13 results on '"Schriml, Lynn M"'
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2. Additional file 2 of A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology
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He, Yongqun, Yu, Hong, Huffman, Anthony, Lin, Asiyah Yu, Natale, Darren A., Beverley, John, Zheng, Ling, Perl, Yehoshua, Wang, Zhigang, Liu, Yingtong, Ong, Edison, Wang, Yang, Huang, Philip, Tran, Long, Du, Jinyang, Shah, Zalan, Shah, Easheta, Desai, Roshan, Huang, Hsin-hui, Tian, Yujia, Merrell, Eric, Duncan, William D., Arabandi, Sivaram, Schriml, Lynn M., Zheng, Jie, Masci, Anna Maria, Wang, Liwei, Liu, Hongfang, Smaili, Fatima Zohra, Hoehndorf, Robert, Pendlington, Zoë May, Roncaglia, Paola, Ye, Xianwei, Xie, Jiangan, Tang, Yi-Wei, Yang, Xiaolin, Peng, Suyuan, Zhang, Luxia, Chen, Luonan, Hur, Junguk, Omenn, Gilbert S., Athey, Brian, and Smith, Barry
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Additional file 2: Supplemental Table 1. Resources used for our coronavirus disease-related data collection.
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- 2022
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3. Additional file 1 of A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology
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He, Yongqun, Yu, Hong, Huffman, Anthony, Lin, Asiyah Yu, Natale, Darren A., Beverley, John, Zheng, Ling, Perl, Yehoshua, Wang, Zhigang, Liu, Yingtong, Ong, Edison, Wang, Yang, Huang, Philip, Tran, Long, Du, Jinyang, Shah, Zalan, Shah, Easheta, Desai, Roshan, Huang, Hsin-hui, Tian, Yujia, Merrell, Eric, Duncan, William D., Arabandi, Sivaram, Schriml, Lynn M., Zheng, Jie, Masci, Anna Maria, Wang, Liwei, Liu, Hongfang, Smaili, Fatima Zohra, Hoehndorf, Robert, Pendlington, Zoë May, Roncaglia, Paola, Ye, Xianwei, Xie, Jiangan, Tang, Yi-Wei, Yang, Xiaolin, Peng, Suyuan, Zhang, Luxia, Chen, Luonan, Hur, Junguk, Omenn, Gilbert S., Athey, Brian, and Smith, Barry
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Additional file 1: Supplemental file 1. Visualization of the Evolution of CIDO.
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- 2022
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4. Additional file 3 of A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology
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He, Yongqun, Yu, Hong, Huffman, Anthony, Lin, Asiyah Yu, Natale, Darren A., Beverley, John, Zheng, Ling, Perl, Yehoshua, Wang, Zhigang, Liu, Yingtong, Ong, Edison, Wang, Yang, Huang, Philip, Tran, Long, Du, Jinyang, Shah, Zalan, Shah, Easheta, Desai, Roshan, Huang, Hsin-hui, Tian, Yujia, Merrell, Eric, Duncan, William D., Arabandi, Sivaram, Schriml, Lynn M., Zheng, Jie, Masci, Anna Maria, Wang, Liwei, Liu, Hongfang, Smaili, Fatima Zohra, Hoehndorf, Robert, Pendlington, Zoë May, Roncaglia, Paola, Ye, Xianwei, Xie, Jiangan, Tang, Yi-Wei, Yang, Xiaolin, Peng, Suyuan, Zhang, Luxia, Chen, Luonan, Hur, Junguk, Omenn, Gilbert S., Athey, Brian, and Smith, Barry
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Additional file 3: Supplemental Table 2. CIDO statistics including terms imported from major reference ontologies.
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- 2022
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5. Wikidata as a knowledge graph for the life sciences
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Waagmeester, Andra, Stupp, Gregory, Burgstaller-Muehlbacher, Sebastian, Good, Benjamin M, Griffith, Malachi, Obi L Griffith, Hanspers, Kristina, Hermjakob, Henning, Hudson, Toby S, Hybiske, Kevin, Keating, Sarah M, Manske, Magnus, Mayers, Michael, Mietchen, Daniel, Mitraka, Elvira, Pico, Alexander R, Putman, Timothy, Riutta, Anders, Queralt-Rosinach, Nuria, Schriml, Lynn M, Shafee, Thomas, Slenter, Denise, Stephan, Ralf, Thornton, Katherine, Tsueng, Ginger, Tu, Roger, Ul-Hasan, Sabah, Willighagen, Egon, and Chunlei Wu
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ComputingMethodologies_PATTERNRECOGNITION ,Uncategorized - Abstract
© Waagmeester et al. Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.
