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Zhang X, Feng Y, Li F, Ding J, Tahseen D, Hinojosa E, Chen Y, Tao C. Evaluating MedDRA-to-ICD terminology mappings. BMC Med Inform Decis Mak 2024; 23:299. [PMID: 38326827 PMCID: PMC10851449 DOI: 10.1186/s12911-023-02375-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 11/14/2023] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND In this era of big data, data harmonization is an important step to ensure reproducible, scalable, and collaborative research. Thus, terminology mapping is a necessary step to harmonize heterogeneous data. Take the Medical Dictionary for Regulatory Activities (MedDRA) and International Classification of Diseases (ICD) for example, the mapping between them is essential for drug safety and pharmacovigilance research. Our main objective is to provide a quantitative and qualitative analysis of the mapping status between MedDRA and ICD. We focus on evaluating the current mapping status between MedDRA and ICD through the Unified Medical Language System (UMLS) and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We summarized the current mapping statistics and evaluated the quality of the current MedDRA-ICD mapping; for unmapped terms, we used our self-developed algorithm to rank the best possible mapping candidates for additional mapping coverage. RESULTS The identified MedDRA-ICD mapped pairs cover 27.23% of the overall MedDRA preferred terms (PT). The systematic quality analysis demonstrated that, among the mapped pairs provided by UMLS, only 51.44% are considered an exact match. For the 2400 sampled unmapped terms, 56 of the 2400 MedDRA Preferred Terms (PT) could have exact match terms from ICD. CONCLUSION Some of the mapped pairs between MedDRA and ICD are not exact matches due to differences in granularity and focus. For 72% of the unmapped PT terms, the identified exact match pairs illustrate the possibility of identifying additional mapped pairs. Referring to its own mapping standard, some of the unmapped terms should qualify for the expansion of MedDRA to ICD mapping in UMLS.
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Affiliation(s)
- Xinyuan Zhang
- McWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yixue Feng
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Fang Li
- McWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jin Ding
- McWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Danyal Tahseen
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ezekiel Hinojosa
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yong Chen
- The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Cui Tao
- McWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
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2
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Jenkins VK, Larkin A, Thurmond J. Using FlyBase: A Database of Drosophila Genes and Genetics. Methods Mol Biol 2022; 2540:1-34. [PMID: 35980571 DOI: 10.1007/978-1-0716-2541-5_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Since 1992, FlyBase has provided a freely available online database of information about the model organism Drosophila melanogaster. Data in FlyBase is curated manually from research papers as well as computationally from a variety of relevant sources, to serve as an information hub that enables and accelerates research discovery. This chapter aims to give users new to the database an overview of the layout and types of data available, as well as introducing some tools with which to access the data. More experienced users will find useful information about recent improvements and descriptions to enable more efficient navigation of the database.
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Affiliation(s)
| | - Aoife Larkin
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Jim Thurmond
- Department of Biology, Indiana University, Bloomington, IN, USA
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Azer K, Kaddi CD, Barrett JS, Bai JPF, McQuade ST, Merrill NJ, Piccoli B, Neves-Zaph S, Marchetti L, Lombardo R, Parolo S, Immanuel SRC, Baliga NS. History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications. Front Physiol 2021; 12:637999. [PMID: 33841175 PMCID: PMC8027332 DOI: 10.3389/fphys.2021.637999] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/25/2021] [Indexed: 12/24/2022] Open
Abstract
Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.
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Affiliation(s)
- Karim Azer
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | - Chanchala D. Kaddi
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | | | - Jane P. F. Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States
| | - Sean T. McQuade
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Nathaniel J. Merrill
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Benedetto Piccoli
- Department of Mathematical Sciences and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Susana Neves-Zaph
- Translational Disease Modeling, Data and Data Science, Sanofi, Bridgewater, NJ, United States
| | - Luca Marchetti
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Rosario Lombardo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Silvia Parolo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
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4
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Zanin M, Aitya NA, Basilio J, Baumbach J, Benis A, Behera CK, Bucholc M, Castiglione F, Chouvarda I, Comte B, Dao TT, Ding X, Pujos-Guillot E, Filipovic N, Finn DP, Glass DH, Harel N, Iesmantas T, Ivanoska I, Joshi A, Boudjeltia KZ, Kaoui B, Kaur D, Maguire LP, McClean PL, McCombe N, de Miranda JL, Moisescu MA, Pappalardo F, Polster A, Prasad G, Rozman D, Sacala I, Sanchez-Bornot JM, Schmid JA, Sharp T, Solé-Casals J, Spiwok V, Spyrou GM, Stalidzans E, Stres B, Sustersic T, Symeonidis I, Tieri P, Todd S, Van Steen K, Veneva M, Wang DH, Wang H, Wang H, Watterson S, Wong-Lin K, Yang S, Zou X, Schmidt HH. An Early Stage Researcher's Primer on Systems Medicine Terminology. NETWORK AND SYSTEMS MEDICINE 2021; 4:2-50. [PMID: 33659919 PMCID: PMC7919422 DOI: 10.1089/nsm.2020.0003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2020] [Indexed: 12/19/2022] Open
Abstract
Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.
