Author: "Lynn M Schriml" - Searchworks@Jio Institute Digital Library Search Results

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1. Wikidata as a knowledge graph for the life sciences

Author: Andra Waagmeester, Gregory Stupp, Sebastian Burgstaller-Muehlbacher, Benjamin M Good, Malachi Griffith, Obi L Griffith, Kristina Hanspers, Henning Hermjakob, Toby S Hudson, Kevin Hybiske, Sarah M Keating, Magnus Manske, Michael Mayers, Daniel Mietchen, Elvira Mitraka, Alexander R Pico, Timothy Putman, Anders Riutta, Nuria Queralt-Rosinach, Lynn M Schriml, Thomas Shafee, Denise Slenter, Ralf Stephan, Katherine Thornton, Ginger Tsueng, Roger Tu, Sabah Ul-Hasan, Egon Willighagen, Chunlei Wu, and Andrew I Su
Subjects: science forum, knowledge graphs, data mining, drug repurposing, wikidata, Medicine, Science, Biology (General), QH301-705.5
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.
Published: 2020
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2. Genomic reproducibility in the bioinformatics era

Author: Pelin Icer Baykal, Paweł Piotr Łabaj, Florian Markowetz, Lynn M. Schriml, Daniel J. Stekhoven, Serghei Mangul, and Niko Beerenwinkel
Subjects: Reproducibility, genomics, bioinformatics tools, technical replicates, synthetic replicates, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract: Abstract In biomedical research, validating a scientific discovery hinges on the reproducibility of its experimental results. However, in genomics, the definition and implementation of reproducibility remain imprecise. We argue that genomic reproducibility, defined as the ability of bioinformatics tools to maintain consistent results across technical replicates, is essential for advancing scientific knowledge and medical applications. Initially, we examine different interpretations of reproducibility in genomics to clarify terms. Subsequently, we discuss the impact of bioinformatics tools on genomic reproducibility and explore methods for evaluating these tools regarding their effectiveness in ensuring genomic reproducibility. Finally, we recommend best practices to improve genomic reproducibility.
Published: 2024
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3. Standardized metadata for human pathogen/vector genomic sequences.

Author: Vivien G Dugan, Scott J Emrich, Gloria I Giraldo-Calderón, Omar S Harb, Ruchi M Newman, Brett E Pickett, Lynn M Schriml, Timothy B Stockwell, Christian J Stoeckert, Dan E Sullivan, Indresh Singh, Doyle V Ward, Alison Yao, Jie Zheng, Tanya Barrett, Bruce Birren, Lauren Brinkac, Vincent M Bruno, Elizabet Caler, Sinéad Chapman, Frank H Collins, Christina A Cuomo, Valentina Di Francesco, Scott Durkin, Mark Eppinger, Michael Feldgarden, Claire Fraser, W Florian Fricke, Maria Giovanni, Matthew R Henn, Erin Hine, Julie Dunning Hotopp, Ilene Karsch-Mizrachi, Jessica C Kissinger, Eun Mi Lee, Punam Mathur, Emmanuel F Mongodin, Cheryl I Murphy, Garry Myers, Daniel E Neafsey, Karen E Nelson, William C Nierman, Julia Puzak, David Rasko, David S Roos, Lisa Sadzewicz, Joana C Silva, Bruno Sobral, R Burke Squires, Rick L Stevens, Luke Tallon, Herve Tettelin, David Wentworth, Owen White, Rebecca Will, Jennifer Wortman, Yun Zhang, and Richard H Scheuermann
Subjects: Medicine, Science
Abstract: High throughput sequencing has accelerated the determination of genome sequences for thousands of human infectious disease pathogens and dozens of their vectors. The scale and scope of these data are enabling genotype-phenotype association studies to identify genetic determinants of pathogen virulence and drug/insecticide resistance, and phylogenetic studies to track the origin and spread of disease outbreaks. To maximize the utility of genomic sequences for these purposes, it is essential that metadata about the pathogen/vector isolate characteristics be collected and made available in organized, clear, and consistent formats. Here we report the development of the GSCID/BRC Project and Sample Application Standard, developed by representatives of the Genome Sequencing Centers for Infectious Diseases (GSCIDs), the Bioinformatics Resource Centers (BRCs) for Infectious Diseases, and the U.S. National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health (NIH), informed by interactions with numerous collaborating scientists. It includes mapping to terms from other data standards initiatives, including the Genomic Standards Consortium's minimal information (MIxS) and NCBI's BioSample/BioProjects checklists and the Ontology for Biomedical Investigations (OBI). The standard includes data fields about characteristics of the organism or environmental source of the specimen, spatial-temporal information about the specimen isolation event, phenotypic characteristics of the pathogen/vector isolated, and project leadership and support. By modeling metadata fields into an ontology-based semantic framework and reusing existing ontologies and minimum information checklists, the application standard can be extended to support additional project-specific data fields and integrated with other data represented with comparable standards. The use of this metadata standard by all ongoing and future GSCID sequencing projects will provide a consistent representation of these data in the BRC resources and other repositories that leverage these data, allowing investigators to identify relevant genomic sequences and perform comparative genomics analyses that are both statistically meaningful and biologically relevant.
Published: 2014
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4. The Genomic Standards Consortium.

Author: Dawn Field, Linda Amaral-Zettler, Guy Cochrane, James R Cole, Peter Dawyndt, George M Garrity, Jack Gilbert, Frank Oliver Glöckner, Lynette Hirschman, Ilene Karsch-Mizrachi, Hans-Peter Klenk, Rob Knight, Renzo Kottmann, Nikos Kyrpides, Folker Meyer, Inigo San Gil, Susanna-Assunta Sansone, Lynn M Schriml, Peter Sterk, Tatiana Tatusova, David W Ussery, Owen White, and John Wooley
Subjects: Biology (General), QH301-705.5
Abstract: A vast and rich body of information has grown up as a result of the world's enthusiasm for 'omics technologies. Finding ways to describe and make available this information that maximise its usefulness has become a major effort across the 'omics world. At the heart of this effort is the Genomic Standards Consortium (GSC), an open-membership organization that drives community-based standardization activities, Here we provide a short history of the GSC, provide an overview of its range of current activities, and make a call for the scientific community to join forces to improve the quality and quantity of contextual information about our public collections of genomes, metagenomes, and marker gene sequences.
Published: 2011
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5. Modeling the enigma of complex disease etiology

Author: Lynn M. Schriml, Richard Lichenstein, Katharine Bisordi, Cynthia Bearer, J. Allen Baron, and Carol Greene
Subjects: Disease etiology, Diabetes, Asthma, Fetal alcohol syndrome, Pathophysiology, Environmental drivers, Medicine
Abstract: Abstract Background Complex diseases often present as a diagnosis riddle, further complicated by the combination of multiple phenotypes and diseases as features of other diseases. With the aim of enhancing the determination of key etiological factors, we developed and tested a complex disease model that encompasses diverse factors that in combination result in complex diseases. This model was developed to address the challenges of classifying complex diseases given the evolving nature of understanding of disease and interaction and contributions of genetic, environmental, and social factors. Methods Here we present a new approach for modeling complex diseases that integrates the multiple contributing genetic, epigenetic, environmental, host and social pathogenic effects causing disease. The model was developed to provide a guide for capturing diverse mechanisms of complex diseases. Assessment of disease drivers for asthma, diabetes and fetal alcohol syndrome tested the model. Results We provide a detailed rationale for a model representing the classification of complex disease using three test conditions of asthma, diabetes and fetal alcohol syndrome. Model assessment resulted in the reassessment of the three complex disease classifications and identified driving factors, thus improving the model. The model is robust and flexible to capture new information as the understanding of complex disease improves. Conclusions The Human Disease Ontology’s Complex Disease model offers a mechanism for defining more accurate disease classification as a tool for more precise clinical diagnosis. This broader representation of complex disease, therefore, has implications for clinicians and researchers who are tasked with creating evidence-based and consensus-based recommendations and for public health tracking of complex disease. The new model facilitates the comparison of etiological factors between complex, common and rare diseases and is available at the Human Disease Ontology website.
Published: 2023
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6. Analysis of Biomarker Data Towards Development of a Molecular Biomarker Ontology.

Author: Daniel Lyman, Darren A. Natale, Lynn M. Schriml, Kriston Anton, Daniel C. Crichton, and Raja Mazumder
Published: 2021

7. Etiology context of rare diseases in the Human Disease Ontology.

Author: Lynn M. Schriml and J. Allen Baron
Published: 2021
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8. CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase

Author: Kilannin Krysiak, Arpad M Danos, Jason Saliba, Joshua F McMichael, Adam C Coffman, Susanna Kiwala, Erica K Barnell, Lana Sheta, Cameron J Grisdale, Lynzey Kujan, Shahil Pema, Jake Lever, Sarah Ridd, Nicholas C Spies, Veronica Andric, Andreea Chiorean, Damian T Rieke, Kaitlin A Clark, Caralyn Reisle, Ajay C Venigalla, Mark Evans, Payal Jani, Hideaki Takahashi, Avila Suda, Peter Horak, Deborah I Ritter, Xin Zhou, Benjamin J Ainscough, Sean Delong, Chimene Kesserwan, Mario Lamping, Haolin Shen, Alex R Marr, My H Hoang, Kartik Singhal, Mariam Khanfar, Brian V Li, Wan-Hsin Lin, Panieh Terraf, Laura B Corson, Yasser Salama, Katie M Campbell, Kirsten M Farncombe, Jianling Ji, Xiaonan Zhao, Xinjie Xu, Rashmi Kanagal-Shamanna, Ian King, Kelsy C Cotto, Zachary L Skidmore, Jason R Walker, Jinghui Zhang, Aleksandar Milosavljevic, Ronak Y Patel, Rachel H Giles, Raymond H Kim, Lynn M Schriml, Elaine R Mardis, Steven J M Jones, Gordana Raca, Shruti Rao, Subha Madhavan, Alex H Wagner, Malachi Griffith, and Obi L Griffith
Subjects: Genetics
Abstract: CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC’s functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.
Published: 2022
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9. Wikidata as an intuitive resource towards semantic data modeling in data FAIRification.

Author: Annika Jacobsen, Andra Waagmeester, Rajaram Kaliyaperumal, Gregory S. Stupp, Lynn M. Schriml, Mark Thompson 0002, Andrew I. Su, and Marco Roos
Published: 2018

10. Integrating and Maintaining OMIM Cross-references in the Disease Ontology.

Author: Susan M. Bello, Cynthia L. Smith, and Lynn M. Schriml
Published: 2019

11. Wikidata: A Platform for Data Integration and Dissemination for the Life Sciences and Beyond.

Author: Elvira Mitraka, Andra Waagmeester, Sebastian Burgstaller-Muehlbacher, Lynn M. Schriml, Andrew I. Su, and Benjamin M. Good
Published: 2015

12. Metagenomic Methods for Addressing NASA's Planetary Protection Policy Requirements on Future Missions: A Workshop Report

Author: Stefan J. Green, Tamas Torok, Jonathan E. Allen, Emiley Eloe-Fadrosh, Scott A. Jackson, Sunny C. Jiang, Stuart S. Levine, Shawn Levy, Lynn M. Schriml, W. Kelley Thomas, Jason M. Wood, and Scott W. Tighe
Subjects: Geochemistry, Contamination, Space and Planetary Science, Spacecraft Assembly Facility, Geology, DNA, Metagenomics, Astronomy & Astrophysics, Planetary protection, Agricultural and Biological Sciences (miscellaneous), Astronomical and Space Sciences
Abstract: Molecular biology methods and technologies have advanced substantially over the past decade. These new molecular methods should be incorporated among the standard tools of planetary protection (PP) and could be validated for incorporation by 2026. To address the feasibility of applying modern molecular techniques to such an application, NASA conducted a technology workshop with private industry partners, academics, and government agency stakeholders, along with NASA staff and contractors. The technical discussions and presentations of the Multi-Mission Metagenomics Technology Development Workshop focused on modernizing and supplementing the current PP assays. The goals of the workshop were to assess the state of metagenomics and other advanced molecular techniques in the context of providing a validated framework to supplement the bacterial endospore-based NASA Standard Assay and to identify knowledge and technology gaps. In particular, workshop participants were tasked with discussing metagenomics as a stand-alone technology to provide rapid and comprehensive analysis of total nucleic acids and viable microorganisms on spacecraft surfaces, thereby allowing for the development of tailored and cost-effective microbial reduction plans for each hardware item on a spacecraft. Workshop participants recommended metagenomics approaches as the only data source that can adequately feed into quantitative microbial risk assessment models for evaluating the risk of forward (exploring extraterrestrial planet) and back (Earth harmful biological) contamination. Participants were unanimous that a metagenomics workflow, in tandem with rapid targeted quantitative (digital) PCR, represents a revolutionary advance over existing methods for the assessment of microbial bioburden on spacecraft surfaces. The workshop highlighted low biomass sampling, reagent contamination, and inconsistent bioinformatics data analysis as key areas for technology development. Finally, it was concluded that implementing metagenomics as an additional workflow for addressing concerns of NASA's robotic mission will represent a dramatic improvement in technology advancement for PP and will benefit future missions where mission success is affected by backward and forward contamination.
Published: 2023
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13. Assessing resource use: a case study with the Human Disease Ontology

Author: J. Allen Baron and Lynn M Schriml
Subjects: General Agricultural and Biological Sciences, General Biochemistry, Genetics and Molecular Biology, Information Systems
Abstract: As a genomic resource provider, grappling with getting a handle on how your resource is utilized can be extremely challenging. At the same time, being able to thus document the plethora of use cases is vital to demonstrate sustainability. Herein, we describe a flexible workflow, built on readily available software, that the Human Disease Ontology (DO) project has utilized to transition to semi-automated methods to identify uses of the ontology in the published literature. The novel R package DO.utils (https://github.com/DiseaseOntology/DO.utils) has been devised with a small set of key functions to support our usage workflow in combination with Google Sheets. Use of this workflow has resulted in a 3-fold increase in the number of identified publications that use the DO and has provided novel usage insights that offer new research directions and reveal a clearer picture of the DO’s use and scientific impact. The DO’s resource use assessment workflow and the supporting software are designed to be useful to other resources, including databases, software tools, method providers and other web resources, to achieve similar results.Database URL: https://github.com/DiseaseOntology/DO.utils
Published: 2023
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14. Linking Wikidata to the Rest of the Semantic Web.

Author: Andra Waagmeester, Egon L. Willighagen, Núria Queralt Rosinach, Elvira Mitraka, Sebastian Burgstaller-Muehlbacher, Tim E. Putman, Julia Turner, Lynn M. Schriml, Paul Pavlidis, Andrew I. Su, and Benjamin M. Good
Published: 2016

15. The Human Disease Ontology 2022 update

Author: Nicole Campion Dialo, Susan M. Bello, J Allen Baron, Cynthia F. Bearer, Rebecca C. Jackson, Michelle G. Giglio, Carrie McCracken, Katharine Bisordi, Lynn M. Schriml, Carol L. Greene, Dustin Olley, Mike Schor, James B. Munro, Victor Felix, and Richard Lichenstein
Subjects: Standardization, Databases, Factual, AcademicSubjects/SCI00010, Knowledge organization, Genetic Diseases, Inborn, Disease, Genomics, Ontology (information science), Biology, World Wide Web, Resource (project management), Human disease, Documentation, Biological Ontologies, Databases, Genetic, Genetics, Key (cryptography), Database Issue, Humans
Abstract: The Human Disease Ontology (DO) (www.disease-ontology.org) database, has significantly expanded the disease content and enhanced our userbase and website since the DO’s 2018 Nucleic Acids Research DATABASE issue paper. Conservatively, based on available resource statistics, terms from the DO have been annotated to over 1.5 million biomedical data elements and citations, a 10× increase in the past 5 years. The DO, funded as a NHGRI Genomic Resource, plays a key role in disease knowledge organization, representation, and standardization, serving as a reference framework for multiscale biomedical data integration and analysis across thousands of clinical, biomedical and computational research projects and genomic resources around the world. This update reports on the addition of 1,793 new disease terms, a 14% increase of textual definitions and the integration of 22 137 new SubClassOf axioms defining disease to disease connections representing the DO’s complex disease classification. The DO’s updated website provides multifaceted etiology searching, enhanced documentation and educational resources., Graphical Abstract Graphical AbstractThe Human Disease Ontology (DO) integrates disease features and etiological factors to describe disease complexity. The DO is extensively cited and integrated into highly cited databases.
Published: 2021

16. A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology

Author: Yongqun He, Hong Yu, Anthony Huffman, Asiyah Yu Lin, Darren A. Natale, John Beverley, Ling Zheng, Yehoshua Perl, Zhigang Wang, Yingtong Liu, Edison Ong, Yang Wang, Philip Huang, Long Tran, Jinyang Du, Zalan Shah, Easheta Shah, Roshan Desai, Hsin-hui Huang, Yujia Tian, Eric Merrell, William D. Duncan, Sivaram Arabandi, Lynn M. Schriml, Jie Zheng, Anna Maria Masci, Liwei Wang, Hongfang Liu, Fatima Zohra Smaili, Robert Hoehndorf, Zoë May Pendlington, Paola Roncaglia, Xianwei Ye, Jiangan Xie, Yi-Wei Tang, Xiaolin Yang, Suyuan Peng, Luxia Zhang, Luonan Chen, Junguk Hur, Gilbert S. Omenn, Brian Athey, and Barry Smith
Subjects: Coronavirus, Vaccines, SARS-CoV-2, Computer Networks and Communications, Humans, COVID-19, Health Informatics, Amino Acids, Pandemics, Communicable Diseases, COVID-19 Drug Treatment, Computer Science Applications, Information Systems
Abstract: Background The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020. Results As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. Conclusion CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.
Published: 2022
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17. 2015 Disease Ontology update: DO's expanded curation activities to connect disease-related data.