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- 2021
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6. A global metagenomic map of urban microbiomes and antimicrobial resistance
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Danko, David, Bezdan, Daniela, Afshin, Evan E., Ahsanuddin, Sofia, Bhattacharya, Chandrima, Butler, Daniel J., Chng, Kern Rei, Donnellan, Daisy, Hecht, Jochen, Jackson, Katelyn, Kuchin, Katerina, Karasikov, Mikhail, Lyons, Abigail, Mak, Lauren, Meleshko, Dmitry, Mustafa, Harun, Mutai, Beth, Neches, Russell Y., Ng, Amanda, Nikolayeva, Olga, Nikolayeva, Tatyana, Png, Eileen, Ryon, Krista A., Sanchez, Jorge L., Shaaban, Heba, Sierra, Maria A., Thomas, Dominique, Young, Ben, Abudayyeh, Omar O., Alicea, Josue, Bhattacharyya, Malay, Blekhman, Ran, Castro-Nallar, Eduardo, Canas, Ana M., Chatziefthimiou, Aspassia D., Crawford, Robert W., De Filippis, Francesca, Deng, Youping, Desnues, Christelle, Dias-Neto, Emmanuel, Dybwad, Marius, Elhaik, Eran, Ercolini, Danilo, Frolova, Alina, Gankin, Dennis, Gootenberg, Jonathan S., Graf, Alexandra B., Green, David C., Hajirasouliha, Iman, Hastings, Jaden J. A., Hernandez, Mark, Iraola, Gregorio, Kahles, Andre, Kelly, Frank J., Knights, Kaymisha, Kyrpides, Nikos C., Labaj, Pawel P., Lee, Patrick K. H., Leung, Marcus H. Y., Ljungdahl, Per O., Mason-Buck, Gabriella, McGrath, Ken, Meydan, Cem, Mongodin, Emmanuel F., Moraes, Milton Ozorio, Nagarajan, Niranjan, Nieto-Caballero, Marina, Noushmehr, Houtan, Oliveira, Manuela, Ossowski, Stephan, Osuolale, Olayinka O., Ozcan, Orhan, Paez-Espino, David, Rascovan, Nicolas, Richard, Hugues, Ratsch, Gunnar, Schriml, Lynn M., Semmler, Torsten, Sezerman, Osman U., Shi, Leming, Shi, Tieliu, Song, Le Huu, Suzuki, Haruo, Court, Denise Syndercombe, Tighe, Scott W., Tong, Xinzhao, Udekwu, Klas, Ugalde, Juan A., Valentine, Brandon, Vassilev, Dimitar, Vayndorf, Elena M., Velavan, Thirumalaisamy P., Wu, Jun, Zambrano, Maria M., Zhu, Jifeng, Zhu, Sibo, Mason, Christopher E., Jang, Soojin, and Siam, Rania
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Bioinformatics and Systems Biology (methods development to be 10203) - Abstract
We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.
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- 2021
7. SCIENCE FORUM Wikidata as a knowledge graph for the life sciences
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Waagmeester, Andra, Stupp, Gregory, Burgstaller-muehlbacher, Sebastian, Good, Benjamin M, Griffith, Malachi, Griffith, Obi L, Hanspers, Kristina, Hermjakob, Henning, Hudson, Toby S, Hybiske, Kevin, Keating, Sarah M, Manske, Magnus, Mayers, Michael, Mietchen, Daniel, Mitraka, Elvira, Pico, Alexander R, Putman, Timothy, Riutta, Anders, Queralt-rosinach, Nuria, Schriml, Lynn M, Shafee, Thomas, Slenter, Denise, Stephan, Ralf, Thornton, Katherine, Tsueng, Ginger, Tu, Roger, Ul-hasan, Sabah, Willighagen, Egon, Wu, Chunlei, Su, Andrew I, Bioinformatica, and RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health
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DISORDER ,ComputingMethodologies_PATTERNRECOGNITION - Abstract
Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.
