by using simple text queries or homology searches. New querying functions and large sequence sets from a variety of phytopathogenic species will be incorporated in due course.

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35

Jin, Y. H., C. P. Hong, S. R. Choi, Y. P. Lim, and G. S. Sin. "KOREA BRASSICA GENOME RESOURCE BANK (KBGRB): INTEGRATED GENOMIC RESOURCES OF BRASSICA SPECIES." Acta Horticulturae, no. 760 (July 2007): 77–81. http://dx.doi.org/10.17660/actahortic.2007.760.8.

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36

Rogers, Jeffrey. "Genomic resources for rhesus macaques (Macaca mulatta)." Mammalian Genome 33, no. 1 (January 9, 2022): 91–99. http://dx.doi.org/10.1007/s00335-021-09922-z.

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37

Callicrate, Taylor, Rebecca Dikow, James W. Thomas, James C. Mullikin, Erich D. Jarvis, and Robert C. Fleischer. "Genomic resources for the endangered Hawaiian honeycreepers." BMC Genomics 15, no. 1 (2014): 1098. http://dx.doi.org/10.1186/1471-2164-15-1098.

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Ali, Muhammad Waqas, and Philippa Borrill. "Applying genomic resources to accelerate wheat biofortification." Heredity 125, no. 6 (June 11, 2020): 386–95. http://dx.doi.org/10.1038/s41437-020-0326-8.

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39

Saha, Dipnarayan, M. V. Channabyre Gowda, Lalit Arya, Manjusha Verma, and Kailash C. Bansal. "Genetic and Genomic Resources of Small Millets." Critical Reviews in Plant Sciences 35, no. 1 (January 2, 2016): 56–79. http://dx.doi.org/10.1080/07352689.2016.1147907.

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40

Curci, Pasquale L., Domenico De Paola, and Gabriella Sonnante. "Development of chloroplast genomic resources for Cynara." Molecular Ecology Resources 16, no. 2 (September 10, 2015): 562–73. http://dx.doi.org/10.1111/1755-0998.12457.

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41

Pathak, Ajey Kumar, Iliyas Rashid, Naresh Sahebrao Nagpure, Ravindra Kumar, Rameshwar Pati, Mahender Singh, S. Murali, Basdeo Kushwaha, Dinesh Kumar, and Anil Rai. "FisOmics: A portal of fish genomic resources." Genomics 111, no. 6 (December 2019): 1923–28. http://dx.doi.org/10.1016/j.ygeno.2019.01.003.

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42

Varshney, Rajeev K., Jean-Christophe Glaszmann, Hei Leung, and Jean-Marcel Ribaut. "More genomic resources for less-studied crops." Trends in Biotechnology 28, no. 9 (September 2010): 452–60. http://dx.doi.org/10.1016/j.tibtech.2010.06.007.

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43

González, Vanessa L., Amanda M. Devine, Mike Trizna, Daniel G. Mulcahy, Katharine B. Barker, and Jonathan A. Coddington. "Open access genomic resources for terrestrial arthropods." Current Opinion in Insect Science 25 (February 2018): 91–98. http://dx.doi.org/10.1016/j.cois.2017.12.003.

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44

Davila, A. M. R., D. M. Lorenzini, P. N. Mendes, T. S. Satake, G. R. Sousa, L. M. Campos, C. J. Mazzoni, et al. "GARSA: genomic analysis resources for sequence annotation." Bioinformatics 21, no. 23 (October 6, 2005): 4302–3. http://dx.doi.org/10.1093/bioinformatics/bti705.

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45

Gauthier, J. P., F. Legeai, A. Zasadzinski, C. Rispe, and D. Tagu. "AphidBase: a database for aphid genomic resources." Bioinformatics 23, no. 6 (January 19, 2007): 783–84. http://dx.doi.org/10.1093/bioinformatics/btl682.

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46

Sikorski, R. "Genomic medicine. Internet resources for medical genetics." JAMA: The Journal of the American Medical Association 278, no. 15 (October 15, 1997): 1212b—1213. http://dx.doi.org/10.1001/jama.278.15.1212b.

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Smit, Amelia K., Ainsley J. Newson, Louise Keogh, Megan Best, Kate Dunlop, Kylie Vuong, Judy Kirk, Phyllis Butow, Lyndal Trevena, and Anne E. Cust. "GP attitudes to and expectations for providing personal genomic risk information to the public: a qualitative study." BJGP Open 3, no. 1 (February 19, 2019): bjgpopen18X101633. http://dx.doi.org/10.3399/bjgpopen18x101633.