Author: Elvira Mitraka and Lynn M. Schriml
Published: 2015

18. A community approach to the cancer-variant-interpretation bottleneck

Author: Kilannin Krysiak, Arpad M. Danos, Susanna Kiwala, Joshua F. McMichael, Adam C. Coffman, Erica K. Barnell, Lana Sheta, Jason Saliba, Cameron J. Grisdale, Lynzey Kujan, Shahil Pema, Jake Lever, Nicholas C. Spies, Andreea Chiorean, Damian T. Rieke, Kaitlin A. Clark, Payal Jani, Hideaki Takahashi, Peter Horak, Deborah I. Ritter, Xin Zhou, Benjamin J. Ainscough, Sean Delong, Mario Lamping, Alex R. Marr, Brian V. Li, Wan-Hsin Lin, Panieh Terraf, Yasser Salama, Katie M. Campbell, Kirsten M. Farncombe, Jianling Ji, Xiaonan Zhao, Xinjie Xu, Rashmi Kanagal-Shamanna, Kelsy C. Cotto, Zachary L. Skidmore, Jason R. Walker, Jinghui Zhang, Aleksandar Milosavljevic, Ronak Y. Patel, Rachel H. Giles, Raymond H. Kim, Lynn M. Schriml, Elaine R. Mardis, Steven J. M. Jones, Gordana Raca, Shruti Rao, Subha Madhavan, Alex H. Wagner, Obi L. Griffith, and Malachi Griffith
Subjects: Cancer Research, Oncology, Neoplasms, Genetic Variation, Humans, Article
Abstract: As guidelines, therapies, and literature on cancer variants expand, the lack of consensus variant interpretations impedes clinical applications. CIViC is a public domain, crowd-sourced, and adaptable knowledgebase of evidence for the Clinical Interpretation of Variants in Cancer, designed to reduce barriers to knowledge sharing and alleviate the variant interpretation bottleneck.
Published: 2022

19. Community-Driven Metadata Standards for Agricultural Microbiome Research

Author: Posy E. Busby, Jan E. Leach, R. B. Calderon, B. A. Vinatzer, Anjali S. Iyer-Pascuzzi, Linda L. Kinkel, Emiley A. Eloe-Fadrosh, K. A. T. Silverstein, Gwyn A. Beattie, Bharath Prithiviraj, Neelam R. Redekar, Joanne B. Emerson, J. E. Maul, M. Potrykus, J. A. Rojas, A. I. Huerta, D. J. Tomso, Lynn M. Schriml, F. P. Brennan, J. Leonard, Sydney E. Everhart, Kellye Eversole, Jose Pablo Dundore-Arias, Kenneth E. Frost, Sarah C. Castle, A. K. Kalil, Joshua R. Herr, Susannah G. Tringe, and School of Plant and Environmental Sciences
Subjects: Agroecosystem, phytobiome, microbiome, Genomics, Plant Science, lcsh:Plant culture, Crop productivity, lcsh:Microbial ecology, 03 medical and health sciences, 0302 clinical medicine, genomics, ontologies, lcsh:SB1-1110, Microbiome, Molecular Biology, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Pace, 0303 health sciences, metagenomics, Ecology, business.industry, lcsh:QK900-989, Data science, omics, Metadata, meta-analysis, Geography, Metagenomics, Agriculture, synthetic, lcsh:Plant ecology, lcsh:QR100-130, Zero Hunger, business, Agronomy and Crop Science, 030217 neurology & neurosurgery
Abstract: Accelerating the pace of microbiome science to enhance crop productivity and agroecosystem health will require transdisciplinary studies, comparisons among datasets, and synthetic analyses of research from diverse crop management contexts. However, despite the widespread availability of crop-associated microbiome data, variation in field sampling and laboratory processing methodologies, as well as metadata collection and reporting, significantly constrains the potential for integrative and comparative analyses. Here we discuss the need for agriculture-specific metadata standards for microbiome research, and propose a list of "required" and "desirable" metadata categories and ontologies essential to be included in a future minimum information metadata standards checklist for describing agricultural microbiome studies. We begin by briefly reviewing existing metadata standards relevant to agricultural microbiome research, and describe ongoing efforts to enhance the potential for integration of data across research studies. Our goal is not to delineate a fixed list of metadata requirements. Instead, we hope to advance the field by providing a starting point for discussion, and inspire researchers to adopt standardized procedures for collecting and reporting consistent and well-annotated metadata for agricultural microbiome research. National Science FoundationNational Science Foundation (NSF) [1714276] This material was supported in part by the National Science Foundation under grant number 1714276. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Public domain – authored by a U.S. government employee
Published: 2020

20. Darwinian genomics and diversity in the tree of life

Author: Taylorlyn Stephan, Shawn M. Burgess, Hans Cheng, Charles G. Danko, Clare A. Gill, Erich D. Jarvis, Klaus-Peter Koepfli, James E. Koltes, Eric Lyons, Pamela Ronald, Oliver A. Ryder, Lynn M. Schriml, Pamela Soltis, Sue VandeWoude, Huaijun Zhou, Elaine A. Ostrander, and Elinor K. Karlsson
Subjects: Genome, Multidisciplinary, Evolution, Human Genome, Molecular, Genetic Variation, Genomics, Biodiversity, comparative genomics, Biological Sciences, Biological Evolution, Evolution, Molecular, Perspective, natural models, Genetics, Animals, Humans, The Earth BioGenome Project: The Launch of a Moonshot for Biology, Generic health relevance, Phylogeny, Biotechnology
Abstract: Genomics encompasses the entire tree of life, both extinct and extant, and the evolutionary processes that shape this diversity. To date, genomic research has focused on humans, a small number of agricultural species, and established laboratory models. Fewer than 18,000 of ∼2,000,000 eukaryotic species (
Published: 2022
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21. MIxS-SA: a MIxS extension defining the minimum information standard for sequence data from symbiont-associated micro-organisms

Author: Fátima Jorge, Jaelle C. Brealey, Paul J. Brindley, Marie Buysse, Cinzia Cantacessi, Olivier Duron, Raina Fichorova, Connor R. Fitzpatrick, Megan Hahn, Christopher Hunter, Vincent Hervé, Laura J. Knoll, Kevin D. Kohl, Marco Lalle, Julius Lukeš, Joaquín Martínez Martínez, Susan L. Perkins, Robert Poulin, Karyna Rosario, Adam C. Schneider, Lynn M. Schriml, Luke R. Thompson, Ramona L. Walls, Nolwenn M. Dheilly, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM), Centre de Recherche en Ecologie et Evolution de la Santé (CREES), Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de recherche sur la biologie de l'insecte UMR7261 (IRBI), Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Unité de virologie, Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Laboratoire de Ploufragan-Plouzané-Niort [ANSES], University of Otago [Dunedin, Nouvelle-Zélande], NTNU University Museum [Trondheim], Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU)-Norwegian University of Science and Technology (NTNU), The George Washington University (GW), Department of Veterinary Medicine, University of Cambridge [UK] (CAM), Harvard Medical School [Boston] (HMS), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), NYC Department of Health and Mental Hygiene (NYC Health), University of Wisconsin-Madison, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Istituto Superiore di Sanità (ISS), Institute of Parasitology [České Budějovice] (BIOLOGY CENTRE CAS), Biology Centre of the Czech Academy of Sciences (BIOLOGY CENTRE CAS), Czech Academy of Sciences [Prague] (CAS)-Czech Academy of Sciences [Prague] (CAS), Bigelow Laboratory for Ocean Sciences, The City College of New York (CCNY), City University of New York [New York] (CUNY), University of South Florida [Tampa] (USF), Hendrix College, University of Maryland School of Medicine, University of Maryland System, Mississippi State University [Mississippi], National Oceanic and Atmospheric Administration (NOAA), Critical Path Institute [Tucson, AZ], Virologie UMR1161 (VIRO), École nationale vétérinaire - Alfort (ENVA)-Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Cantacessi, Cinzia [0000-0001-6863-2950], and Apollo - University of Cambridge Repository
Subjects: 3107 Microbiology, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, FOS: Biological sciences, Human Genome, Genetics, General Medicine, Infection, 31 Biological Sciences, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract: The symbiont-associated (SA) environmental package is a new extension to the minimum information about any (x) sequence (MIxS) standards, established by the Parasite Microbiome Project (PMP) consortium, in collaboration with the Genomics Standard Consortium. The SA was built upon the host-associated MIxS standard, but reflects the nestedness of symbiont-associated microbiota within and across host-symbiont-microbe interactions. This package is designed to facilitate the collection and reporting of a broad range of metadata information that apply to symbionts such as life history traits, association with one or multiple host organisms, or the nature of host-symbiont interactions along the mutualism-parasitism continuum. To better reflect the inherent nestedness of all biological systems, we present a novel feature that allows users to co-localize samples, to nest a package within another package, and to identify replicates. Adoption of the MIxS-SA and of the new terms will facilitate reports of complex sampling design from a myriad of environments.
Published: 2022
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22. Annotating unknown species of urban microorganisms on a global scale unveils novel functional diversity and local environment association

Author: Patrick K. H. Lee, Johannes Werner, Christopher E. Mason, Yongxiang Zhao, Gabriella Mason-Buck, Christos A. Ouzounis, Gregorio Iraola, Marina Nieto-Caballero, Torsten Semmler, Michael H. Perlin, Ebrahim Afshinnekoo, Anna Różańska, Malay Bhattacharyya, Nicolás Rascovan, O. Osuolale, Jun Wu, Alina Frolova, Juan A. Ugalde, Emmanuel Dias-Neto, Ben Young, Bharath Prithiviraj, Daniela Bezdan, Soojin Jang, Eduardo Castro-Nallar, Lynn M. Schriml, Tieliu Shi, David Danko, Nur Hazlin Hazrin-Chong, Paweł P. Łabaj, María Mercedes Zambrano, Haruo Suzuki, Agnieszka Chmielarczyk, and Youping Deng
Subjects: education.field_of_study, Bacteria, Microorganism, Scale (chemistry), Association (object-oriented programming), Microbiota, Population, Biology, Biochemistry, Genome, Evolutionary biology, Metagenomics, Humans, Metagenome, Microbial Interactions, Microbiome, Urban ecosystem, education, General Environmental Science
Abstract: In urban ecosystems, microbes play a key role in maintaining major ecological functions that directly support human health and city life. However, the knowledge about the species composition and functions involved in urban environments is still limited, which is largely due to the lack of reference genomes in metagenomic studies comprises more than half of unclassified reads. Here we uncovered 732 novel bacterial species from 4728 samples collected from various common surface with the matching materials in the mass transit system across 60 cities by the MetaSUB Consortium. The number of novel species is significantly and positively correlated with the city population, and more novel species can be identified in the skin-associated samples. The in-depth analysis of the new gene catalog showed that the functional terms have a significant geographical distinguishability. Moreover, we revealed that more biosynthetic gene clusters (BGCs) can be found in novel species. The co-occurrence relationship between BGCs and genera and the geographical specificity of BGCs can also provide us more information for the synthesis pathways of natural products. Expanded the known urban microbiome diversity and suggested additional mechanisms for taxonomic and functional characterization of the urban microbiome. Considering the great impact of urban microbiomes on human life, our study can also facilitate the microbial interaction analysis between human and urban environment.
Published: 2022

23. OBO Foundry in 2021: operationalizing open data principles to evaluate ontologies

Author: Nicolas Matentzoglu, Nomi L. Harris, Christopher J. Mungall, James P. Balhoff, William D. Duncan, Seth Carbon, Lynn M. Schriml, Darren A. Natale, Ramona Walls, David Osumi-Sutherland, Randi Vita, Christian J. Stoeckert, Barry Smith, Rebecca C. Jackson, Alan Ruttenberg, Damion M. Dooley, Pier Luigi Buttigieg, Mélanie Courtot, Suzanna E. Lewis, Jie Zheng, Alexander D. Diehl, Melissa A. Haendel, James A. Overton, Bjoern Peters, and Nicole Vasilevsky
Subjects: Metadata, Operationalization, Databases, Factual, Computer science, Dashboard (business), Interoperability, Biological Ontologies, Ontology (information science), Data science, General Biochemistry, Genetics and Molecular Biology, Data Format, Open Biomedical Ontologies, Databases, Networking and Information Technology R&D, Networking and Information Technology R&D (NITRD), Library and Information Studies, OBO Foundry, AcademicSubjects/SCI00960, Original Article, Generic health relevance, General Agricultural and Biological Sciences, Factual, Information Systems
Abstract: Biological ontologies are used to organize, curate and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here, we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology’s compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable, federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data Findable, Accessible, Interoperable, and Reusable (FAIR). Database URL http://obofoundry.org/
Published: 2021