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- 2020
8. A harmonized meta-knowledgebase of clinical interpretations of cancer genomic variants
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Wagner, Alex H, Walsh, Brian, Mayfield, Georgia, Tamborero, David, Sonkin, Dmitriy, Krysiak, Kilannin, Pons, Jordi Deu, Duren, Ryan P, Gao, Jianjiong, McMurry, Julie, Patterson, Sara, Del Vecchio Fitz, Catherine, Sezerman, Ozman U, Warner, Jeremy L, Rieke, Damian T, Aittokallio, Tero, Cerami, Ethan, Ritter, Deborah, Schriml, Lynn M, Freimuth, Robert R, Haendel, Melissa, Raca, Gordana, Madhavan, Subha, Baudis, Michael, Beckmann, Jacques S, Dienstmann, Rodrigo, Chakravarty, Debyani, Li, Xuan Shirley, Mockus, Susan, Elemento, Olivier, Schultz, Nikolaus, Lopez-Bigas, Nuria, Lawler, Mark, Goecks, Jeremy, Griffith, Malachi, Griffith, Obi L, and Margolin, Adam A
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Structure (mathematical logic) ,0303 health sciences ,Matching (statistics) ,Interpretation (philosophy) ,Cancer ,Genomics ,Computational biology ,Biology ,medicine.disease ,3. Good health ,03 medical and health sciences ,0302 clinical medicine ,Precision oncology ,030220 oncology & carcinogenesis ,medicine ,Relevance (information retrieval) ,030304 developmental biology - Abstract
Precision oncology relies on the accurate discovery and interpretation of genomic variants to enable individualized diagnosis, prognosis, and therapy selection. We found that knowledgebases containing clinical interpretations of somatic cancer variants are highly disparate in interpretation content, structure, and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. With the cooperation of experts of the Global Alliance for Genomics and Health (GA4GH) and six prominent cancer variant knowledgebases, we developed a framework for aggregating and harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations covering 3,437 unique variants in 415 genes, 357 diseases, and 791 drugs. We demonstrated large gains in overlap between resources across variants, diseases, and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 56% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide an open and freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.
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- 2018
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9. Disease Ontology: improving and unifying disease annotations across species
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Bello, Susan M., Shimoyama, Mary, Mitraka, Elvira, Laulederkind, Stanley J. F., Smith, Cynthia L., Eppig, Janan T., and Schriml, Lynn M.
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Mouse ,lcsh:R ,lcsh:Medicine ,Molecular Sequence Annotation ,Disease models ,Rats ,Mice ,Gene Ontology ,Species Specificity ,Databases, Genetic ,lcsh:Pathology ,Ontologies ,Rat ,Animals ,Disease ,Resource Article ,lcsh:RB1-214 - Abstract
Model organisms are vital to uncovering the mechanisms of human disease and developing new therapeutic tools. Researchers collecting and integrating relevant model organism and/or human data often apply disparate terminologies (vocabularies and ontologies), making comparisons and inferences difficult. A unified disease ontology is required that connects data annotated using diverse disease terminologies, and in which the terminology relationships are continuously maintained. The Mouse Genome Database (MGD, http://www.informatics.jax.org), Rat Genome Database (RGD, http://rgd.mcw.edu) and Disease Ontology (DO, http://www.disease-ontology.org) projects are collaborating to augment DO, aligning and incorporating disease terms used by MGD and RGD, and improving DO as a tool for unifying disease annotations across species. Coordinated assessment of MGD's and RGD's disease term annotations identified new terms that enhance DO's representation of human diseases. Expansion of DO term content and cross-references to clinical vocabularies (e.g. OMIM, ORDO, MeSH) has enriched the DO's domain coverage and utility for annotating many types of data generated from experimental and clinical investigations. The extension of anatomy-based DO classification structure of disease improves accessibility of terms and facilitates application of DO for computational research. A consistent representation of disease associations across data types from cellular to whole organism, generated from clinical and model organism studies, will promote the integration, mining and comparative analysis of these data. The coordinated enrichment of the DO and adoption of DO by MGD and RGD demonstrates DO's usability across human data, MGD, RGD and the rest of the model organism database community., Summary: Analyzing diverse disease data requires a comprehensive, robust disease ontology to integrate annotations and retrieve accurate, interpretable results. MGD, RGD and DO are working in collaboration to achieve this goal.