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Affiliation(s)
- Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Nadim A.A. Aitya
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - José Basilio
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Jan Baumbach
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Arriel Benis
- Faculty of Technology Management, Holon Institute of Technology (HIT), Holon, Israel
| | - Chandan K. Behera
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics, and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Blandine Comte
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Tien-Tuan Dao
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Xuemei Ding
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nenad Filipovic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - David H. Glass
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Nissim Harel
- Faculty of Sciences, Holon Institute of Technology (HIT), Holon, Israel
| | - Tomas Iesmantas
- Department of Mathematics and Natural Sciences, Kaunas University of Technology, Kaunas, Lithuania
| | - Ilinka Ivanoska
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia
| | - Alok Joshi
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Karim Zouaoui Boudjeltia
- Laboratory of Experimental Medicine (ULB 222), Medicine Faculty, Université libre de Bruxelles, CHU de Charleroi, Charleroi, Belgium
| | - Badr Kaoui
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Daman Kaur
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Niamh McCombe
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - João Luís de Miranda
- Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre, Portalegre, Portugal
- Centro de Recursos Naturais e Ambiente (CERENA), Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | | | | | - Annikka Polster
- Centre for Molecular Medicine Norway (NCMM), Forskningparken, Oslo, Norway
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Ioan Sacala
- Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Jose M. Sanchez-Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Johannes A. Schmid
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Trevor Sharp
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic–Central University of Catalonia, Vic, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Vojtěch Spiwok
- Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague, Czech Republic
| | - George M. Spyrou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Egils Stalidzans
- Computational Systems Biology Group, Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Blaž Stres
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Tijana Sustersic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - Ioannis Symeonidis
- Center for Research and Technology Hellas, Hellenic Institute of Transport, Thessaloniki, Greece
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Altnagelvin, United Kingdom
| | - Kristel Van Steen
- BIO3-Systems Genetics, GIGA-R, University of Liege, Liege, Belgium
- BIO3-Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Da-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and School of Systems Science, Beijing Normal University, Beijing, China
| | - Haiying Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Hui Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Su Yang
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Xin Zou
- Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Harald H.H.W. Schmidt
- Faculty of Health, Medicine & Life Science, Maastricht University, Maastricht, The Netherlands
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5
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Hinderer EW, Moseley HNB. GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts. PLoS One 2020; 15:e0233311. [PMID: 32525872 PMCID: PMC7289357 DOI: 10.1371/journal.pone.0233311] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 05/01/2020] [Indexed: 12/11/2022] Open
Abstract
Gene Ontology is used extensively in scientific knowledgebases and repositories to organize a wealth of biological information. However, interpreting annotations derived from differential gene lists is often difficult without manually sorting into higher-order categories. To address these issues, we present GOcats, a novel tool that organizes the Gene Ontology (GO) into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. We tested GOcats performance using subcellular location categories to mine annotations from GO-utilizing knowledgebases and evaluated their accuracy against immunohistochemistry datasets in the Human Protein Atlas (HPA). In comparison to term categorizations generated from UniProt's controlled vocabulary and from GO slims via OWLTools' Map2Slim, GOcats outperformed these methods in its ability to mimic human-categorized GO term sets. Unlike the other methods, GOcats relies only on an input of basic keywords from the user (e.g. biologist), not a manually compiled or static set of top-level GO terms. Additionally, by identifying and properly defining relations with respect to semantic scope, GOcats can utilize the traditionally problematic relation, has_part, without encountering erroneous term mapping. We applied GOcats in the comparison of HPA-sourced knowledgebase annotations to experimentally-derived annotations provided by HPA directly. During the comparison, GOcats improved correspondence between the annotation sources by adjusting semantic granularity. GOcats enables the creation of custom, GO slim-like filters to map fine-grained gene annotations from gene annotation files to general subcellular compartments without needing to hand-select a set of GO terms for categorization. Moreover, GOcats can customize the level of semantic specificity for annotation categories. Furthermore, GOcats enables a safe and more comprehensive semantic scoping utilization of go-core, allowing for a more complete utilization of information available in GO. Together, these improvements can impact a variety of GO knowledgebase data mining use-cases as well as knowledgebase curation and quality control.