24. A global metagenomic map of urban microbiomes and antimicrobial resistance

Author: Nadine Farhat, Tomoki Takeda, Astred Castro, Ken McGrath, Khaliun Sanchir, Iman Hajirasouliha, Eunice So, Laraib Zafar, Diana N. Nunes, Harun Mustafa, Amy Zhang, Priscilla Lisboa, Christian Schori, Marisano James, Jasna Chalangal, Sebastien Halary, Shahryar Rana, Yunmi Lee, Oli Schacher, Liliana Godoy, David A. Coil, Phanthira Pugdeethosal, Michelle D. Williams, German Marchandon, Angela Cantillo, Naoya Takahashi, Christopher Mozsary, Juana Gonzalez, Patrick K. H. Lee, Gerardo de Lamotte, Alessandro Robertiello, Steven Du, Fabienne Velter, Stefan G. Stark, Miguel Carbajo, Vincent Matthys, David A. Westfall, Julia Boeri, Irène Mauricette Mendy, Jonathan Cedillo, Francesco Oteri, Robert W. Crawford, Takayuki Ito, Tina Wunderlin, Maureen Muscat, David Paez-Espino, Carmen Urgiles, Aida Nesimi, Steffen Schaaf, Adan Ramirez-Rojas, Kunihiko Miyake, Christopher E. Mason, Anais Cardenas, Sharah Islam, Diego Benítez, Melissa Pool Pizzi, Kianna Ciaramella, Ciro Borrelli, Riham Islam, Dorottya Nagy-Szakal, Abd-Manaaf Bakere, Ait-hamlat Adel, Olha Lakhneko, Badamnyambuu Iderzorig, Ana Valeria Castro, Adam Phillips, Robert A. Petit, Flavia Corsi, Romain Conte, Krista Ryon, Soojin Jang, Joseph Benson, Fernanda de Souza Gomes Kehdy, Cindy Wang, Nicole Mathews, Jenn-Wei Chen, Rachel Paras, Paulina Pastuszek, Abigail Lyons, Paul Roldán, Muntaha Munia, Pierre Nicolas, Cassie L. Ettinger, Kyrylo Pyrshev, Katterinne N. Mendez, Eduardo Castro-Nallar, Valeriia Dotsenko, Michelle Tuz, Krizzy Mallari, Eileen Png, Yuya Sonohara, Tanja Miketic, Stéphane Delmas, Shu Zhang, Masaki Sato, Yuanting Zheng, Jifeng Zhu, Roland Häusler, Lucie Bittner, Savlatjon Rahmatulloev, Jonathan Foox, Bruno D'Alessandro, Alketa Plaku, Faisal Alquaddoomi, Yang Zhang, Kern Rei Chng, Juliana Lago, Allaeddine Chettouh, Tamera Henry, Houtan Noushmehr, Tranette Gregory, Sara Abdul Majid, Frank J. Kelly, Benjamin Pulatov, Laurie Casalot, Takema Kajita, Lennard Epping, Thais Fernanda Bartelli, Eftar Moniruzzaman, Renee Vivancos-Koopman, Thirumalaisamy P. Velavan, Tracy W. Liu, Yelyzaveta Tymoshenko, Alma Plaku, Nika Gurianova, Ambar Mendez, Anna Tomaselli, Sonia Dorado, Donato Giovannelli, Hira Choudhry, Synti Ng, Sheelta S. Kumar, Jennifer Q. Lu, Weijun Liang, Ellen Koag, Dennis Gankin, Maria João Amorim, Gwenola Simon, Kiyoshi Suganuma, Mikhail Karasikov, Christos A. Ouzounis, Madelyn May, Eran Elhaik, Stephan Ossowski, Kevin Bolzli, Matthew Arthur, Yuya Oto, Jananan Pathmanathan, Salah Mahmoud, Kou Takahashi, Brunna Marques, Kelly French, Felipe Sepúlveda, Shusei Yoshikawa, Paulo Thiago de Souza Santos, Andrew N. Gray, Juliana S Bernardes, Felipe Segato, Björn Brindefalk, George C. Yeh, Jhovana L. Velasco Flores, Jill Sullivan, Silva Baburyan, Denisse Flores, Russell Y. Neches, Sabrina Persaud, Rasheena Wright, Takumi Togashi, Verónica Antelo, Nao Kato, Skye Felice, Tatjana Mustac, Daisy Donnellan, Katerine Carrillo, Anna Litskevitch, Catalina García, Sota Ito, Naya Eady, Andrew Wan, Irene Meng, Sophie Guasco, Danilo Ercolini, Francesca De Filippis, Vincent Lemaire, Luice Fan, Lothar H. Wieler, Mariia Rybak, Jorge Sanchez, Jonathan S. Gootenberg, Itsuki Tomita, Maritza S Mosella, Laura Garcia, Natalka Makogon, Daisy Cheung, Hitler Francois Vasquez Arevalo, Freddy Asenjo, Gabriela P. Branco, Erika Cifuentes, Chloé Dequeker, Aspassia D. Chatziefthimiou, Alexis Terrero, Roy Meoded, Isabelle de Oliveira Moraes, Shaleni K. Singh, Orgil-Erdene Molomjamts, Karishma Miah, Laurent David, Wolfgang Haehr, Dao Phuong Giang, Romain Lannes, Prashanthi Ratnanandan, Ryota Yamanaka, Riccardo Vicedomini, Sadaf Ayaz, Oluwatosin M. Osuolale, Laura E. Vann, Gregory Chem, Andrea Gonzalez, Aszia Burrell, Ariel Chernomoretz, Sakura Ishizuka, Michelle Rivera, Avigdor Nosrati, Michelle B. Chen, Juliette Auvinet, Nils Ordioni, Tomoro Warashina, Guillaume Blanc, Tomislav Ivankovic, Christina Black, Lauren E. Hittle, David Hess-Homeier, Michael Kozhar, Hamood Suliman, Karobi Moitra, Saher Rahiel, Spyridon Gkotzis, Jenny Arevalo, Shaikh B. Iqbal, Beth Mutai, Mohammed Mohsin, Scott Tighe, Sylvie Collin, Yoshitaka Saito, Wayne Menary, Youping Deng, Lucy Lee, Esmeralda Jiminez, Ayuki Watanabe, Nikos C. Kyrpides, Natasha Mohan, Angelika Pupiec, Dedan Githae, Simone Cawthorne, Jonathan A. Eisen, Tomoki Iwashiro, Chiaki Homma, Thomas Saw Aung, Laura Molina, Marcus H. Y. Leung, Ophélie Da Silva, Yan Ling Wong, Hosna Noorzi, Mario Moreno, Alina Butova, Leming Shi, Brian W. Wong, Sarah S. Jackson, Moses Lin, Annabelle Meagher, Pujita Das, Catherine Burke, Mitsuki Ota, Maria Domenica Moccia, Nicolas Sprinsky, Catherine E. Pugh, David C. Green, Fazlina Fauzi, Erdenetsetseg Batdelger, Annie Geiger, Valeria Ventorino, Tolulope Oluwadare, Delisia Cuebas, Catalina Truong, Leonardo Posada, Michael Angelov, Tathiane M. Malta, Amanda Ng, Francesca Nadalin, Arya Hawkins-Zafarnia, Yuh Shiwa, Athena Mitsios, Milton Ozório Moraes, Manolo Laiola, Kalyn Ali, Jaden J.A. Hastings, Ikuto Saito, Maheen Shakil, Chisato Suzuki, Elena M. Vayndorf, Hubert Rehrauer, Ajay Menon, Kaitlan Russell, Aliyah Shari, Rebecca Smith, Gregorio Iraola, Max Priestman, Alan Briones, Silver A. Wolf, Camila Gonzalez-Poblete, Eleonora De Lazzari, Shirley Chiu, Michelle Ki, Irene Hoxie, Marianne Jaubert, Ayantu Jinfessa, Ryan J. King, Nghiem Xuan Hoan, Jalia Bynoe, Jacob Friedman, Aneisa Ramcharan, Pablo Fresia, Cristina Muñoz, Muhammad Afaq, Anyi Tang, Médine Benchouaia, Isabella Kuniko T. Takenaka, Anastasia Chasapi, Areeg Naeem, Hannah Benisty, Cecilia N. Cossio, Nathalie Hüsser, Mahfuza Sabina, Thais S. Sabedot, JoAnn Jacobs, Camila P. E. de Souza, Manuela Oliveira, Jean-Pierre Bouly, Mariko Usui, Wilson Miranda, Natalia Marciniak, Hiram Caballero, Samuel Weekes, Alexandra B. Graf, Emily Leong, Tatyana Nikolayeva, Dominique Thomas, Charlotte Greselle, Cecilia Salazar, Sreya Ray Chaudhuri, Kevin Becher, Sandra Roth, Ryusei Miura, Kari Oline Bøifot, Dimitri Manoir, Oliver Toth, Chandrima Bhattacharya, Manuel Perez, Isha Lamba, Takafumi Tsurumaki, Timothy D. Read, Anna-Lena M. Schinke, Ryan Sankar, Le Huu Song, Narasimha Rao Nedunuri, Emmanuel Dias-Neto, Ana Flávia Costa, Adiell Melamed, Christelle Desnues, Natalie R. Davidson, Aaron E. Darling, Hyung Jun Kim, Josephine Galipon, Jacqueline Orrego, Dimitar Vassilev, Michael Huber, Nur Hazlin Hazrin-Chong, Gaston H. Gonnet, Kaymisha Knights, Osman U. Sezerman, Dmitry Meleshko, Eunice Thambiraja, Jingcheng Yang, Aubin Fleiss, Gloria Nguyen, Katelyn Jackson, Nuria Aventin, Stephanie L. Hyland, Andrea Hässig, Catharine Aquino, Simona Lysakova, Israel O. Osuolale, Kasia Sluzek, Rania Siam, Alina Frolova, Samuel Hernandez, Yui Him Lo, Bazartseren Boldgiv, Ben Young, Maryna Korshevniuk, Majelia Ampadu, Yuk Man Tang, Amanda L. Muehlbauer, Sade Thomas, Gabriel Figueroa, Alexis Rivera, Lisbeth Pineda, Alexandra Dutan, Jennifer M. Tran, Chris K. Deng, Vedbar S. Khadka, Paola Florez de Sessions, Elizabeth Humphries, Hugues Richard, Hiba Naveed, Nora C. Toussaint, Mahshid Khavari, Maria del Mar Vivanco Ruiz, Antonin Thiébaut, Nicolás Rascovan, Marius Dybwad, Orhan Özcan, Lawrence Kwong, David Danko, Shaira Khan, Andrea Tassinari, Silvia Beurmann, Tsoi Ying Lai, Nanami Kubota, Tieliu Shi, Diana Chicas, Evan E. Afshin, Hirokazu Yano, Jonas Krebs, Mayuko Nakagawa, Hyun Jung Lee, Irene González Navarrete, Rachid Ounit, Lucia E. Alvarado-Arnez, Masaki Nasu, Allison Chan, Harilanto Andrianjakarivony, Jennifer Amachee, Mahdi Taye, Wan Chiew Ng, Kathryn O’Brien, Shino Ishikawa, Tristan Bitard-Feildel, Sora Takagi, Felix Hartkopf, Niamh B. O’Hara, Marcos A. S. Fonseca, Subhamitra Pakrashi, Amrit Kaur, Eva Hell, Patricia Vera-Wolf, Naimah Munim, Luiza Ferreira de Araújo, Mizuki Igarashi, Brianna Pompa-Hogan, Alessandra Carbone, Anne-Sophie Benoiston, Eric Helfrich, Michael A. Suarez-Villamil, Omar O. Abudayyeh, Natasha Abdullah, Jaime J. Fuentes, Juan Carlos Forero, Tetiana Yeskova, Denis Bertrand, Sambhawa Priya, Denisse Maldonado, Agier Nicolas, Ana Valeria B Castro, Starr Chatziefthimiou, André Kahles, Aaishah Francis, Fernanda Arredondo, Emilio Tarcitano, Irvind Buttar, Alex Alexiev, Jennifer Molinet, Sarah Shalaby, Itunu A. Oluwadare, Jason Sperry, Katrin Bakhl, Ana M. Cañas, Sofia Ahsanuddin, Miar Elaskandrany, Elodie Laine, Sven Bönigk, Johannes Werner, Stephen Eduard Boja Ruiz, Gargi Dayama, Paulina Buczansla, Brandon Valentine, Bharath Prithiviraj, Toni Bode, Stas Zubenko, Jake Cohen, Guilllaume Jospin, Zulena Saravi, Per O. Ljungdahl, Inderjit Kaur, Mauricio Moldes, Giuseppe KoLoMonaco, Denise Syndercombe Court, Sonia Bouchard, Sonia Losim, Sookwon Moon, Heba Shaaban, Suraj Patel, Sibo Zhu, Sarh Aly, Arif Asyraf Md Supie, LaShonda Dorsey, Juan Guerra, François Baudon, Rantimi A. Olawoyin, Alexia Bordigoni, Iqra Faiz, Mathilde Garcia, Gabriella Mason-Buck, María Gabriela Portilla, Niranjan Nagarajan, Fumie Takahara, Nancy Merino, Watson Andrew, Gina Kim, Yuma Sato, Hyenah Shim, Marie-Laure Jerier, Affifah Saadah Ahmad Kassim, Katerina Kuchin, Daniel Butler, Paweł P. Łabaj, Nadezhda Kobko-Litskevitch, Emmanuel F. Mongodin, Yuto Togashi, Paula Rodríguez, Pilar Lopez Hernandez, Xiaoqing Chen, Maria A. Sierra, Olga Nikolayeva, Manon Loubens, Colleen Conger, Hikaru Shirahata, Chenhao Li, Timothy Donahoe, Youngja Park, Lucia Elena Alvarado Arnez, Salama Chaker, Francisco Chavez, Alessandra Breschi, Jorge L. Sanchez, Kaung Myat San, Nayra Aguilar Rojas, Marcos Abraao, Kai Sasaki, Bryan Nazario, Olena Yemets, Klas I. Udekwu, Lynn M. Schriml, Anisia Peters, Aliaksei Holik, Mark Hernandez, Emile Faure, Malay Bhattacharyya, Josef W. Moser, Núria Andreu Somavilla, María Mercedes Zambrano, Kannan Rajendran, Gabriela E. Albuquerque, Tao Qing, Kazutoshi Tsuda, Ymke De Jong, Princess Osma, Mayra Arauco Livia, Javier Quilez Oliete, Carl Chrispin, Hyun Woo Joo, Ingrid Lafontaine, Nala An, Seisuke Sato, Felipe Segato Dezem, Andrew Maltez Thomas, Alexandre Desert, Xiao Wen Cai, O. Osuolale, Jun Wu, Coral Pardo-Esté, Courtney Robinson, Yuri Matsuzaki, Marina Nieto-Caballero, Cem Meydan, Ralph Schlapbach, Mark Menor, Sofia Castro, Rachel Kwong, Brittany Blyther, Olexandr Lykhenko, Jason R. Schriml, Christian Brion, Jenessa Orpilla, Juan A. Ugalde, Elsy Mankah Ngwa, Álvaro Aranguren, Lauren Mak, Matías Giménez, Ashanti Narce, Torsten Semmler, Stefan I. Tsonev, Abdollahi Nika, Katherine E. Dahlhausen, Monika Devi, Gunnar Rätsch, Oasima Muner, Carla Bello, Muhammad Al-Fath Amran, Anyelic Rosario, Melissa Ortega, Andrea Patrignani, Ante Peros, Elias McComb, Ryo Sato, Ireen Alam, Clara N. Dias, Soma Tanaka, Dayana Calderon, Ran Blekhman, Mathilde Mignotte, Alicia Boyd, Jochen Hecht, Thomas Neff, Xinzhao Tong, Josue Alicea, Kiara Olmeda, Sonia Marinovic, Carme Arnan, Kohei Ito, Samantha L. Goldman, Marianna S. Serpa, Renee Richer, Kaisei Sato, Jordana M. Silva, Akash Keluth Chavan, Sangwan Kim, Laís Pereira Ferreira, Sophie Vacant, Nowshin Sayara, Haruo Suzuki, Madeline Leahy, Juan C. Severyn, Sierra Vincent, Masaru Tomita, Maliha Mamun, Lucinda B. Davenport, Gabriella Oken, Dagmara Lewandowska, Gustavo Adolfo Malca Salas, Andrii Kuklin, Tyler Wong, Charlie Feigin, Eric Minwei Liu, Sonia L. Ghose, Daniela Bezdan, Antonietta La Storia, Juan P. Escalera-Antezana, Nuno Rufino de Sousa, Samuel M. Gerner, Weill Cornell Medicine [New York], Icahn School of Medicine at Mount Sinai [New York] (MSSM), Genome Institute of Singapore (GIS), Centre for Genomic Regulation [Barcelona] (CRG), Universitat Pompeu Fabra [Barcelona] (UPF)-Centro Nacional de Analisis Genomico [Barcelona] (CNAG), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), AUTRES, Massachusetts Institute of Technology (MIT), Indian Statistical Institute [Kolkata], University of Minnesota System, Universidad Andrés Bello [Santiago] (UNAB), California State University [Sacramento], University of Naples Federico II, University of Hawaii, Institut méditerranéen d'océanologie (MIO), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Toulon (UTLN), Medical Genomics Group, University College of London [London] (UCL)-UCL Cancer Institute, Norwegian Defence Research Establishment (FFI), Lund University [Lund], Institute of Molecular Biology and Genetics of National Academy of Sciences of Ukraine, University of Vienna [Vienna], King‘s College London, University of Colorado [Boulder], Institut Pasteur de Montevideo, Réseau International des Instituts Pasteur (RIIP), Institut Pasteur Korea - Institut Pasteur de Corée, Fudan University [Shanghai], City University of Hong Kong [Hong Kong] (CUHK), Stockholm University, University of