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- 2018
10. The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium inaugural meeting report
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Afshinnekoo, Ebrahim, Ahsanuddin, Sofia, Ghedin, Elodie, Read, Timothy, Fraser, Claire, Dudley, Joel, Bowler, Christopher, Mason, Christopher E., Chernomoretz, Ariel, Stolovitzky, Gustavo, Łabaj, Paweł P., Graf, Alexandra B., Darling, Aaron, Burke, Catherine, Noushmehr, Houtan, Dias-Neto, Emmanuel, Guo, Yongli, Xie, Zhi, Lee, Patrick K. H., Shi, Leming, Ruiz-Perez, Carlos A., Zambrano, Maria Mercedes, Siam, Rania, Ouf, Amged, Richard, Hugues, Lafontaine, Ingrid, Wieler, Lothar H., Semmler, Torsten, Prithiviraj, Bharath, Nedunuri, Narasimha, Mehr, Shaadi, Banihashemi, Kambiz, Lista, Florigio, Anselmo, Anna, Suzuki, Haruo, Kuroda, Makoto, Yamashita, Riu, Sato, Yukoto, Kaminuma, Eli, Aranda, Celia M. Alpuche, Martinez, Jesus, Dada, Christopher, Dybwad, Marius, Oliveira, Manuela, Schuster, Stephan, Siwo, Geoffrey H., Jang, Soojin, Seo, Sung Chul, Hwang, Sung Ho, Ossowski, Stephan, Bezdan, Daniela, Chaker, Salama, Chatziefthimiou, Aspassia D., Udekwu, Klas, Liungdahl, Per, Sezerman, Ugur, Meydan, Cem, Elhaik, Eran, Gonnet, Gaston, Schriml, Lynn M., Mongodin, Emmanuel, Huttenhower, Curtis, Gilbert, Jack, Eisen, Jonathan, Hirschberg, David, Hernandez, Mark, McGrath, Ken, McGrath, Leanne, Gray, Andrew, Osuolale, Olayinka, Segata, Nicola, Fillo, Silvia, Iraola, Gregorio, Zhou, Yiming, Chang, Yujun, Li, Yang, Zhend, Yuanting, Hou, Wanwan, Ramirez, Adan, Cepeda, Martha, Desnues, Christelle, Rascovan, Nicolas, Baron, Colin, Nagarajan, Niranjan, Ercolini, Danilo, Menary, Wayne, Tighe, Scott, Donia, Mohamed, Levy, Shawn, Benito, Joseph, Jones, Angela, Kasarskis, Andrew, Maritz, Julia, Jorgensen, Ellen, Neches, Russell, Livelli, Tom, Barnetche, Jesus Martinez, Pasolli, Edoardo, Greenfield, Nick, Hasan, Nur, Brownstein, John, Nozick, Linda, Michels, Harold, Schriml, Lynn, Brownstein, Catherine, Garbarino, Jeanne, Lyons, Abby, Zhu, Jeff, Genome Center [UC Davis], University of California [Davis] (UC Davis), University of California (UC)-University of California (UC), American University in Cairo, Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Robert Koch Institute [Berlin] (RKI), Centre for Infection Medicine, Institute of Microbiology and Epizootics, Health Corps Italian Army, Department of Molecular Biology, Immunology and Experimental Medicine, Army Medical and Veterinary Research Center, Integrative Biology Unit, Parco Tecnologico Padano, Department of Environmental Science and Engineering, Graduate School of Science and Engineering, Department of Population Medicine and Diagnostic Sciences, Cornell University [New York], Dynamique cellulaire et moléculaire de la muqueuse respiratoire, Université de Reims Champagne-Ardenne (URCA)-IFR53-Institut National de la Santé et de la Recherche Médicale (INSERM), Biostatistics Department, Harvard School of Public Health, Institute for genomic and systems biology, Argonne National Laboratory [Lemont] (ANL), Department of Ecology and Evolution [Chicago], University of Chicago, Alfred P. Sloan Foundation [2015-13964], National Institutes of Health [F31GM111053, R01NS076465, R25EB020393], Promega, CosmosID, Illumina, Copan, QIAGEN, University of California-University of California, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Acibadem University Dspace, Afshinnekoo, Ebrahim, Ahsanuddin, Sofia, Ghedin, Elodie, Read, Timothy, Fraser, Claire, Dudley, Joel, Bowler, Christopher, Mason, Christopher E., Chernomoretz, Ariel, Stolovitzky, Gustavo, Łabaj, Paweł P., Graf, Alexandra