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Affiliation(s)
- Eugene W Hinderer
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, Kentucky, United States of America
| | - Hunter N B Moseley
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, Kentucky, United States of America.,Markey Cancer Center, University of Kentucky, Lexington, Kentucky, United States of America.,Resource Center for Stable Isotope-Resolved Metabolomics, University of Kentucky, Lexington, Kentucky, United States of America.,Institute for Biomedical Informatics, University of Kentucky, Lexington, Kentucky, United States of America.,Center for Clinical and Translational Science, University of Kentucky, Lexington, Kentucky, United States of America
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6
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Cardoso SD, Da Silveira M, Pruski C. Construction and exploitation of an historical knowledge graph to deal with the evolution of ontologies. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105508] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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7
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8
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Ontology mapping for semantically enabled applications. Drug Discov Today 2019; 24:2068-2075. [PMID: 31158512 DOI: 10.1016/j.drudis.2019.05.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 04/12/2019] [Accepted: 05/28/2019] [Indexed: 12/14/2022]
Abstract
In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential of semantically enabled or enriched applications and for meaningful insights, such as drug discovery, using machine-learning technologies. We discuss challenges and solutions for better ontology mappings, as well as how to select ontologies before their application. In addition, we describe tools and algorithms for ontology mapping, including evaluation of tool capability and quality of mappings. Finally, we outline the requirements for an ontology mapping service (OMS) and the progress being made towards implementation of such sustainable services.
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9
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Koutsandreas T, Ladoukakis E, Pilalis E, Zarafeta D, Kolisis FN, Skretas G, Chatziioannou AA. ANASTASIA: An Automated Metagenomic Analysis Pipeline for Novel Enzyme Discovery Exploiting Next Generation Sequencing Data. Front Genet 2019; 10:469. [PMID: 31178894 PMCID: PMC6543708 DOI: 10.3389/fgene.2019.00469] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 05/01/2019] [Indexed: 01/27/2023] Open
Abstract
Metagenomic analysis of environmental samples provides deep insight into the enzymatic mixture of the corresponding niches, capable of revealing peptide sequences with novel functional properties exploiting the high performance of next-generation sequencing (NGS) technologies. At the same time due to their ever increasing complexity, there is a compelling need for ever larger computational configurations to ensure proper bioinformatic analysis, and fine annotation. With the aiming to address the challenges of such an endeavor, we have developed a novel web-based application named ANASTASIA (automated nucleotide aminoacid sequences translational plAtform for systemic interpretation and analysis). ANASTASIA provides a rich environment of bioinformatic tools, either publicly available or novel, proprietary algorithms, integrated within numerous automated algorithmic workflows, and which enables versatile data processing tasks for (meta)genomic sequence datasets. ANASTASIA was initially developed in the framework of the European FP7 project HotZyme, whose aim was to perform exhaustive analysis of metagenomes derived from thermal springs around the globe and to discover new enzymes of industrial interest. ANASTASIA has evolved to become a stable and extensible environment for diversified, metagenomic, functional analyses for a range of applications overarching industrial biotechnology to biomedicine, within the frames of the ELIXIR-GR project. As a showcase, we report the successful in silico mining of a novel thermostable esterase termed “EstDZ4” from a metagenomic sample collected from a hot spring located in Krisuvik, Iceland.