Maryland School of Medicine, University of Maryland System, Fundação Oswaldo Cruz (FIOCRUZ), University of São Paulo (USP), Instituto de Patologia e Imunologia Molecular da Universidade do Porto (IPATIMUP), Barcelona Institute of Science and Technology (BIST), Elizade University, Acibadem Mehmet Ali Aydınlar University, Paléogénomique microbienne - Microbial paleogenomics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Robert Koch Institute [Berlin] (RKI), East China Normal University [Shangaï] (ECNU), Cairo University, Vietnamese-German Center for Medical Research, Keio University, Université du Vermont, Universidad del Desarrollo, University of Sofia, University of Alaska [Fairbanks] (UAF), Universitätsklinikum Tübingen - University Hospital of Tübingen, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Corporación Corpogen-Research Center, Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Sorbonne Université (SU)-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), Weill Cornell Medicine [Cornell University], Cornell University [New York], University of Naples Federico II = Università degli studi di Napoli Federico II, Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Fundação Oswaldo Cruz / Oswaldo Cruz Foundation (FIOCRUZ), Universidade de São Paulo = University of São Paulo (USP), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Софийски университет = Sofia University, Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-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), Universidad Andrés Bello - UNAB (CHILE), Acibadem University Dspace, Danko, D., Bezdan, D., Afshin, E. E., Ahsanuddin, S., Bhattacharya, C., Butler, D. J., Chng, K. R., Donnellan, D., Hecht, J., Jackson, K., Kuchin, K., Karasikov, M., Lyons, A., Mak, L., Meleshko, D., Mustafa, H., Mutai, B., Neches, R. Y., Ng, A., Nikolayeva, O., Nikolayeva, T., Png, E., Ryon, K. A., Sanchez, J. L., Shaaban, H., Sierra, M. A., Thomas, D., Young, B., Abudayyeh, O. O., Alicea, J., Bhattacharyya, M., Blekhman, R., Castro-Nallar, E., Canas, A. M., Chatziefthimiou, A. D., Crawford, R. W., De Filippis, F., Deng, Y., Desnues, C., Dias-Neto, E., Dybwad, M., Elhaik, E., Ercolini, D., Frolova, A., Gankin, D., Gootenberg, J. S., Graf, A. B., Green, D. C., Hajirasouliha, I., Hastings, J. J. A., Hernandez, M., Iraola, G., Jang, S., Kahles, A., Kelly, F. J., Knights, K., Kyrpides, N. C., Labaj, P. P., Lee, P. K. H., Leung, M. H. Y., Ljungdahl, P. O., Mason-Buck, G., Mcgrath, K., Meydan, C., Mongodin, E. F., Moraes, M. O., Nagarajan, N., Nieto-Caballero, M., Noushmehr, H., Oliveira, M., Ossowski, S., Osuolale, O. O., Ozcan, O., Paez-Espino, D., Rascovan, N., Richard, H., Ratsch, G., Schriml, L. M., Semmler, T., Sezerman, O. U., Shi, L., Shi, T., Siam, R., Song, L. H., Suzuki, H., Court, D. S., Tighe, S. W., Tong, X., Udekwu, K. I., Ugalde, J. A., Valentine, B., Vassilev, D. I., Vayndorf, E. M., Velavan, T. P., Wu, J., Zambrano, M. M., Zhu, J., Zhu, S., Mason, C. E., Abdullah, N., Abraao, M., Adel, A. -H., Afaq, M., Al-Quaddoomi, F. S., Alam, I., Albuquerque, G. E., Alexiev, A., Ali, K., Alvarado-Arnez, L. E., Aly, S., Amachee, J., Amorim, M. G., Ampadu, M., Amran, M. A. -F., An, N., Andrew, W., Andrianjakarivony, H., Angelov, M., Antelo, V., Aquino, C., Aranguren, A., Araujo, L. F., Vasquez Arevalo, H. F., Arevalo, J., Arnan, C., Alvarado Arnez, L. E., Arredondo, F., Arthur, M., Asenjo, F., Aung, T. S., Auvinet, J., Aventin, N., Ayaz, S., Baburyan, S., Bakere, A. -M., Bakhl, K., Bartelli, T. F., Batdelger, E., Baudon, F., Becher, K., Bello, C., Benchouaia, M., Benisty, H., Benoiston, A. -S., Benson, J., Benitez, D., Bernardes, J., Bertrand, D., Beurmann, S., Bitard-Feildel, T., Bittner, L., Black, C., Blanc, G., Blyther, B., Bode, T., Boeri, J., Boldgiv, B., Bolzli, K., Bordigoni, A., Borrelli, C., Bouchard, S., Bouly, J. -P., Boyd, A., Branco, G. P., Breschi, A., Brindefalk, B., Brion, C., Briones, A., Buczansla, P., Burke, C. M., Burrell, A., Butova, A., Buttar, I., Bynoe, J., Bonigk, S., Boifot, K. O., Caballero, H., Cai, X. W., Calderon, D., Cantillo, A., Carbajo, M., Carbone, A., Cardenas, A., Carrillo, K., Casalot, L., Castro, S., Castro, A. V., Castro, A., Castro, A. V. B., Cawthorne, S., Cedillo, J., Chaker, S., Chalangal, J., Chan, A., Chasapi, A. I., Chatziefthimiou, S., Chaudhuri, S. R., Chavan, A. K., Chavez, F., Chem, G., Chen, X., Chen, M., Chen, J. -W., Chernomoretz, A., Chettouh, A., Cheung, D., Chicas, D., Chiu, S., Choudhry, H., Chrispin, C., Ciaramella, K., Cifuentes, E., Cohen, J., Coil, D. A., Collin, S., Conger, C., Conte, R., Corsi, F., Cossio, C. N., Costa, A. F., Cuebas, D., D'Alessandro, B., Dahlhausen, K. E., Darling, A. E., Das, P., Davenport, L. B., David, L., Davidson, N. R., Dayama, G., Delmas, S., Deng, C. K., Dequeker, C., Desert, A., Devi, M., Dezem, F. S., Dias, C. N., Donahoe, T. R., Dorado, S., Dorsey, L., Dotsenko, V., Du, S., Dutan, A., Eady, N., Eisen, J. A., Elaskandrany, M., Epping, L., Escalera-Antezana, J. P., Ettinger, C. L., Faiz, I., Fan, L., Farhat, N., Faure, E., Fauzi, F., Feigin, C., Felice, S., Ferreira, L. P., Figueroa, G., Fleiss, A., Flores, D., Velasco Flores, J. L., Fonseca, M. A. S., Foox, J., Forero, J. C., Francis, A., French, K., Fresia, P., Friedman, J., Fuentes, J. J., Galipon, J., Garcia, M., Garcia, L., Garcia, C., Geiger, A., Gerner, S. M., Ghose, S. L., Giang, D. P., Gimenez, M., Giovannelli, D., Githae, D., Gkotzis, S., Godoy, L., Goldman, S., Gonnet, G. H., Gonzalez, J., Gonzalez, A., Gonzalez-Poblete, C., Gray, A., Gregory, T., Greselle, C., Guasco, S., Guerra, J., Gurianova, N., Haehr, W., Halary, S., Hartkopf, F., Hawkins-Zafarnia, A., Hazrin-Chong, N. H., Helfrich, E., Hell, E., Henry, T., Hernandez, S., Hernandez, P. L., Hess-Homeier, D., Hittle, L. E., Hoan, N. X., Holik, A., Homma, C., Hoxie, I., Huber, M., Humphries, E., Hyland, S., Hassig, A., Hausler, R., Husser, N., Petit, R. A., Iderzorig, B., Igarashi, M., Iqbal, S. B., Ishikawa, S., Ishizuka, S., Islam, S., Islam, R., Ito, K., Ito, S., Ito, T., Ivankovic, T., Iwashiro, T., Jackson, S., Jacobs, J., James, M., Jaubert, M., Jerier, M. -L., Jiminez, E., Jinfessa, A., De Jong, Y., Joo, H. W., Jospin, G., Kajita, T., Ahmad Kassim, A. S., Kato, N., Kaur, A., Kaur, I., de Souza Gomes Kehdy, F., Khadka, V. S., Khan, S., Khavari, M., Ki, M., Kim, G., Kim, H. J., Kim, S., King, R. J., Kolomonaco, G., Koag, E., Kobko-Litskevitch, N., Korshevniuk, M., Kozhar, M., Krebs, J., Kubota, N., Kuklin, A., Kumar, S. S., Kwong, R., Kwong, L., Lafontaine, I., Lago, J., Lai, T. Y., Laine, E., Laiola, M., Lakhneko, O., Lamba, I., de Lamotte, G., Lannes, R., De Lazzari, E., Leahy, M., Lee, H., Lee, Y., Lee, L., Lemaire, V., Leong, E., Lewandowska, D., Li, C., Liang, W., Lin, M., Lisboa, P., Litskevitch, A., Liu, E. M., Liu, T., Livia, M. A., Lo, Y. H., Losim, S., Loubens, M., Lu, J., Lykhenko, O., Lysakova, S., Mahmoud, S., Majid, S. A., Makogon, N., Maldonado, D., Mallari, K., Malta, T. M., Mamun, M., Manoir, D., Marchandon, G., Marciniak, N., Marinovic, S., Marques, B., Mathews, N., Matsuzaki, Y., Matthys, V., May, M., Mccomb, E., Meagher, A., Melamed, A., Menary, W., Mendez, K. N., Mendez, A., Mendy, I. M., Meng, I., Menon, A., Menor, M., Meoded, R., Merino, N., Miah, K., Mignotte, M., Miketic, T., Miranda, W., Mitsios, A., Miura, R., Miyake, K., Moccia, M. D., Mohan, N., Mohsin, M., Moitra, K., Moldes, M., Molina, L., Molinet, J., Molomjamts, O. -E., Moniruzzaman, E., Moon, S., de Oliveira Moraes, I., Moreno, M., Mosella, M. S., Moser, J. W., Mozsary, C., Muehlbauer, A. L., Muner, O., Munia, M., Munim, N., Muscat, M., Mustac, T., Munoz, C., Nadalin, F., Naeem, A., Nagy-Szakal, D., Nakagawa, M., Narce, A., Nasu, M., Navarrete, I. G., Naveed, H., Nazario, B., Nedunuri, N. R., Neff, T., Nesimi, A., Ng, W. C., Ng, S., Nguyen, G., Ngwa, E., Nicolas, A., Nicolas, P., Nika, A., Noorzi, H., Nosrati, A., Nunes, D. N., O'Brien, K., O'Hara, N. B., Oken, G., Olawoyin, R. A., Oliete, J. Q., Olmeda, K., Oluwadare, T., Oluwadare, I. A., Ordioni, N., Orpilla, J., Orrego, J., Ortega, M., Osma, P., Osuolale, I. O., Osuolale, O. M., Ota, M., Oteri, F., Oto, Y., Ounit, R., Ouzounis, C. A., Pakrashi, S., Paras, R., Pardo-Este, C., Park, Y. -J., Pastuszek, P., Patel, S., Pathmanathan, J., Patrignani, A., Perez, M., Peros, A., Persaud, S., Peters, A., Phillips, A., Pineda, L., Pizzi, M. P., Plaku, A., Pompa-Hogan, B., Portilla, M. G., Posada, L., Priestman, M., Prithiviraj, B., Priya, S., Pugdeethosal, P., Pugh, C. E., Pulatov, B., Pupiec, A., Pyrshev, K., Qing, T., Rahiel, S., Rahmatulloev, S., Rajendran, K., Ramcharan, A., Ramirez-Rojas, A., Rana, S., Ratnanandan, P., Read, T. D., Rehrauer, H., Richer, R., Rivera, A., Rivera, M., Robertiello, A., Robinson, C., Rodriguez, P., Rojas, N. A., Roldan, P., Rosario, A., Roth, S., Ruiz, M., Boja Ruiz, S. E., Russell, K., Rybak, M., Sabedot, T. S., Sabina, M., Saito, I., Saito, Y., Malca Salas, G. A., Salazar, C., San, K. M., Sanchez, J., Sanchir, K., Sankar, R., de Souza Santos, P. T., Saravi, Z., Sasaki, K., Sato, Y., Sato, M., Sato, S., Sato, R., Sato, K., Sayara, N., Schaaf, S., Schacher, O., Schinke, A. -L. M., Schlapbach, R., Schori, C., Schriml, J. R., Segato, F., Sepulveda, F., Serpa, M. S., De Sessions, P. F., Severyn, J. C., Shakil, M., Shalaby, S., Shari, A., Shim, H., Shirahata, H., Shiwa, Y., Da Silva, O., Silva, J. M., Simon, G., Singh, S. K., Sluzek, K., Smith, R., So, E., Andreu Somavilla, N., Sonohara, Y., Rufino de Sousa, N., Souza, C., Sperry, J., Sprinsky, N., Stark, S. G., La Storia, A., Suganuma, K., Suliman, H., Sullivan, J., Supie, A. A. M., Suzuki, C., Takagi, S., Takahara, F., Takahashi, N., Takahashi, K., Takeda, T., Takenaka, I. K., Tanaka, S., Tang, A., Man Tang, Y., Tarcitano, E., Tassinari, A., Taye, M., Terrero, A., Thambiraja, E., Thiebaut, A., Thomas, S., Thomas, A. M., Togashi, Y., Togashi, T., Tomaselli, A., Tomita, M., Tomita, I., Toth, O., Toussaint, N. C., Tran, J. M., Truong, C., Tsonev, S. I., Tsuda, K., Tsurumaki, T., Tuz, M., Tymoshenko, Y., Urgiles, C., Usui, M., Vacant, S., Vann, L. E., Velter, F., Ventorino, V., Vera-Wolf, P., Vicedomini, R., Suarez-Villamil, M. A., Vincent, S., Vivancos-Koopman, R., Wan, A., Wang, C., Warashina, T., Watanabe, A., Weekes, S., Werner, J., Westfall, D., Wieler, L. H., Williams, M., Wolf, S. A., Wong, B., Wong, Y. L., Wong, T., Wright, R., Wunderlin, T., Yamanaka, R., Yang, J., Yano, H., Yeh, G. C., Yemets, O., Yeskova, T., Yoshikawa, S., Zafar, L., Zhang, Y., Zhang, S., Zhang, A., Zheng, Y., and Zubenko, S.
Subjects: Urban Population, Drug Resistance, Sequence assembly, Microbiologia, microbiome, global health, computer.software_genre, Medical and Health Sciences, shotgun sequencing, BGC, 0302 clinical medicine, Databases, Genetic, 11. Sustainability, Global health, AMR, 11 Medical and Health Sciences, ComputingMilieux_MISCELLANEOUS, 0303 health sciences, built environment, metagenome, antimicrobial resistance, NGS, de novo assembly, biology, Shotgun sequencing, Microbiota, built Environment, Bacterial, Biodiversity, Biological Sciences, 3. Good health, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Infection, Biotechnology, Geospatial analysis, [SDV.BID]Life Sciences [q-bio]/Biodiversity, Article, General Biochemistry, Genetics and Molecular Biology, Databases, 03 medical and health sciences, Antibiotic resistance, Genetic, Drug Resistance, Bacterial, International MetaSUB Consortium, Genetics, Humans, Microbiome, 030304 developmental biology, Human Genome, 06 Biological Sciences, 15. Life on land, biology.organism_classification, Resistènica als medicaments antiinfecciosos, SAÚDE PÚBLICA, Genòmica, 13. Climate action, Evolutionary biology, Metagenomics, Antimicrobial Resistance, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, computer, 030217 neurology & neurosurgery, Archaea, Developmental Biology
Abstract: Summary 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., Graphical abstract, Highlights • Cities possess a consistent “core” set of non-human microbes • Urban microbiomes echo important features of cities and city-life • Antimicrobial resistance genes are widespread in cities • Cities contain many novel bacterial and viral species, This systematic, worldwide catalog of urban microbiomes represents a metagenomic atlas important for understanding the ecology, virulence, and antibiotic resistance of city-specific microbial communities.
Published: 2021
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25. Correction for Vangay et al., 'Microbiome Metadata Standards: Report of the National Microbiome Data Collaborative’s Workshop and Follow-On Activities'