B., Darling, Aaron, Burke, Catherine, Noushmehr, Houtan, Dias-Neto, Emmanuel, Guo, Yongli, Xie, Zhi, Lee, Patrick K. H., Shi, Leming, Ruiz-Perez, Carlos A., Zambrano, Maria Mercede, Siam, Rania, Ouf, Amged, Richard, Hugue, Lafontaine, Ingrid, Wieler, Lothar H., Semmler, Torsten, Prithiviraj, Bharath, Nedunuri, Narasimha, Mehr, Shaadi, Banihashemi, Kambiz, Lista, Florigio, Anselmo, Anna, Suzuki, Haruo, Kuroda, Makoto, Yamashita, Riu, Sato, Yukoto, Kaminuma, Eli, Aranda, Celia M. Alpuche, Martinez, Jesu, Dada, Christopher, Dybwad, Mariu, Oliveira, Manuela, Schuster, Stephan, Siwo, Geoffrey H., Jang, Soojin, Seo, Sung Chul, Hwang, Sung Ho, Ossowski, Stephan, Bezdan, Daniela, Chaker, Salama, Chatziefthimiou, Aspassia D., Udekwu, Kla, Liungdahl, Per, Sezerman, Ugur, Meydan, Cem, Elhaik, Eran, Gonnet, Gaston, Schriml, Lynn M., Mongodin, Emmanuel, Huttenhower, Curti, Gilbert, Jack, Eisen, Jonathan, Hirschberg, David, Hernandez, Mark, Mcgrath, Ken, Mcgrath, Leanne, Gray, Andrew, Osuolale, Olayinka, Segata, Nicola, Fillo, Silvia, Iraola, Gregorio, Zhou, Yiming, Chang, Yujun, Li, Yang, Zhend, Yuanting, Hou, Wanwan, Ramirez, Adan, Cepeda, Martha, Desnues, Christelle, Rascovan, Nicola, Baron, Colin, Nagarajan, Niranjan, Ercolini, Danilo, Menary, Wayne, Tighe, Scott, Donia, Mohamed, Levy, Shawn, Benito, Joseph, Jones, Angela, Kasarskis, Andrew, Maritz, Julia, Jorgensen, Ellen, Neches, Russell, Livelli, Tom, Barnetche, Jesus Martinez, Pasolli, Edoardo, Greenfield, Nick, Hasan, Nur, Brownstein, John, Nozick, Linda, Michels, Harold, Schriml, Lynn, Brownstein, Catherine, Garbarino, Jeanne, Lyons, Abby, and Zhu, Jeff
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0301 basic medicine ,Microbiology (medical) ,Built environment ,Antimicrobial resistance markers ,[SDV]Life Sciences [q-bio] ,030106 microbiology ,BIOINFORMÁTICA ,Biosynthetic gene cluster ,Genomics ,Biology ,Meeting Report ,Microbiology ,Biosynthetic gene clusters ,Microbiome ,Next-generation sequencing ,12. Responsible consumption ,Metagenomic ,03 medical and health sciences ,Data visualization ,Urban planning ,Databases, Genetic ,11. Sustainability ,City Planning ,Architecture ,Environmental planning ,Ecosystem ,Metadesign ,business.industry ,Antimicrobial resistance marker ,Biotechnology ,030104 developmental biology ,Research Design ,13. Climate action ,Metagenomics ,Public Health ,Sample collection ,business ,Human - Abstract
International audience; The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium is a novel, interdisciplinary initiative comprised of experts across many fields, including genomics, data analysis, engineering, public health, and architecture. The ultimate goal of the MetaSUB Consortium is to improve city utilization and planning through the detection, measurement, and design of metagenomics within urban environments. Although continual measures occur for temperature, air pressure, weather, and human activity, including longitudinal, cross-kingdom ecosystem dynamics can alter and improve the design of cities. The MetaSUB Consortium is aiding these efforts by developing and testing metagenomic methods and standards, including optimized methods for sample collection, DNA/RNA isolation, taxa characterization, and data visualization. The data produced by the consortium can aid city planners, public health officials, and architectural designers. In addition, the study will continue to lead to the discovery of new species, global maps of antimicrobial resistance (AMR) markers, and novel biosynthetic gene clusters (BGCs). Finally, we note that engineered metagenomic ecosystems can help enable more responsive, safer, and quantified cities.