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Affiliation(s)
- Theodoros Koutsandreas
- Institute of Chemical Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.,e-NIOS Applications PC, Athens, Greece
| | - Efthymios Ladoukakis
- Institute of Chemical Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.,Laboratory of Biotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - Eleftherios Pilalis
- Institute of Chemical Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.,e-NIOS Applications PC, Athens, Greece
| | - Dimitra Zarafeta
- Institute of Chemical Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece
| | - Fragiskos N Kolisis
- Institute of Chemical Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.,Laboratory of Biotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - Georgios Skretas
- Institute of Chemical Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece
| | - Aristotelis A Chatziioannou
- Institute of Chemical Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.,e-NIOS Applications PC, Athens, Greece
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Trifan A, Oliveira JL. Patient data discovery platforms as enablers of biomedical and translational research: A systematic review. J Biomed Inform 2019; 93:103154. [PMID: 30922867 DOI: 10.1016/j.jbi.2019.103154] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 03/15/2019] [Accepted: 03/18/2019] [Indexed: 11/28/2022]
Abstract
BACKGROUND The global shift from paper health records to electronic ones has led to an impressive growth of biomedical digital data along the past two decades. Exploring and extracting knowledge from these data has the potential to enhance translational research and lead to positive outcomes for the population's health and healthcare. OBECTIVE The aim of this study was to conduct a systematic review to identify software platforms that enable discovery, secondary use and interoperability of biomedical data. Additionally, we aim evaluating the identified solutions in terms of clinical interest and main healthcare-related outcomes. METHODS A systematic search of the scientific literature published and indexed in Pubmed between January 2014 and September 2018 was performed. Inclusion criteria were as follows: relevance for the topic of biomedical data discovery, English language, and free full text. To increase the recall, we developed a semi-automatic and incremental methodology to retrieve articles that cite one or more of the previous set. RESULTS A total number of 500 candidate papers were retrieved through this methodology. Of these, 85 were eligible for abstract assessment. Finally, 37 studies qualified for a full-text review, and 20 provided enough information for the study objectives. CONCLUSIONS This study revealed that biomedical discovery platforms are both a current necessity and a significantly innovative agent in the area of healthcare. The outcomes that were identified, in terms of scientific publications, clinical studies and research collaborations stand as evidence.
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Cardoso SD, Pruski C, Da Silveira M. Supporting biomedical ontology evolution by identifying outdated concepts and the required type of change. J Biomed Inform 2018; 87:1-11. [DOI: 10.1016/j.jbi.2018.08.013] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 08/16/2018] [Accepted: 08/28/2018] [Indexed: 11/16/2022]
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12
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Sad-Houari N, Taghezout N, Nador A. A knowledge-based model for managing the ontology evolution: case study of maintenance in SONATRACH. J Inf Sci 2018. [DOI: 10.1177/0165551518802261] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The challenges of the development of a suitable ontology scheme in decision-making environment should be taken in conjunction with the exploitation of more recent technologies. It is expected that the use of ontologies will lead to the construction of more intelligent applications, allowing them to work more specifically at a human conceptual level. We propose in this article an approach that analyses the impact of changes in the ontology on business rules in order to detect inconsistencies that may be generated. In addition, the developed tool provides solutions to repair inconsistencies with the help of domain experts. In our work, business rules are edited from the concepts and properties that are stored in an OWL (Web Ontology Language) ontology named OntoloG. This latter is implemented throughout the use of Protégé 4.0.2.with the OWL sub-language. OntoloG has been developed by the knowledge acquisition from documents, collection and capitalisation of business rules process with experts in SONATRACH AVAL.
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Affiliation(s)
- Nawal Sad-Houari
- Laboratoire d’Informatique Oran (LIO), Département du Vivant et de l’Environnement, Faculté des Sciences de la Nature et de la Vie, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB, BP 1505, El M’naouer, 31000 Oran Algérie
| | - Noria Taghezout
- Laboratoire d’Informatique Oran (LIO), Université ORAN1 Ahmed Ben Bella, BP 1524 EL Mnaouer Oran, Algeria
| | - Aissa Nador
- Department of IT and Information Systems, SONATRACH Downstream Activity, Algeria
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Marijn Stok F, Renner B, Allan J, Boeing H, Ensenauer R, Issanchou S, Kiesswetter E, Lien N, Mazzocchi M, Monsivais P, Stelmach-Mardas M, Volkert D, Hoffmann S. Dietary Behavior: An Interdisciplinary Conceptual Analysis and Taxonomy. Front Psychol 2018; 9:1689. [PMID: 30298030 PMCID: PMC6160746 DOI: 10.3389/fpsyg.2018.01689] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 08/22/2018] [Indexed: 01/08/2023] Open
Abstract
Background: Dietary behavior encompasses many aspects, terms for which are used inconsistently across different disciplines and research traditions. This hampers communication and comparison across disciplines and impedes the development of a cumulative science. We describe the conceptual analysis of the fuzzy umbrella concept "dietary behavior" and present the development of an interdisciplinary taxonomy of dietary behavior. Methods: A four-phase multi-method approach was employed. Input was provided by 76 scholars involved in an international research project focusing on the determinants of dietary behavior. Input was collected from the scholars via an online mind mapping procedure. After structuring, condensing, and categorizing this input into a compact taxonomy, the result was presented to all scholars, discussed extensively, and adapted. A second revision round was then conducted among a core working group. Results: A total of 145 distinct entries were made in the original mind mapping procedure. The subsequent steps allowed us to reduce and condense the taxonomy into a final product consisting of 34 terms organized into three main categories: Food Choice, Eating Behavior, and Dietary Intake/Nutrition. In a live discussion session attended by 50 of the scholars involved in the development of the taxonomy, it was judged to adequately reflect their input and to be a valid and useful starting point for interdisciplinary understanding and collaboration. Conclusion: The current taxonomy can be used as a tool to facilitate understanding and cooperation between different disciplines investigating dietary behavior, which may contribute to a more successful approach to tackling the complex public health challenges faced by the field. The taxonomy need not be viewed as a final product, but can continue to grow in depth and width as additional experts provide their input.