Author: Pajau Vangay, Josephine Burgin, Anjanette Johnston, Kristen L. Beck, Daniel C. Berrios, Kai Blumberg, Shane Canon, Patrick Chain, John-Marc Chandonia, Danielle Christianson, Sylvain V. Costes, Joan Damerow, William D. Duncan, Jose Pablo Dundore-Arias, Kjiersten Fagnan, Jonathan M. Galazka, Sean M. Gibbons, David Hays, Judson Hervey, Bin Hu, Bonnie L. Hurwitz, Pankaj Jaiswal, Marcin P. Joachimiak, Linda Kinkel, Joshua Ladau, Stanton L. Martin, Lee Ann McCue, Kayd Miller, Nigel Mouncey, Chris Mungall, Evangelos Pafilis, T. B. K. Reddy, Lorna Richardson, Simon Roux, Lynn M. Schriml, Justin P. Shaffer, Jagadish Chandrabose Sundaramurthi, Luke R. Thompson, Ruth E. Timme, Jie Zheng, Elisha M. Wood-Charlson, and Emiley A. Eloe-Fadrosh
Subjects: Microbiology, QR1-502
Published: 2021
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26. Abstract 1193: Enhancing pediatric cancer variant curation and representation through standardized classification and automation

Author: Jason Saliba, Jake Lever, Kilannin Krysiak, Arpad Danos, Alex Wagner, Heather E. Williams, Laveniya Satgunaseelan, David Meredith, Cameron J. Grisdale, Chimene Kesserwan, Jianling Ji, Shruti Rao, Catherine Cottrell, Alanna Church, Mark Evans, Yasmina Jaufeerally-Fakim, Lynn M. Schriml, Angshumoy Roy, Gordana Raca, Malachi Griffith, and Obi L. Griffith
Subjects: Cancer Research, Oncology
Abstract: Childhood cancers present unique challenges for variant interpretation in a clinical context due to their rarity, low mutation burden, diversity of molecular alterations, and heterogeneity among patients. Consequently, genes and variants associated with childhood tumors are under-represented in public cancer databases and knowledgebases. A focused effort is needed for the structured curation of genetic variant-level data to document diagnostic, prognostic, and therapeutic biomarkers for childhood cancers. The Pediatric Cancer Curation Advancement Subcommittee (PCCAS), a collaboration between the Clinical Interpretations of Variants in Cancer knowledgebase (CIViC; civicdb.org), the ClinGen Somatic Pediatric Cancer Taskforce, Disease Ontology (DO; disease-ontology.org) and CIViCmine (bionlp.bcgsc.ca/civicmine/), is addressing this challenge through enhanced curation, tagging, and automation. PCCAS created a pediatric specific curation standard operating procedure (SOP) to harmonize pediatric evidence entered in CIViC. Our SOP provides general guidance and considerations to define and classify childhood cancers and to represent childhood cancer evidence on a spectrum of age-related incidence and presentation. For instance, pediatric evidence in CIViC is now tagged using Human Phenotype Ontology (HPO) age of onset terms, allowing pediatric evidence to be easily searched, tracked, and sorted. We also initiated the addition of new age of onset terms to enhance the granularity of these tags. WHO ICD-O nomenclature has been chosen for pediatric disease classification in CIViC. ICD-O provides updated terminology including specific genetic subtypes, which are important in pediatric cancers where their underlying molecular profiles often define the disease. To aid curator selection of disease, we verified pediatric relevant ICD-O terms inclusion in DO and restructured DO disease hierarchies to ensure proper mapping. CIViC highlights our pediatric cancer initiative in multiple areas including a homepage feature linking directly to a dedicated pediatric advanced search that returns all evidence tagged with pediatric or young adult age of onset. Most importantly, our childhood specific SOP and initiatives are included in all ClinGen Somatic Cancer and CIViC training sessions for consistent implementation. CIViCmine supports CIViC by using natural language processing to identify important cancer biomarkers in the literature. To better identify pediatric biomarkers, we are adapting and refining CIViCmine to use MeSH terms and other approaches to enhance accuracy in the identification of childhood evidence in both the literature and CIViC. In conclusion, implementation of these procedures, features, and automation are pushing to make childhood cancer variant evidence more accessible and interpretable. Citation Format: Jason Saliba, Jake Lever, Kilannin Krysiak, Arpad Danos, Alex Wagner, Heather E. Williams, Laveniya Satgunaseelan, David Meredith, Cameron J. Grisdale, Chimene Kesserwan, Jianling Ji, Shruti Rao, Catherine Cottrell, Alanna Church, Mark Evans, Yasmina Jaufeerally-Fakim, Lynn M. Schriml, Angshumoy Roy, Gordana Raca, Malachi Griffith, Obi L. Griffith. Enhancing pediatric cancer variant curation and representation through standardized classification and automation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1193.
Published: 2022
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27. Minimum Information about an Uncultivated Virus Genome (MIUViG)

Author: Seth R. Bordenstein, Frederik Schulz, Pelin Yilmaz, Rebecca Vega Thurber, Natalia Ivanova, Christelle Desnues, Shinichi Sunagawa, Karyna Rosario, Simon Roux, Steven W. Wilhelm, Nicole S. Webster, Mart Krupovic, Lisa Zeigler Allen, Catherine Putonti, K. Eric Wommack, Tanja Woyke, Eugene V. Koonin, Joanne B. Emerson, Jed A. Fuhrman, Hiroyuki Ogata, Ramy K. Aziz, Arvind Varsani, Marie-Agnès Petit, Bonnie L. Hurwitz, Evelien M. Adriaenssens, Andrew M. Kropinski, Katrine Whiteson, Thomas Rattei, Kyung Bum Lee, Peer Bork, David Paez-Espino, Mark J. Young, Jens H. Kuhn, Ben Temperton, Rebecca A. Daly, Natalya Yutin, Emiley A. Eloe-Fadrosh, Manuel Martinez-Garcia, Curtis A. Suttle, Susannah G. Tringe, Alejandro Reyes, Bas E. Dutilh, Nikos C. Kyrpides, Rex R. Malmstrom, Ilene Karsch Mizrachi, Kelly C. Wrighton, Rob Lavigne, Mya Breitbart, Lynn M. Schriml, Philip Hugenholtz, Melissa B. Duhaime, François Enault, Pascal Hingamp, Francisco Rodriguez-Valera, Clara Amid, Matthew B. Sullivan, Jessica M. Labonté, Grieg F. Steward, J. Rodney Brister, Takashi Yoshida, Guy Cochrane, DOE Joint Genome Institute [Walnut Creek], University of Liverpool, Theoretical Biology & Bioinformatics [Utrecht], University Medical Center [Utrecht], Radboud University Medical Center [Nijmegen], National Center for Biotechnology Information (NCBI), University of Guelph, Biologie Moléculaire du Gène chez les Extrêmophiles (BMGE), Institut Pasteur [Paris] (IP), National Institute of Allergy and Infectious Diseases [Bethesda] (NIAID-NIH), National Institutes of Health [Bethesda] (NIH), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Arizona State University [Tempe] (ASU), University of Cape Town, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Cairo University, Vanderbilt University [Nashville], European Molecular Biology Laboratory [Heidelberg] (EMBL), University of South Florida [Tampa] (USF), Colorado State University [Fort Collins] (CSU), Microbes évolution phylogénie et infections (MEPHI), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), University of Michigan [Ann Arbor], University of Michigan System, University of California [Davis] (UC Davis), University of California (UC), Laboratoire Microorganismes : Génome et Environnement (LMGE), Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), University of Southern California (USC), Institut méditerranéen d'océanologie (MIO), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), University of Queensland [Brisbane], University of Arizona, Texas A&M University [Galveston], National Institute of Genetics (NIG), Universidad de Alicante, Kyoto University, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, University of Chicago, Department of Microbiology and Ecosystem Science [Vienna], University of Vienna [Vienna], Universidad de los Andes [Bogota] (UNIANDES), Universidad Miguel Hernández [Elche] (UMH), University of Maryland School of Medicine, University of Maryland System, University of Hawai‘i [Mānoa] (UHM), Ohio State University [Columbus] (OSU), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), University of British Columbia (UBC), University of Exeter, Oregon State University (OSU), Australian Institute of Marine Science [Townsville] (AIMS Townsville), Australian Institute of Marine Science (AIMS), University of California [Irvine] (UC Irvine), The University of Tennessee [Knoxville], University of Delaware [Newark], Max Planck Institute for Marine Microbiology, Max-Planck-Gesellschaft, Montana State University (MSU), J. Craig Venter Institute, University of California [San Diego] (UC San Diego), This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under US Department of Energy Contract No. DE-AC02-05CH11231 for S.R., the Netherlands Organization for Scientific Research (NWO) Vidi grant 864.14.004 for B.E.D., the Intramural Research Program of the National Library of Medicine, National Institutes of Health for E.V.K., I.K.M., J.R.B. and N.Y., the Virus-X project (EU Horizon 2020, No. 685778) for F.E. and M.K., Battelle Memorial Institute's prime contract with the US National Institute of Allergy and Infectious Diseases (NIAID) under Contract No. HHSN272200700016I for J.H.K., the GOA grant 'Bacteriophage Biosystems' from KU Leuven for R.L., the European Molecular Biology Laboratory for C.A. and G.R.C., Cairo University Grant 2016-57 for R.K.A., National Science Foundation award 1456778, National Institutes of Health awards R01 AI132581 and R21 HD086833, and The Vanderbilt Microbiome Initiative award for S.R.B., National Science Foundation awards DEB-1239976 for M.B. and K.R. and DEB-1555854 for M.B., the NSF Early Career award DEB-1555854 and NSF Dimensions of Biodiversity #1342701 for K.C.W. and R.A.D., the Agence Nationale de la Recherche JCJC grant ANR-13-JSV6-0004 and Investissements d'Avenir Méditerranée Infection 10-IAHU-03 for C.D., the Gordon and Betty Moore Foundation Marine Microbiology Initiative No. 3779 and the Simons Foundation for J.A.F., the French government 'Investissements d'Avenir' program OCEANOMICS ANR-11-BTBR-0008 and European FEDER Fund 1166-39417 for P. Hingamp, Australian Research Council Laureate Fellowship FL150100038 to P. Hugenholtz the National Science Foundation award 1801367 and C-DEBI Research Grant for J.M.L., the Gordon and Betty Moore Foundation grant 5334 and Ministry of Economy and Competitivity refs. CGL2013-40564-R and SAF2013-49267-EXP for M.M.-G., the Grant-in-Aid for Scientific Research on Innovative Areas from the Ministry of Education, Culture, Science, Sports, and Technology (MEXT) of Japan No. 16H06429, 16K21723, and 16H06437 for H.O. and T.Y., National Science Foundation award DBI-1661357 to C.P., the Ministry of Economy and Competitivity ref CGL2016-76273-P (cofunded with FEDER funds) for F.R.-V., the Gordon and Betty Moore Foundation awards 3305 and 3790 and NSF Biological Oceanography OCE 1536989 for M.B.S., the ETH Zurich and Helmut Horten Foundation and the Novartis Foundation for Medical-Biological Research (17B077) for S.S., a BIOS-SCOPE award from Simons Foundation International and NERC award NE/P008534/1 to B.T., NSF Biological Oceanography Grant 1635913 for R.V.T., the Australian Research Council Future Fellowship FT120100480 for N.S.W., a Gilead Sciences Cystic Fibrosis Research Scholarship for K.L.W., Gordon and Better Moore Foundation Grant 4971 for S.W.W., the NSF EPSCoR grant 1736030 for K.E.W., the National Science Foundation award DEB-4W4596 and National Institutes of Health award R01 GM117361 for M.J.Y., the Gordon and Betty Moore Foundation No. 7000 and the National Oceanic and Atmospheric Administration (NOAA) under award NA15OAR4320071 for L.Z.A. DDBJ is supported by ROIS and MEXT. The work conducted by the US Department of Energy Joint Genome Institute is supported by the Office of Science of the US Department of Energy under contract no. DE-AC02-05CH11231. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the US Department of Health and Human Services or of the institutions and companies affiliated with the authors. B.E.D., A.K., M.K., J.H.K., R.L. and A.V. are members of the ICTV Executive Committee, but the views and opinions expressed are those of the authors and not those of the ICTV., Universidad de Alicante. Departamento de Fisiología, Genética y Microbiología, Ecología Microbiana Molecular, Institut Pasteur [Paris], University of South Florida (USF), University of California, Laboratoire Microorganismes : Génome et Environnement - Clermont Auvergne (LMGE), Université Clermont Auvergne (UCA)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Kyoto University [Kyoto], Universidad de los Andes [Bogota], Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich), University of California [Irvine] (UCI), J Craig Venter Institute, Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Toulon (UTLN), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Sub Bioinformatics, and Theoretical Biology and Bioinformatics
Subjects: [SDV]Life Sciences [q-bio], Microbiología, 2.2 Factors relating to physical environment, Applied Microbiology and Biotechnology, Genome, 0302 clinical medicine, Databases, Genetic, Tumours of the digestive tract Radboud Institute for Molecular Life Sciences [Radboudumc 14], phage, Viral, 0303 health sciences, Genetic Databases, Environmental microbiology, pipeline, Genome project, dynamics, Genomics, annotation, Viruses, Molecular Medicine, tacomony, Infection, Genetic databases, Biotechnology, Virus Cultivation, In silico, Biomedical Engineering, Phage biology, Bioengineering, Computational biology, Genome, Viral, Biology, Virus, dna viruses, Article, Uncultivated virus genomes, Databases, 03 medical and health sciences, Annotation, Genetic, Virology, MD Multidisciplinary, Genetics, 030304 developmental biology, Human Genome, Biological classification, commitee, prediction, Metagenomics, Minimum Information about any (x) Sequence (MIxS), 030217 neurology & neurosurgery, discovery
Abstract: We present an extension of the Minimum Information about any (x) Sequence (MIxS) standard for reporting sequences of uncultivated virus genomes. Minimum Information about an Uncultivated Virus Genome (MIUViG) standards were developed within the Genomic Standards Consortium framework and include virus origin, genome quality, genome annotation, taxonomic classification, biogeographic distribution and in silico host prediction. Community-wide adoption of MIUViG standards, which complement the Minimum Information about a Single Amplified Genome (MISAG) and Metagenome-Assembled Genome (MIMAG) standards for uncultivated bacteria and archaea, will improve the reporting of uncultivated virus genomes in public databases. In turn, this should enable more robust comparative studies and a systematic exploration of the global virosphere., Nature Biotechnology, 37 (1), ISSN:1546-1696, ISSN:1087-0156
Published: 2018
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28. COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model

Author: Daniel Lyman, Edmund Cauley, Raja Mazumder, Darren A. Natale, Ashia Joseph, Keith A. Crandall, Nikhita Gogate, Robel Kahsay, Amanda Bell, Lynn M. Schriml, and Sabyasach Sen
Subjects: COVID-19 biomarkers, AcademicSubjects/SCI01060, Coronavirus disease 2019 (COVID-19), Population, Disease, Bioinformatics, metabolic syndrome, Article, 03 medical and health sciences, 0302 clinical medicine, Immune system, cancer, Medicine, education, Molecular Biology, 030304 developmental biology, 0303 health sciences, education.field_of_study, SARS-CoV-2, business.industry, COVID-19, Cancer, vascular disease, medicine.disease, 030220 oncology & carcinogenesis, Problem Solving Protocol, Biomarker (medicine), Cancer biomarkers, Metabolic syndrome, business, Information Systems
Abstract: Scientists, medical researchers, and health care workers have mobilized worldwide in response to the outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; SCoV2). Preliminary data have captured a wide range of host responses, symptoms, and lingering problems post-recovery within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, co-morbidities, genetics, and other factors. As COVID-19-related data continue to accumulate from disparate groups, the heterogeneous nature of these datasets poses challenges for efficient extrapolation of meaningful observations, hindering translation of information into clinical applications. Attempts to utilize, analyze, or combine biomarker datasets from multiple sources have shown to be inefficient and complicated, without a unifying resource. As such, there is an urgent need within the research community for the rapid development of an integrated and harmonized COVID-19 Biomarker Knowledgebase. By leveraging data collection and integration methods, backed by a robust data model developed to capture cancer biomarker data we have rapidly crowdsourced the collection and harmonization of COVID-19 biomarkers. Our resource currently has 138 unique biomarkers. We found multiple instances of the same biomarker substance being suggested as multiple biomarker types during our extensive cross-validation and manual curation. As a result, our Knowledgebase currently has 265 biomarker type combinations. Every biomarker entry is made comprehensive by bringing in together ancillary data from multiple sources such as biomarker accessions (canonical UniProtKB accession, PubChem Compound ID, Cell Ontology ID, Protein Ontology ID, NCI Thesaurus Code, and Disease Ontology ID), BEST biomarker category, and specimen type (Uberon Anatomy Ontology) unified with ontology standards. Our preliminary observations show distinct trends in the collated biomarkers. Most biomarkers are related to the immune system (SAA,TNF-∝, and IP-10) or coagulopathies (D-dimer, antithrombin, and VWF) and a few have already been established as cancer biomarkers (ACE2, IL-6, IL-4 and IL-2). These trends align with proposed hypotheses of clinical manifestations compounding the complexity of COVID-19 pathobiology. We explore these trends as we put forth a COVID-19 biomarker resource that will help researchers and diagnosticians alike. All biomarker data are freely available from https://data.oncomx.org/covid19 .
Published: 2021
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29. Correction for Vangay et al., 'Microbiome Metadata Standards: Report of the National Microbiome Data Collaborative's Workshop and Follow-On Activities'