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- 2016
11. Linking Wikidata to the Semantic Web
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Waagmeester, Andra, Willighagen, Egon, Núria Queralt Rosinach, Mitraka, Elvira, Burgstaller-Muehlbacher, Sebastian, Putman, Tim E, Turner, Julia, Schriml, Lynn M, Pavlidis, Paul, Su, Andrew I, and Good, Benjamin M
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InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL ,ComputingMethodologies_DOCUMENTANDTEXTPROCESSING ,InformationSystems_DATABASEMANAGEMENT - Abstract
Poster described at: http://ceur-ws.org/Vol-1795/paper46.pdf Wikidata is the linked database of Wikipedia and its sister projects from the Wikimedia foundation. Wikidata can be queried by SPARQL queries. Either through the WikiData Query Service (WDQS: http://query.wikidata.org) or its SPARQL endpoint at: https://query.wikidata.org/bigdata/namespace/wdq/sparql. Both the WDQS and the SPARQL endpoint do not allow submitting federated SPARQL queries. In our efforts to make Wikidata the central hub of linked data in the life science, being able to submit federated queries can be an asset. Although federation is not supported from Wikidata, the SPARQL endpoint is accessible for other SPARQL endpoint that do support federation. We compare four federated SPARQL query patterns to query Wikidata in combination with other semantic web formats.
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- 2017
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12. Database resources of the National Center for Biotechnology Information
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Wheeler, David L., Barrett, Tanya, Benson, Dennis A., Bryant, Stephen H., Canese, Kathi, Church, Deanna M., DiCuccio, Michael, Edgar, Ron, Federhen, Scott, Helmberg, Wolfgang, Kenton, David L., Khovayko, Oleg, Lipman, David J., Madden, Thomas L., Maglott, Donna R., Ostell, James, Pontius, Joan U., Pruitt, Kim D., Schuler, Gregory D., Schriml, Lynn M., Sequeira, Edwin, Sherry, Steven T., Sirotkin, Karl, Starchenko, Grigory, Suzek, Tugba O., Tatusov, Roman, Tatusova, Tatiana A., Wagner, Lukas, and Yaschenko, Eugene
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Models, Molecular ,Databases, Factual ,03 medical and health sciences ,0302 clinical medicine ,Databases, Genetic ,Protein Interaction Mapping ,Genetics ,Animals ,Humans ,natural sciences ,Amino Acid Sequence ,Conserved Sequence ,030304 developmental biology ,0303 health sciences ,National Library of Medicine (U.S.) ,Gene Expression Profiling ,Computational Biology ,Articles ,Genomics ,United States ,Protein Structure, Tertiary ,3. Good health ,Phenotype ,Genes ,Sequence Alignment ,Software ,030217 neurology & neurosurgery - Abstract
In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data retrieval systems and computational resources for the analysis of data in GenBank and other biological data made available through NCBI's website. NCBI resources include Entrez, Entrez Programming Utilities, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD) and the Conserved Domain Architecture Retrieval Tool (CDART). Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized datasets. All of the resources can be accessed through the NCBI home page at http://www.ncbi.nlm.nih.gov.
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- 2004
13. An examination of the ecological correlates and evolution of polygyny in marsh wrens in Delta, British Columbia
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Schriml, Lynn M.
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Biology, Ecology - Abstract
In this study, I examined the occurrence of polygyny in marsh wrens (Cistothorus palustris) in Delta, B.C. Using four field seasons of data, I found in this population that males were moderately (19%) to highly (66%) polygynous, that in three years of the study (1979, 1992, 1993) polygynous males fledged more young than did monogamous males, and that the breeding sex ratio was female biased in three out of the four years of the study (1979, 1982, 1993). In order to examine if the observed male reproductive success was an accurate measure of male fecundity, I used DNA fingerprinting to assess paternity and therefore male realized reproductive success for the 1992 and 1993 breeding seasons. In order to ascertain the importance of predation of breeding nests on female choice of a breeding situation and male and female reproductive success, I compared predation rates on polygynous and monogamous nests. In order to test all of the possible reasons for polygyny occurring simultaneously I used a multi-modeled approach. I found that the skewed sex ratio model explained the occurrence of polygyny in 1979, 1982, and 1993. Additionally in 1979, 1982, and 1992, the polygyny threshold model also explained polygyny in the Delta population. In 1993, I found that the random settlement model was also supported by the results of this study. Overall in this population of marsh wrens the largest factors affecting the occurrence of polygyny was the skewed sex ratio of the breeding population in favor of females. Secondly, determination of which males became polygynous was most likely affected by female choice of a breeding situation, with females apparently preferring males whose territories were further from the upland edge of the marsh.
- Published
- 2009
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- View/download PDF
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