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BackgroundAs part of a pilot randomised controlled trial examining the impact of personal melanoma genomic risk information on behavioural and psychosocial outcomes, GPs were sent a booklet containing their patient’s genomic risk of melanoma.AimUsing this booklet as an example of genomic risk information that might be offered on a population-level in the future, this study explored GP attitudes towards communicating genomic risk information and resources needed to support this process.Design & settingSemi-structured interviews were conducted with 22 Australian GPs.MethodThe interviews were recorded and transcribed, and data were analysed thematically.ResultsGPs in this sample believed that communicating genomic risk may become a responsibility within primary care and they recommended a shared decisionmaking approach to guide the testing process. Factors were identified that may influence how and when GPs communicate genomic risk information. GPs view genomics-based risk as one of many disease risk factors and feel that this type of information could be applied in practice in the context of overall risk assessment for diseases for which prevention and early detection strategies are available. They believe it is important to ensure that patients understand their genomic risk and do not experience long-term adverse psychological responses. GPs desire clinical practice guidelines that specify recommendations for genomic risk assessment and patient management, point-of-care resources, and risk prediction tools that include genomic and traditional risk factors.ConclusionThese findings will inform the development of resources for preparing GPs to manage and implement genomic risk information in practice.

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Caurcel, Carlos, Dominik R. Laetsch, Richard Challis, Sujai Kumar, Karim Gharbi, and Mark Blaxter. "MolluscDB: a genome and transcriptome database for molluscs." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1825 (April 5, 2021): 20200157. http://dx.doi.org/10.1098/rstb.2020.0157.

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As sequencing becomes more accessible and affordable, the analysis of genomic and transcriptomic data has become a cornerstone of many research initiatives. Communities with a focus on particular taxa or ecosystems need solutions capable of aggregating genomic resources and serving them in a standardized and analysis-friendly manner. Taxon-focussed resources can be more flexible in addressing the needs of a research community than can universal or general databases. Here, we present MolluscDB, a genome and transcriptome database for molluscs. MolluscDB offers a rich ecosystem of tools, including an Ensembl browser, a BLAST server for homology searches and an HTTP server from which any dataset present in the database can be downloaded. To demonstrate the utility of the database and verify the quality of its data, we imported data from assembled genomes and transcriptomes of 22 species, estimated the phylogeny of Mollusca using single-copy orthologues, explored patterns of gene family size change and interrogated the data for biomineralization-associated enzymes and shell matrix proteins. MolluscDB provides an easy-to-use and openly accessible data resource for the research community. This article is part of the Theo Murphy meeting issue ‘Molluscan genomics: broad insights and future directions for a neglected phylum’.

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Irizarry, Kristopher J. L., Doug Bryant, Jordan Kalish, Curtis Eng, Peggy L. Schmidt, Gini Barrett, and Margaret C. Barr. "Integrating Genomic Data Sets for Knowledge Discovery: An Informed Approach to Management of Captive Endangered Species." International Journal of Genomics 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2374610.

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Many endangered captive populations exhibit reduced genetic diversity resulting in health issues that impact reproductive fitness and quality of life. Numerous cost effective genomic sequencing and genotyping technologies provide unparalleled opportunity for incorporating genomics knowledge in management of endangered species. Genomic data, such as sequence data, transcriptome data, and genotyping data, provide critical information about a captive population that, when leveraged correctly, can be utilized to maximize population genetic variation while simultaneously reducing unintended introduction or propagation of undesirable phenotypes. Current approaches aimed at managing endangered captive populations utilize species survival plans (SSPs) that rely upon mean kinship estimates to maximize genetic diversity while simultaneously avoiding artificial selection in the breeding program. However, as genomic resources increase for each endangered species, the potential knowledge available for management also increases. Unlike model organisms in which considerable scientific resources are used to experimentally validate genotype-phenotype relationships, endangered species typically lack the necessary sample sizes and economic resources required for such studies. Even so, in the absence of experimentally verified genetic discoveries, genomics data still provides value. In fact, bioinformatics and comparative genomics approaches offer mechanisms for translating these raw genomics data sets into integrated knowledge that enable an informed approach to endangered species management.

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Van, Kyujung, Dong Hyun Kim, Jin Hee Shin, and Suk-Ha Lee. "Genomics of plant genetic resources: past, present and future." Plant Genetic Resources 9, no. 2 (March 15, 2011): 155–58. http://dx.doi.org/10.1017/s1479262111000098.

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Plant genetic resources (PGR) include cultivars, landraces, wild species closely related to cultivated varieties, breeder's elite lines and mutants. The loss of genetic diversity caused by the practice of agriculture and the availability of genetic information has resulted in a great effort dedicated to the collection of PGR. Prior to the advent of molecular profiling, accessions in germplasm collections were examined based on morphology. The development of molecular techniques now allows a more accurate analysis of large collections. Next-generation sequencing (NGS) with de novo assembly and resequencing has already provided a substantial amount of information, which warrants the coordination of existing databases and their integration into genebanks. Thus, the integration and coordination of genomic data into genebanks is very important and requires an international effort. From the determination of phenotypic traits to the application of NGS to whole genomes, every aspect of genomics will have a great impact not only on PGR conservation, but also on plant breeding programmes.

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