Author: Sylvain V. Costes, Jagadish Chandrabose Sundaramurthi, Evangelos Pafilis, Stanton L. Martin, Jonathan M. Galazka, Daniel C. Berrios, Pankaj Jaiswal, Sean M. Gibbons, Anjanette Johnston, John-Marc Chandonia, Jose Pablo Dundore-Arias, Justin P. Shaffer, Nigel J Mouncey, Kristen L. Beck, David Hays, Marcin P. Joachimiak, Luke R. Thompson, J. Damerow, Patrick S. G. Chain, Jie Zheng, Elisha M. Wood-Charlson, Christopher J. Mungall, Pajau Vangay, William D. Duncan, Lorna Richardson, Linda L. Kinkel, W. Judson Hervey, Bonnie L. Hurwitz, Simon Roux, Kayd Miller, Ruth Timme, Josephine Burgin, Lynn M. Schriml, Shane Canon, Lee Ann McCue, Bin Hu, Kjiersten Fagnan, T. B. K. Reddy, D. S. Christianson, Kai Blumberg, Joshua Ladau, and Emiley A. Eloe-Fadrosh
Subjects: Physiology, media_common.quotation_subject, metadata, microbiome, Art, Biochemistry, Microbiology, Computer Science Applications, Modeling and Simulation, Perspective, Genetics, ontology, Author Correction, data standards, Molecular Biology, Humanities, Ecology, Evolution, Behavior and Systematics, media_common
Abstract: Author(s): Vangay, Pajau; Burgin, Josephine; Johnston, Anjanette; Beck, Kristen L; Berrios, Daniel C; Blumberg, Kai; Canon, Shane; Chain, Patrick; Chandonia, John-Marc; Christianson, Danielle; Costes, Sylvain V; Damerow, Joan; Duncan, William D; Dundore-Arias, Jose Pablo; Fagnan, Kjiersten; Galazka, Jonathan M; Gibbons, Sean M; Hays, David; Hervey, Judson; Hu, Bin; Hurwitz, Bonnie L; Jaiswal, Pankaj; Joachimiak, Marcin P; Kinkel, Linda; Ladau, Joshua; Martin, Stanton L; McCue, Lee Ann; Miller, Kayd; Mouncey, Nigel; Mungall, Chris; Pafilis, Evangelos; Reddy, TBK; Richardson, Lorna; Roux, Simon; Schriml, Lynn M; Shaffer, Justin P; Sundaramurthi, Jagadish Chandrabose; Thompson, Luke R; Timme, Ruth E; Zheng, Jie; Wood-Charlson, Elisha M; Eloe-Fadrosh, Emiley A | Abstract: Volume 6, no. 1, e01194-20, 2021, https://doi.org/10.1128/mSystems.01194-20. The article byline and affiliation line should read as given in this correction.
Published: 2021

30. Response to Biesecker et al

Author: Ada Hamosh, Helen V. Firth, Peter N. Robinson, Joanna S. Amberger, Nara Sobreira, Melissa Haendel, Wayne W. Grody, Garry R. Cutting, David Valle, Richard A. Gibbs, Lynn M. Schriml, Jennifer E. Posey, Christopher G. Chute, Carol A. Bocchini, Carol J. Bult, Sonja A. Rasmussen, Harry C. Dietz, Joann Bodurtha, James R. Lupski, Nan Wu, and Alan F. Scott
Subjects: medicine.medical_specialty, Genetics, medicine, MEDLINE, Biology, Dermatology, Genetics (clinical)
Published: 2021
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31. Gemina: A Web-Based Epidemiology and Genomic Metadata System Designed to Identify Infectious Agents.

Author: Lynn M. Schriml, Aaron Gussman, Kathy Phillippy, Samuel V. Angiuoli, Kumar Hari, Alan Goates, Ravi Jain, Tanja Davidsen, Anurhada Ganapathy, Elodie Ghedin, Steven Salzberg, Owen White, and Neil Hall
Published: 2007
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32. Global Genetic Cartography of Urban Metagenomes and Anti-Microbial Resistance

Author: Danilo Ercolini, David C. Green, Mikhail Karasikov, Soojin Jang, Olga Nikolayeva, Elena M. Vayndorf, Kern Rei Chng, Per O. Ljungdahl, Gabriella Mason-Buck, Aspassia D. Chatziefthimiou, Heba Shaaban, Alina Frolova, Eduardo Castro-Nallar, Houtan Noushmehr, Nicolás Rascovan, Chandrima Bhattacharya, Christopher E. Mason, Valentine B, Haruo Suzuki, Niranjan Nagarajan, Ken McGrath, Iman Hajirasouliha, David Danko, Emmanuel F. Mongodin, André Kahles, Abigail Lyons, Dimitar Vassilev, Ran Blekhman, Maria A. Sierra, Harun Mustafa, Malay Bhattacharyya, Deng Y, Nikolayeva T, Daniela Bezdan, Jonathan S. Gootenberg, O. Osuolale, Jun Wu, Klas I. Udekwu, Russell Y. Neches, Shanfeng Zhu, Juan A. Ugalde, David Paez-Espino, Christelle Desnues, Özcan O, Jochen Hecht, Omar O. Abudayyeh, Xinzhao Tong, Le Huu Song, Jackson K, Beth Mutai, Denise Syndercombe Court, Ebrahim Afshinnekoo, Tieliu Shi, Gregorio Iraola, Gankin D, Thirumalaisamy P. Velavan, Leung Mhy, De Filippis F, Png E, Emmanuel Dias-Neto, Marius Dybwad, Robert W. Crawford, Amanda Hui Qi Ng, María Mercedes Zambrano, Hugues Richard, Alicea J, Sofia Ahsanuddin, Cañas Am, Osman Ugur Sezerman, Mark Hernandez, Daniel Butler, Paweł P. Łabaj, Sanchez Jl, Katerina Kuchin, Cem Meydan, Frank J. Kelly, Eran Elhaik, Donnellan D, Alexandra B. Graf, Lynn M. Schriml, Knights K, Domonique Thomas, Jun Zhu, Nikos C. Kyrpides, Scott Tighe, Krista Ryon, Manuela Oliveira, Ben Young, Lee Pkh, Marina Nieto-Caballero, Torsten Semmler, Gunnar Rätsch, Lauren Mak, Leming Shi, Milton Ozório Moraes, Dmitry Meleshko, Stephan Ossowski, Microbes évolution phylogénie et infections (MEPHI), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institut méditerranéen d'océanologie (MIO), and Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Toulon (UTLN)
Subjects: 0303 health sciences, education.field_of_study, Resistance (ecology), biology, 030306 microbiology, Transit system, Population, 15. Life on land, biology.organism_classification, [SDE.ES]Environmental Sciences/Environmental and Society, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, 03 medical and health sciences, Geography, 13. Climate action, Evolutionary biology, Metagenomics, Ecosystem, Microbiome, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, education, Built environment, 030304 developmental biology, Archaea
Abstract: Although studies have shown that urban environments and mass-transit systems have distinct genetic profiles, there are no systematic worldwide studies of these dense, human microbial ecosystems. To address this gap in knowledge, we created a global metagenomic and antimicrobial resistance (AMR) atlas of urban mass transit systems from 60 cities, spanning 4,728 samples and 4,424 taxonomically-defined microorganisms collected for three years. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance markers, and novel genetic elements, including 10,928 novel predicted viral species, 1302 novel bacteria, and 2 novel archaea. Urban microbiomes often resemble human commensal microbiomes from the skin and airways, but also contain a consistent “core” of 31 species which are predominantly not human commensal species. Samples show distinct microbial signatures which may be used to accurately predict properties of their city of origin including population, proximity to the coast, and taxonomic profile. These data also show that AMR density across cities varies by several orders of magnitude, including many AMRs present on plasmids with cosmopolitan distributions. Together, these results constitute a high-resolution, global metagenomic atlas, which enables the discovery of new genetic components of the built human environment, highlights potential forensic applications, and provides an essential first draft of the global AMR burden of the world’s cities.
Published: 2020

33. COVID-19 pandemic reveals the peril of ignoring metadata standards

Author: Granger G. Sutton, Bonnie L. Hurwitz, Folker Meyer, Robert D. Finn, Neil M Davies, Scott Tighe, Lynn M. Schriml, Emiley A. Eloe-Fadrosh, Ramona Walls, Christopher I. Hunter, Ilene Karsch Mizrachi, Nikos C. Kyrpides, Maria Chuvochina, Philip Hugenholtz, and Susanna-Assunta Sansone
Subjects: Statistics and Probability, 2019-20 coronavirus outbreak, Standards, 010504 meteorology & atmospheric sciences, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Genomic data, Pneumonia, Viral, Datasets as Topic, Library and Information Sciences, 01 natural sciences, Education, 03 medical and health sciences, Betacoronavirus, Viral genetics, Political science, Pandemic, Genetics, Humans, Viral, lcsh:Science, Pandemics, 030304 developmental biology, 0105 earth and related environmental sciences, 0303 health sciences, Metadata, SARS-CoV-2, Comment, COVID-19, Pneumonia, Genomics, Data science, Research data, Computer Science Applications, ComputingMethodologies_PATTERNRECOGNITION, Research management, lcsh:Q, Data integration, Statistics, Probability and Uncertainty, Coronavirus Infections, Information Systems
Abstract: Efficient response to the pandemic through the mobilization of the larger scientific community is challenged by the limited reusability of the available primary genomic data. Here, the Genomic Standards Consortium board highlights the essential need for contextual genomic data FAIRness, for empowering key data-driven biological questions.
Published: 2020
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34. A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer

Author: Susan M. Mockus, Deborah I. Ritter, David Tamborero, Obi L. Griffith, Jeremy Goecks, Gordana Raca, Damian T. Rieke, Georgia Mayfield, Nuria Lopez-Bigas, Jianjiong Gao, Kilannin Krysiak, Melissa A. Haendel, Ryan P Duren, Olivier Elemento, Kyle Ellrott, Jordi Deu-Pons, Adam A. Margolin, Brian Walsh, Tero Aittokallio, Michael Baudis, Rodrigo Dienstmann, Subha Madhavan, Julie A. McMurry, Sara E. Patterson, Ethan Cerami, Ozman Ugur Sezerman, Robert R. Freimuth, Beth A. Pitel, Nikolaus Schultz, Lynn M. Schriml, Alex H. Wagner, Jeremy L. Warner, Mark Lawler, Jacques S. Beckmann, Dmitriy Sonkin, Catherine Del Vecchio Fitz, Xuan Shirley Li, Debyani Chakravarty, Malachi Griffith, Acibadem University Dspace, Variant Interpretation for Cancer Consortium, Institut Català de la Salut, [Wagner AH, Krysiak K] Washington University School of Medicine, St. Louis, MO, USA. [Walsh B, Mayfield G] Oregon Health and Science University, Portland, OR, USA. [Tamborero D] Pompeu Fabra University, Barcelona, Spain. Karolinska Institute, Solna, Sweden. [Sonkin D] National Cancer Institute, Rockville, MD, USA. [Dienstmann R] Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain, and Vall d'Hebron Barcelona Hospital Campus
Subjects: Matching (statistics), Knowledge Bases, MEDLINE, Computational biology, Biology, 03 medical and health sciences, 0302 clinical medicine, Information Science::Computing Methodologies::Algorithms::Artificial Intelligence::Knowledge Bases [INFORMATION SCIENCE], terapéutica::medicina de precisión [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], SDG 3 - Good Health and Well-being, Neoplasms, fenómenos genéticos::variación genética [FENÓMENOS Y PROCESOS], Genetics research, Databases, Genetic, medicine, Genetics, Humans, Relevance (information retrieval), Medicina personalitzada, Precision Medicine, Ciencias de la información::metodologías computacionales::algoritmos::inteligencia artificial::bases del conocimiento [CIENCIA DE LA INFORMACIÓN], 030304 developmental biology, Cancer, Structure (mathematical logic), 0303 health sciences, Intel·ligència artificial - Aplicacions a la medicina, Interpretation (philosophy), Diploidy, Genetic Variation/genetics, Genomics/methods, Neoplasms/genetics, Precision Medicine/methods, Genetic Variation, Genomics, medicine.disease, 3. Good health, Genetic Phenomena::Genetic Variation [PHENOMENA AND PROCESSES], Genòmica, Precision oncology, 030220 oncology & carcinogenesis, Meta-analysis, Therapeutics::Precision Medicine [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], Analysis
Abstract: Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. 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 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases., This analysis presents a harmonized meta-knowledgebase to facilitate clinical interpretation of somatic genomic variants in cancer. This community-based project highlights the need for cooperative efforts to curate clinical interpretations of somatic variants for robust practice of precision oncology.
Published: 2020
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35. Author response: Wikidata as a knowledge graph for the life sciences

Author: Sabah Ul-Hasan, Andrew I. Su, Michael Mayers, Lynn M. Schriml, Daniel Mietchen, Ginger Tsueng, Thomas Shafee, Roger Tu, Malachi Griffith, Egon Willighagen, Ralf Stephan, Chunlei Wu, Toby S. Hudson, Gregory S. Stupp, Sarah M. Keating, Denise Slenter, Sebastian Burgstaller-Muehlbacher, Kristina Hanspers, Andra Waagmeester, Henning Hermjakob, Núria Queralt-Rosinach, Alexander R. Pico, Anders Riutta, Timothy E. Putman, Elvira Mitraka, Kevin Hybiske, Magnus Manske, Benjamin M. Good, Obi L. Griffith, and Katherine Thornton
Subjects: Information retrieval, Knowledge graph, Computer science
Published: 2020
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36. ECO, the Evidence & Conclusion Ontology: community standard for evidence information

Author: Shoshannah Ball, Rebecca Tauber, Marcus C. Chibucos, Pascale Gaudet, Elizabeth T. Hobbs, Elvira Mitraka, Dustin Olley, Suvarna Nadendla, Christopher J. Mungall, Ivan Erill, Deborah A. Siegele, Michelle G. Giglio, James C. Hu, James B. Munro, and Lynn M. Schriml
Subjects: Process (engineering), media_common.quotation_subject, Information Storage and Retrieval, Ontology (information science), Biology, Public domain, World Wide Web, 03 medical and health sciences, Annotation, Databases, User-Computer Interface, 0302 clinical medicine, Genetic, Sequence Analysis, Protein, Information and Computing Sciences, Databases, Genetic, Genetics, Database Issue, Animals, Humans, Function (engineering), 030304 developmental biology, media_common, 0303 health sciences, Internet, Protein, Assertion, Quality control, Computational Biology, Proteins, Biological Sciences, Gene Ontology, UniProt, Sequence Analysis, 030217 neurology & neurosurgery, Environmental Sciences, Developmental Biology
Abstract: The Evidence and Conclusion Ontology (ECO) contains terms (classes) that describe types of evidence and assertion methods. ECO terms are used in the process of biocuration to capture the evidence that supports biological assertions (e.g. gene product X has function Y as supported by evidence Z). Capture of this information allows tracking of annotation provenance, establishment of quality control measures and query of evidence. ECO contains over 1500 terms and is in use by many leading biological resources including the Gene Ontology, UniProt and several model organism databases. ECO is continually being expanded and revised based on the needs of the biocuration community. The ontology is freely available for download from GitHub (https://github.com/evidenceontology/) or the project’s website (http://evidenceontology.org/). Users can request new terms or changes to existing terms through the project’s GitHub site. ECO is released into the public domain under CC0 1.0 Universal.
Published: 2018
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37. Abstract 210: Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration

Author: Alex H. Wagner, Susanna Kiwala, Matthew C. Hiemenz, Kesserwan A. Chimene, Adam C. Coffman, Subha Madhavan, Arpad Danos, Lisa Dyer, Ryan J. Schmidt, Katherine A. Janeway, Deborah I. Ritter, Lynn M. Schriml, Huiling Xu, Donald W. Parsons, Erica K. Barnell, Malachi Griffith, Wan-Hsin Lin, Kristen L. Sund, Panieh Terraf, Alanna J. Church, Jianling Ji, Lana Sheta, Nurit Paz Yacov, Gordana Raca, Laura Corson, Jason Saliba, Kilannin Krysiak, Joshua F. McMichael, Laura Fuqua, Heather E. Williams, Xinjie Xu, Laveniya Satgunaseelan, Shashikant Kulkarni, Yasmine Akkari, Shruti Rao, Angshumoy Roy, Marian H. Harris, Rashmi Kanagal-Shamana, Ted Laetsch, Liying Zhang, Kevin E. Fisher, Jeffrey Jean, and Obi L. Griffith
Subjects: Cancer Research, Oncology, Cancer type, Childhood cancer, Genetic variants, Library science, Childhood disease, Disease, Sociology, Citation, Pediatric cancer, Representation (politics)
Abstract: Childhood cancers are driven by unique profiles of somatic genetic alterations, with a significant contribution from predisposing germline variants. Understanding the genomic landscape of pediatric cancers is complicated by their rarity, the heterogeneity of variation within a given disease, and the complex forms of structural variation they contain. Variants in childhood disease may differ from those in adult versions of the same cancer type, or may have different clinical significance. Currently, pediatric variants are underrepresented in cancer variant databases, and an urgent need exists for their publicly available expert curation. To address this, the Pediatric Cancer Taskforce (PCT) was formed within the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) (https://www.clinicalgenome.org/working-groups/somatic/). The PCT is a multi-institutional group of 39 members with broad experience in childhood cancer and variant curation, whose work consists of standardization and classification of genetic variants in pediatric cancers. The CIViC knowledgebase (www.civicdb.org) is a freely available resource for Clinical Interpretation of Variants in Cancer, which leverages public curation and expert moderation to address the problem of annotating the large volume of clinically actionable cancer variants. PCT curators work together with PCT expert members and the CIViC team on variant curation, and have submitted over 230 Evidence Items and over 10 Assertions to CIViC. To further address issues specific to pediatric curation, the PCT is working with CIViC to develop new pediatric-specific CIViC features and modifications of the data model that will aid in pediatric curation. A pediatric user interface, as well as representation of large scale structural and copy number variation are being developed for version two of CIViC, expected to be released in 1-2 years, which will enable curation of a new class of structural variants often encountered in pediatric cancer. A novel standard operating procedure for childhood cancer curation in CIViC is being developed by PCT experts, curators and the CIViC team. This SOP will cover topics including curation of structural variants, as well as pediatric-specific variant tiering guidelines which take into account the sparse nature of evidence in pediatric cases. A companion resource, CIViCmine (http://bionlp.bcgsc.ca/civicmine/), will be further developed to incorporate pediatric data. These and other joint efforts of the PCT and CIViC will significantly enhance pediatric variant representation for public use, to support the care of children with cancer. Citation Format: Arpad Danos, Wan-Hsin Lin, Jason Saliba, Angshumoy Roy, Alanna J. Church, Shruti Rao, Deborah Ritter, Kilannin Krysiak, Alex Wagner, Erica Barnell, Lana Sheta, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Laura Corson, Kevin Fisher, Heather E. Williams, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Kesserwan A. Chimene, Laura Fuqua, Lisa Dyer, Huiling Xu, Jeffrey Jean, Laveniya Satgunaseelan, Liying Zhang, Ted W. Laetsch, Donald W. Parsons, Ryan Schmidt, Lynn M. Schriml, Kristen L. Sund, Shashikant Kulkarni, Subha Madhavan, Xinjie Xu, Rashmi Kanagal-Shamana, Marian Harris, Yasmine Akkari, Nurit Paz Yacov, Panieh Terraf, Malachi Griffith, Obi L. Griffith, Gordana Raca. Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 210.
Published: 2021
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38. Wikidata as a FAIR knowledge graph for the life sciences

Author: Andra Waagmeester, Gregory Stupp, Sebastian Burgstaller-Muehlbacher, Benjamin M. Good, Malachi Griffith, Obi Griffith, Kristina Hanspers, Henning Hermjakob, Toby S. Hudson, Kevin Hybiske, Sarah M. Keating, Magnus Manske, Michael Mayers, Daniel Mietchen, Elvira Mitraka, Alexander R. Pico, Timothy Putman, Anders Riutta, Núria Queralt-Rosinach, Lynn M. Schriml, Thomas Shafee, Denise Slenter, Ralf Stephan, Katherine Thornton, Ginger Tsueng, Roger Tu, Sabah Ul-Hasan, Egon Willighagen, Chunlei Wu, and Andrew I. Su
Subjects: 0303 health sciences, business.industry, Computer science, Interoperability, World Wide Web, Open Biomedical Ontologies, 03 medical and health sciences, 0302 clinical medicine, Knowledge graph, Knowledge base, Synchronization (computer science), Use case, business, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: Wikidata is a community-maintained knowledge base that epitomizes the FAIR principles of Findability, Accessibility, Interoperability, and Reusability. Here, we describe the breadth and depth of biomedical knowledge contained within Wikidata, assembled from primary knowledge repositories on genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases. We built a collection of open-source tools that simplify the addition and synchronization of Wikidata with source databases. We furthermore demonstrate several use cases of how the continuously updated, crowd-contributed knowledge in Wikidata can be mined. These use cases cover a diverse cross section of biomedical analyses, from crowdsourced curation of biomedical ontologies, to phenotype-based diagnosis of disease, to drug repurposing.
Published: 2019
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39. Parasite microbiome project: Grand challenges

Author: Susan L. Perkins, Paul J. Brindley, Karyna Rosario, Nolwenn M. Dheilly, Raina N. Fichorova, Robert Poulin, Julius Lukeš, Jonathan Z. Kaye, Laura J. Knoll, Lynn M. Schriml, Luke R. Thompson, Joaquín Martínez Martínez, and Kevin D. Kohl
Subjects: Opinion, Life Cycles, QH301-705.5, Parasitic Life Cycles, Immunology, Parasitism, Microbial Genomics, Pathogenesis, Biology, Pathology and Laboratory Medicine, Microbiology, Microbial Ecology, Evolution, Molecular, Microbial ecology, Virology, Medicine and Health Sciences, Parasitic Diseases, Genetics, Parasite hosting, Animals, Humans, Parasites, Microbiome, Biology (General), Parasite Evolution, Molecular Biology, Grand Challenges, Parasitic life cycles, Evolutionary Biology, Ecology, Bacteria, Microbiota, Ecology and Environmental Sciences, Biology and Life Sciences, Genomics, RC581-607, Organismal Evolution, Trophic Interactions, Species Interactions, Community Ecology, Medical Microbiology, Host-Pathogen Interactions, Microbial Evolution, Parasitology, Immunologic diseases. Allergy, Developmental Biology
Published: 2019

40. Toward unrestricted use of public genomic data

Author: Cath Brooksbank, Patrick S. G. Chain, David W. Ussery, Daniele Daffonchio, Martin G. Klotz, John P. A. Ioannidis, William B. Whitman, João C. Setubal, Claire M. Fraser, Owen White, Jihyun F. Kim, Rudolf Amann, Janet K. Jansson, Aristides Patrinos, Peer Bork, James M. Tiedje, Steven J. Hallam, Ramon Rosselló-Móra, Christos A. Ouzounis, Christopher E. Mason, Alejandro Reyes Muñoz, Gene W. Tyson, Adrian Turjanski, Katherine S. Pollard, Konstantinos T. Konstantinidis, Susanna-Assunta Sansone, Alice C. McHardy, Hans-Peter Klenk, Mark Borodovsky, Ioannis Xenarios, Lynn M. Schriml, Jack A. Gilbert, Folker Meyer, George M. Weinstock, Jacques Ravel, Rita R. Colwell, Pieter C. Dorrestein, Shakuntala Baichoo, Rob Knight, Rick Stevens, Richard J. Roberts, Patrick D. Schloss, Rotem Sorek, Robert D. Finn, Benjamin J. Blencowe, Víctor de Lorenzo, Antoine Danchin, Nikos C. Kyrpides, Mircea Podar, Philip Hugenholtz, and BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany.
Subjects: 0301 basic medicine, General Science & Technology, Genomic data, media_common.quotation_subject, Access to Information, 03 medical and health sciences, Databases, 0302 clinical medicine, GEORGE (programming language), Genetic, Databases, Genetic, Genetics, Humans, media_common, Multidisciplinary, Genome, Genome, Human, Information Dissemination, Human Genome, Art, Genomics, Access to information, 030104 developmental biology, GENÔMICA, Humanities, 030217 neurology & neurosurgery, Human
Abstract: Despite some notable progress in data sharing policies and practices, restrictions are still often placed on the open and unconditional use of various genomic data after they have received official approval for release to the public domain or to public databases. These restrictions, which often conflict with the terms and conditions of the funding bodies who supported the release of those data for the benefit of the scientific community and society, are perpetuated by the lack of clear guiding rules for data usage. Existing guidelines for data released to the public domain recognize but fail to resolve tensions between the importance of free and unconditional use of these data and the “right” of the data producers to the first publication. This self-contradiction has resulted in a loophole that allows different interpretations and a continuous debate between data producers and data users on the use of public data. We argue that the publicly available data should be treated as open data, a shared resource with unrestricted use for analysis, interpretation, and publication.
Published: 2019

41. Wikidata as a linked-data hub for Biodiversity data

Author: Andrew I. Su, Lynn M. Schriml, and Andra Waagmeester
Subjects: wikipedia, Computer science, Crowd sourcing, crowd-sourcing, Biodiversity, molecular biology, General Medicine, Linked data, wikidata, Data science
Abstract: Wikidata (http://www.wikidata.org) is the linked database of the Wikimedia Foundation. Like its sister project Wikipedia it is open to humans and machines. Initially primarily intended as a central repository of structured data for the approximately 200 language versions of Wikipedia, Wikidata currently also serves many other use cases. It is an open, Semantic Web-compatible database that anyone can edit. Here, we present the Gene Wiki initiative. In 2008, this project started by creating Wikipedia articles for all human genes (Huss et al. 2008). These articles were enriched with structured information on these genes as tables (called infoboxes). With the onset of Wikidata in 2012, the project diverted its attention from the infoboxes and since we have been enriching Wikidata with structured knowledge from public scientific resources on gene, proteins, diseases and compounds (Burgstaller-Muehlbacher et al. 2016). This structured information is added to Wikidata, while active links to the primary source are maintained. Adding a new resource to Wikidata is a community-driven process that starts with modelling the subjects of the resource under scrutiny. This involves seeking commonalities with similar concepts in Wikidata and, if none are found, new are created. This process mostly happens in a collaboratively-edited document (i.e. GDocs), where different graphical networks are drawn to reflect the data being modelled and its embedding in Wikidata. Once consensus has been reached, the model typically exists in a human-readable document. To allow future validations of these models on existing data, it is converted in a machine-readable Shape Expression (ShEx) (Anonymous 2019, Waagmeester et al. 2017). Shape Expressions schema language can be consumed and produced by humans and machines and is useful in model development, legacy review or as formal documentation. Once a semantic data model (as Shape Expression) is found, i.e. community consensus is reached, a bot is developed to convert the knowledge from the primary source, into the Wikidata model. While Wikidata is linked data (part of the semantic web), many life science resources are not. On the contrary, many distinct file formats or API output formats are used to present life-science knowledge. To convert between these different formats, bots need to be developed that are able to parse the different resources and serialize into wikidata. We have developed a software library in the Python programming language, which we use to build these bots. Once created, these bots run regularly to keep Wikidata up-to-date with knowledge on genes, proteins, diseases and drugs. Having scientific knowledge represented in Wikidata comes with benefits. First, having research data on Wikidata increases its sustainability. When research projects end, their findings now remain on an independently funded infrastructure. Having someone else dealing with an infrastructure for a data commons also relieves the research community of having to do it themselves, leading to more time to focus on doing research As a generic public data commons, Wikidata allows public scrutiny and rapid integration with other domains. Inconsistencies or disagreement between resources become more visible, due to the unified data models and interfaces. The latter we leverage as a feature in our bots. One of our core resources is, for example, the Disease Ontology (Schriml et al. 2018). This ontology on human diseases is continuously updated by its curation team. 2 times per month, updates are then synchronised with Wikidata. If inconsistencies and disagreement with other resources surface, they are logged and shared with the curation team of the Disease Ontology. Hence, we have created a bi-directional update cycle, improving both the Disease Ontology and Wikidata. Although our bots focus on molecular biology, our approaches are generic in onset that we are confident a similar approach can work in biodiversity informatics.
Published: 2019

42. Integrated Genomic Analysis of Diverse Induced Pluripotent Stem Cells from the Progenitor Cell Biology Consortium

Author: Shiva Kumar Shanmukhappa, Jeffrey S. Huo, Adam A. Margolin, Shane L. Hubler, Punam Malik, Robin Schroll, Carolyn Lutzko, Elias T. Zambidis, James A. Wells, Kenneth Daily, Winston Hide, Christopher N. Mayhew, Stacy Bush, Yuliang Wang, Bruce J. Aronow, Nathan Salomonis, Phillip J. Dexheimer, Mehdi Keddache, Antonis K. Hatzopoulos, Shannan J. Ho Sui, Jose A. Cancelas, Larsson Omberg, and Lynn M. Schriml
Subjects: Resource, 0301 basic medicine, Somatic cell, Cellular differentiation, Induced Pluripotent Stem Cells, Gene Expression, Computational biology, Biology, Biochemistry, 03 medical and health sciences, Genetics, Humans, Progenitor cell, Induced pluripotent stem cell, lcsh:QH301-705.5, lcsh:R5-920, Genome, Human, Stem Cells, Cell Differentiation, Genomics, Cell Biology, DNA Methylation, Cellular Reprogramming, 030104 developmental biology, lcsh:Biology (General), DNA methylation, Human genome, Stem cell, lcsh:Medicine (General), Reprogramming, Developmental Biology
Abstract: Summary The rigorous characterization of distinct induced pluripotent stem cells (iPSC) derived from multiple reprogramming technologies, somatic sources, and donors is required to understand potential sources of variability and downstream potential. To achieve this goal, the Progenitor Cell Biology Consortium performed comprehensive experimental and genomic analyses of 58 iPSC from ten laboratories generated using a variety of reprogramming genes, vectors, and cells. Associated global molecular characterization studies identified functionally informative correlations in gene expression, DNA methylation, and/or copy-number variation among key developmental and oncogenic regulators as a result of donor, sex, line stability, reprogramming technology, and cell of origin. Furthermore, X-chromosome inactivation in PSC produced highly correlated differences in teratoma-lineage staining and regulator expression upon differentiation. All experimental results, and raw, processed, and metadata from these analyses, including powerful tools, are interactively accessible from a new online portal at https://www.synapse.org to serve as a reusable resource for the stem cell community., Highlights • Comprehensive portal for diverse iPSC, protocols, metadata, and genomic assays • Recurrent CNV occur during reprogramming, impact oncogenes and tumor suppressors • DNA methylation is influenced by cell of origin in iPSC • PSC X-chromosome inactivation impacts lineage differentiation outcomes, Inherent differences exist among induced pluripotent stem cells (iPSC) from different laboratories, donor of origin, reprogramming methods, and culturing conditions. To carefully evaluate these differences, Lutzko and colleagues from the Progenitor Cell Biology Consortium characterize dozens of iPSC generated with diverse methods to identify methodological and donor-specific differences.
Published: 2016
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43. Human Disease Ontology 2018 update: classification, content and workflow expansion

Author: Siobhan Kibbey, Mike Schor, Becky Tauber, Victor Felix, Nicole Campion, Katharine Bisordi, Linda Jeng, Richard Lichenstein, David B. Kurland, Cynthia F. Bearer, Connor P. Oates, Michelle G. Giglio, Lance Nickle, Lynn M. Schriml, Carol L. Greene, James B. Munro, Christopher Le, Elvira Mitraka, Brooke Hyman, and Poorna Sreekumar
Subjects: 0303 health sciences, Information retrieval, Databases, Factual, business.industry, Suite, Classification of mental disorders, Disease, Biology, Ontology (information science), Semantics, Automation, Workflow, 03 medical and health sciences, 0302 clinical medicine, Biological Ontologies, Genetics, Humans, Database Issue, business, 030217 neurology & neurosurgery, Axiom, 030304 developmental biology
Abstract: The Human Disease Ontology (DO) (http://www.disease-ontology.org), database has undergone significant expansion in the past three years. The DO disease classification includes specific formal semantic rules to express meaningful disease models and has expanded from a single asserted classification to include multiple-inferred mechanistic disease classifications, thus providing novel perspectives on related diseases. Expansion of disease terms, alternative anatomy, cell type and genetic disease classifications and workflow automation highlight the updates for the DO since 2015. The enhanced breadth and depth of the DO’s knowledgebase has expanded the DO’s utility for exploring the multi-etiology of human disease, thus improving the capture and communication of health-related data across biomedical databases, bioinformatics tools, genomic and cancer resources and demonstrated by a 6.6× growth in DO’s user community since 2015. The DO’s continual integration of human disease knowledge, evidenced by the more than 200 SVN/GitHub releases/revisions, since previously reported in our DO 2015 NAR paper, includes the addition of 2650 new disease terms, a 30% increase of textual definitions, and an expanding suite of disease classification hierarchies constructed through defined logical axioms.
Published: 2018

44. Augmenting the disease ontology improves and unifies disease annotations across species

Author: Mary Shimoyama, Stanley J. F. Laulederkind, Janan T. Eppig, Lynn M. Schriml, Cynthia L. Smith, Elvira Mitraka, and Susan M. Bello
Subjects: 0301 basic medicine, ved/biology, business.industry, Computer science, ved/biology.organism_classification_rank.species, Neuroscience (miscellaneous), Medicine (miscellaneous), Usability, Computational biology, Disease, Data type, General Biochemistry, Genetics and Molecular Biology, Rat Genome Database, Terminology, Domain (software engineering), 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Immunology and Microbiology (miscellaneous), Disease Ontology, Model organism, business, 030217 neurology & neurosurgery
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 larger scale comparisons and inferences challenging at best. A single disease ontology that connects data annotated using diverse disease terminologies, and in which the terminology relationships applicable to human and animal models are continuously maintained, is needed. 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 the 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 OMIM and RGD's RDO disease term annotations identified new terms that when added to DO enhance DO's representation of human diseases for which model organism data exist. Expansion of DO term content and cross-references to clinical vocabularies (e.g. OMIM, ORDO, MeSH) has enriched the DO's disease 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 for users and facilitates application of the DO for computational research. A coherent 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 for disease annotation by MGD and RGD demonstrates DO's potential use across organisms and increases interoperability between MGD, RGD and the wider model organism database (MOD) community at the disease annotation level. Further, the human genetics/genomics community will benefit from a consistent way to interrogate model organism disease associations.
Published: 2018
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45. FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration

Author: Robert Hoehndorf, Emma Griffiths, Damion M. Dooley, Gurinder Gosal, Fiona S. L. Brinkman, Matthew Lange, William W. L. Hsiao, Lynn M. Schriml, and Pier Luigi Buttigieg
Subjects: 0301 basic medicine, Vocabulary, Knowledge management, Traceability, Computer science, Animal food, Environmental economics, media_common.quotation_subject, lcsh:TX341-641, Diseases, Ontology (information science), Article, 03 medical and health sciences, Plant sciences, Sustainability, Agriculture, media_common, 2. Zero hunger, Thesaurus (information retrieval), Food security, lcsh:TP368-456, 030102 biochemistry & molecular biology, business.industry, Public Health, Environmental and Occupational Health, lcsh:Food processing and manufacture, 030104 developmental biology, World Wide Web Consortium, business, lcsh:Nutrition. Foods and food supply, Food Science
Abstract: The construction of high capacity data sharing networks to support increasing government and commercial data exchange has highlighted a key roadblock: the content of existing Internet-connected information remains siloed due to a multiplicity of local languages and data dictionaries. This lack of a digital lingua franca is obvious in the domain of human food as materials travel from their wild or farm origin, through processing and distribution chains, to consumers. Well defined, hierarchical vocabulary, connected with logical relationships—in other words, an ontology—is urgently needed to help tackle data harmonization problems that span the domains of food security, safety, quality, production, distribution, and consumer health and convenience. FoodOn (http://foodon.org) is a consortium-driven project to build a comprehensive and easily accessible global farm-to-fork ontology about food, that accurately and consistently describes foods commonly known in cultures from around the world. FoodOn addresses food product terminology gaps and supports food traceability. Focusing on human and domesticated animal food description, FoodOn contains animal and plant food sources, food categories and products, and other facets like preservation processes, contact surfaces, and packaging. Much of FoodOn’s vocabulary comes from transforming LanguaL, a mature and popular food indexing thesaurus, into a World Wide Web Consortium (W3C) OWL Web Ontology Language-formatted vocabulary that provides system interoperability, quality control, and software-driven intelligence. FoodOn compliments other technologies facilitating food traceability, which is becoming critical in this age of increasing globalization of food networks., Food Ontology: FoodOn consortium efforts facilitate food traceability A well-defined food ontology can reduce the barrier of food data harmonization problems world wide. Currently the lack of standard digital vocabulary in food prevents food traceability throughout increasingly global food exchange networks. In this study, Damion Dooley and William Hsiao from the University of British Columbia lead a group of international scientists who give a detailed description of FoodOn, a-consortium-driven project to build a comprehensive food ontology that covers vocabulary from farm to fork. It addresses current gaps in food terminology and focuses on human and domesticated animal food description. It can be foreseen that employing FoodOn vocabulary will speed up traceability of contaminated foods, and ultimately lead to positive economic and human health outcomes.
Published: 2018

46. The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease

Author: Paul N. Schofield, Drashtti Vasant, Tudor Groza, Melissa A. Haendel, Peter N. Robinson, Lynn M. Schriml, Suzanna E. Lewis, Dawid Moldenhauer, Sebastian Köhler, Anthony J. Brookes, Nicole L. Washington, Tomasz Zemojtel, Christopher J. Mungall, Nicole Vasilevsky, Gareth Baynam, Tim Beck, Helen Parkinson, Andreas Zankl, and Warren A. Kibbe
Subjects: Common disease, MEDLINE, Disease, Biology, Models, Biological, Article, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Terminology as Topic, Human Phenotype Ontology, Genetics, Humans, Genetics(clinical), Genetics (clinical), 030304 developmental biology, 0303 health sciences, Gene ontology, fungi, Genetic Diseases, Inborn, 3. Good health, Semantic unification, Gene Ontology, Phenotype, Risk allele, Mendelian inheritance, symbols, 030217 neurology & neurosurgery, Rare disease
Abstract: The Human Phenotype Ontology (HPO) is widely used in the rare disease community for differential diagnostics, phenotype-driven analysis of next-generation sequence-variation data, and translational research, but a comparable resource has not been available for common disease. Here, we have developed a concept-recognition procedure that analyzes the frequencies of HPO disease annotations as identified in over five million PubMed abstracts by employing an iterative procedure to optimize precision and recall of the identified terms. We derived disease models for 3,145 common human diseases comprising a total of 132,006 HPO annotations. The HPO now comprises over 250,000 phenotypic annotations for over 10,000 rare and common diseases and can be used for examining the phenotypic overlap among common diseases that share risk alleles, as well as between Mendelian diseases and common diseases linked by genomic location. The annotations, as well as the HPO itself, are freely available.
Published: 2015
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47. Molecular, phenotypic, and sample-associated data to describe pluripotent stem cell lines and derivatives

Author: Robin Schroll, Nathan Salomonis, Stacy Bush, Mehdi Keddache, Bruce J. Aronow, Carolyn Lutzko, Diana Nordling, Antonis K. Hatzopoulos, Phillip J. Dexheimer, Christopher N. Mayhew, Winston Hide, Lynn M. Schriml, Shannan J. Ho Sui, Elke Grassman, Kenneth Daily, Alexander R. Pico, Kristen K. Dang, Thanneer M. Perumal, Lorena Pantano, Punam Malik, Jose A. Cancelas, Larsson Omberg, and Samad Lotia
Subjects: Pluripotent Stem Cells, 0301 basic medicine, Statistics and Probability, Data Descriptor, Cell Culture Techniques, Computational biology, Biology, Library and Information Sciences, Stem cell marker, Flow cytometry, Education, 03 medical and health sciences, medicine, Animals, Humans, Induced pluripotent stem cell, Genetics, medicine.diagnostic_test, RNA sequencing, Phenotype, Computer Science Applications, Metadata, Induced pluripotent stem cells, 030104 developmental biology, Methylation analysis, Cell culture, Differentiation, DNA methylation, Data integration, Stem cell, Statistics, Probability and Uncertainty, Information Systems
Abstract: The use of induced pluripotent stem cells (iPSC) derived from independent patients and sources holds considerable promise to improve the understanding of development and disease. However, optimized use of iPSC depends on our ability to develop methods to efficiently qualify cell lines and protocols, monitor genetic stability, and evaluate self-renewal and differentiation potential. To accomplish these goals, 57 stem cell lines from 10 laboratories were differentiated to 7 different states, resulting in 248 analyzed samples. Cell lines were differentiated and characterized at a central laboratory using standardized cell culture methodologies, protocols, and metadata descriptors. Stem cell and derived differentiated lines were characterized using RNA-seq, miRNA-seq, copy number arrays, DNA methylation arrays, flow cytometry, and molecular histology. All materials, including raw data, metadata, analysis and processing code, and methodological and provenance documentation are publicly available for re-use and interactive exploration at https://www.synapse.org/pcbc. The goal is to provide data that can improve our ability to robustly and reproducibly use human pluripotent stem cells to understand development and disease.
Published: 2017
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48. Genomic Methods and Microbiological Technologies for Profiling Novel and Extreme Environments for the Extreme Microbiome Project (XMP)

Author: Nathan J. Bivens, Stefan J. Green, Natàlia Garcia-Reyero, Sofia Ahsanuddin, Shawn Levy, Sarah Stewart Johnson, Elena Zaikova, Nadim J. Ajami, Lynn M. Schriml, Joseph Russell Carmical, Noah Alexander, Rita R. Colwell, Jessica J Hoffman, Kelley Thomas, Tara M. Rock, T. C. Hunter, Ian C. Herriott, Ken McGrath, Ebrahim Afshinnekoo, Daniela Bezdan, Diana Krawczyk, Scott Tighe, Matthew L. Settles, Jodie Lee, Nick Greenfield, Alexa B. R. McIntyre, Jon Penterman, Nikos C. Kyrpides, Mohamed S. Donia, Felipe Gómez, Don A. Baldwin, Jonathan Foox, Samantha B. Joye, Joshua Hyman, Christopher E. Mason, and Ellen Jorgensen
Subjects: 0301 basic medicine, Technology, Biology, shotgun sequencing, Medical and Health Sciences, Article, 03 medical and health sciences, Environmental Microbiology, Genetics, Extreme environment, Profiling (information science), Microbiome, Polyzyme, Molecular Biology, Life Below Water, Microbial Biofilms, metagenomics, whole genome, Shotgun sequencing, Ecology, Microbiota, Human Genome, Bacterial, DNA, Reference Standards, Biological Sciences, Molecular Typing, 030104 developmental biology, 13. Climate action, Metagenomics, Project site, RNA, Metagenome, Sequence Analysis, extremophile, Extreme Environments, Biotechnology
Abstract: © 2017, Association of Biomolecular Resource Facilities. All rights reserved. The Extreme Microbiome Project (XMP) is a project launched by the Association of Biomolecular Resource Facilities Metagenomics Research Group (ABRF MGRG) that focuses on whole genome shotgun sequencing of extreme and unique environments using a wide variety of biomolecular techniques. The goals are multifaceted, including development and refinement of new techniques for the following: 1) the detection and characterization of novel microbes, 2) the evaluation of nucleic acid techniques for extremophilic samples, and 3) the identification and implementation of the appropriate bioinformatics pipelines. Here, we highlight the different ongoing projects that we have been working on, as well as details on the various methods we use to characterize the microbiome and metagenome of these complex samples. In particular, we present data of a novel multienzyme extraction protocol that we developed, called Polyzyme or MetaPolyZyme. Presently, the XMP is characterizing sample sites around the world with the intent of discovering new species, genes, and gene clusters. Once a project site is complete, the resulting data will be publically available. Sites include Lake Hillier in Western Australia, the “Door to Hell” crater in Turkmenistan, deep ocean brine lakes of the Gulf of Mexico, deep ocean sediments from Greenland, permafrost tunnels in Alaska, ancient microbial biofilms from Antarctica, Blue Lagoon Iceland, Ethiopian toxic hot springs, and the acidic hypersaline ponds in Western Australia.
Published: 2017
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49. Consent insufficient for data release—Response

Author: João C. Setubal, James M. Tiedje, Steven J. Hallam, Lynn M. Schriml, Rudolf Amann, Antoine Danchin, David W. Ussery, Folker Meyer, Susanna-Assunta Sansone, Pieter C. Dorrestein, Nikos C. Kyrpides, George M. Weinstock, Claire M. Fraser, Mark Borodovsky, Richard J. Roberts, Mircea Podar, Christos A. Ouzounis, Alejandro Reyes Muñoz, Philip Hugenholtz, Robert D. Finn, Aristides Patrinos, John P. A. Ioannidis, Benjamin J. Blencowe, Alice C. McHardy, Hans-Peter Klenk, William B. Whitman, Patrick D. Schloss, Shakuntala Baichoo, Rick Stevens, Daniele Daffonchio, Katherine S. Pollard, Janet K. Jansson, Víctor de Lorenzo, Jacques Ravel, Adrian Turjanski, Konstantinos T. Konstantinidis, Ioannis Xenarios, Christopher E. Mason, Rotem Sorek, Patrick S. G. Chain, Gene W. Tyson, Rita R. Colwell, Peer Bork, Martin G. Klotz, Rob Knight, Jack A. Gilbert, Ramon Rosselló-Móra, Owen White, Jihyun F. Kim, and Cath Brooksbank
Subjects: 0301 basic medicine, Value (ethics), Multidisciplinary, Data collection, business.industry, Internet privacy, Data sharing, Power (social and political), 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Informed consent, 030220 oncology & carcinogenesis, Political science, Openness to experience, Confidentiality, business, Personally identifiable information
Abstract: Nicol et al. make insightful comments on issues related to data sharing and ethics, regulations, and imbalance of power. We largely share their concerns, and we did indeed mention some of them briefly in our Policy Forum. Our discussion pertained to all types of data, many or most of which do not include any personal information or do not involve individuals or humans in any way. For example, environmental genomics data generated through public funding should become available shortly after generation and should enjoy unrestricted usage. As we acknowledged in the Policy Forum, when it comes to personal data, issues of privacy, confidentiality, and informed consent need to be considered carefully and the best arrangements may vary on a case-by-case basis. It makes sense to anticipate these issues before data collection begins and to aim for research designs and informed consent forms that allow maximal, prompt, open use of valuable data. We agree with Nicol et al. that public release of data may affect distribution of burdens and benefits across and within populations, and this is something that should be closely monitored. However, we think that usually more openness would diminish the inadvertent concentration of informational power in the hands of select for-profit enterprises that may wish to hoard data for their own advantage. Conversely, the public release of data may offer more value for the public and more widely distributed benefits of science. The list of author affiliations is available at [www.sciencemag.org/content/363/6425/350/suppl/DC1][1]. [1]: http://www.sciencemag.org/content/363/6425/350/suppl/DC1
Published: 2019
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50. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea

Author: Granger G. Sutton, Pelin Yilmaz, Robert D. Finn, Jessica K. Jarett, Rob Knight, Gene W. Tyson, Frank Oliver Gloeckner, Jack A. Gilbert, Jeremy A. Dodsworth, T. B. K. Reddy, Noah Fierer, Jillian F. Banfield, Nikos C. Kyrpides, Mircea Podar, Philip Hugenholtz, Ilene Karsch-Mizrachi, Adam R. Rivers, Donovan H. Parks, Lynn M. Schriml, Miranda Harmon-Smith, Jonathan A. Eisen, Frederik Schulz, Shibu Yooseph, Folker Meyer, Sean P. Jungbluth, Brian P. Hedlund, Brett J. Baker, Natalia Ivanova, Tanja Woyke, Ramunas Stepanauskas, Steven J. Hallam, George M. Garrity, Eric D. Becraft, Susannah G. Tringe, George M. Weinstock, Bruce W. Birren, Konstantinos T. Konstantinidis, Alla Lapidus, Alicia Clum, A. M. Eren, Thomas Rattei, Scott Tighe, William C. Nelson, Peer Bork, Christian Rinke, Thijs J. G. Ettema, Devin F. R. Doud, Emiley A. Eloe-Fadrosh, Guy Cochrane, Katherine D. McMahon, Alex Copeland, Wen Tso Liu, Rex R. Malmstrom, and Robert M. Bowers
Subjects: 0301 basic medicine, Sequence analysis, 030106 microbiology, Biomedical Engineering, Bioengineering, Genomics, Computational biology, Biology, Applied Microbiology and Biotechnology, Genome, Genome Standards Consortium, Article, Microbiology, 03 medical and health sciences, Genome, Archaeal, Life Science, Biologiska vetenskaper, Whole genome sequencing, Multiple displacement amplification, Genome project, Sequence Analysis, DNA, Biological Sciences, 030104 developmental biology, Metagenomics, Molecular Medicine, Erratum, Genome, Bacterial, Biotechnology, Reference genome
Abstract: We present two standards developed by the Genomic Standards Consortium (GSC) for reporting bacterial and archaeal genome sequences. Both are extensions of the Minimum Information about Any (x) Sequence (MIxS). The standards are the Minimum Information about a Single Amplified Genome (MISAG) and the Minimum Information about a Metagenome-Assembled Genome (MIMAG), including, but not limited to, assembly quality, and estimates of genome completeness and contamination. These standards can be used in combination with other GSC checklists, including the Minimum Information about a Genome Sequence (MIGS), Minimum Information about a Metagenomic Sequence (MIMS), and Minimum Information about a Marker Gene Sequence (MIMARKS). Community-wide adoption of MISAG and MIMAG will facilitate more robust comparative genomic analyses of bacterial and archaeal diversity.
Published: 2016
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