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Affiliation(s)
- F. Marijn Stok
- Department of Psychology, University of Konstanz, Konstanz, Germany
| | - Britta Renner
- Department of Psychology, University of Konstanz, Konstanz, Germany
| | - Julia Allan
- Health Psychology, The Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Regina Ensenauer
- Experimental Pediatrics and Metabolism, University Children’s Hospital, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sylvie Issanchou
- Centre des Sciences du Goût et de l’Alimentation, AgroSup Dijon, CNRS, INRA, Université Bourgogne Franche-Comté, Dijon, France
| | - Eva Kiesswetter
- Institute for Biomedicine of Aging, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nanna Lien
- Department of Nutrition, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Mario Mazzocchi
- Department of Statistical Sciences, University of Bologna, Bologna, Italy
| | - Pablo Monsivais
- Centre for Diet and Activity Research, MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Marta Stelmach-Mardas
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Department of Biophysics, Poznan University of Medical Sciences, Poznań, Poland
| | - Dorothee Volkert
- Institute for Biomedicine of Aging, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Stefan Hoffmann
- Department of Marketing, Institute of Business Administration, Kiel University, Kiel, Germany
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14
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Kolyvakis P, Kalousis A, Smith B, Kiritsis D. Biomedical ontology alignment: an approach based on representation learning. J Biomed Semantics 2018; 9:21. [PMID: 30111369 PMCID: PMC6094585 DOI: 10.1186/s13326-018-0187-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 07/16/2018] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. RESULTS An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results. CONCLUSIONS Our proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.
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Affiliation(s)
- Prodromos Kolyvakis
- École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, Lausanne, 1015 Switzerland
| | - Alexandros Kalousis
- Business Informatics Department, University of Applied Sciences, HES-SO, Western Switzerland Carouge, Switzerland
| | - Barry Smith
- Department of Philosophy and Department of Biomedical Informatics, 104 Park Hall, University at Buffalo, Buffalo, 14260 NY USA
| | - Dimitris Kiritsis
- École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, Lausanne, 1015 Switzerland
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15
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Vitali F, Lombardo R, Rivero D, Mattivi F, Franceschi P, Bordoni A, Trimigno A, Capozzi F, Felici G, Taglino F, Miglietta F, De Cock N, Lachat C, De Baets B, De Tré G, Pinart M, Nimptsch K, Pischon T, Bouwman J, Cavalieri D. ONS: an ontology for a standardized description of interventions and observational studies in nutrition. GENES AND NUTRITION 2018; 13:12. [PMID: 29736190 PMCID: PMC5928560 DOI: 10.1186/s12263-018-0601-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 04/03/2018] [Indexed: 12/12/2022]
Abstract
Background The multidisciplinary nature of nutrition research is one of its main strengths. At the same time, however, it presents a major obstacle to integrate data analysis, especially for the terminological and semantic interpretations that specific research fields or communities are used to. To date, a proper ontology to structure and formalize the concepts used for the description of nutritional studies is still lacking. Results We have developed the Ontology for Nutritional Studies (ONS) by harmonizing selected pre-existing de facto ontologies with novel health and nutritional terminology classifications. The ONS is the result of a scholarly consensus of 51 research centers in nine European countries. The ontology classes and relations are commonly encountered while conducting, storing, harmonizing, integrating, describing, and searching nutritional studies. The ONS facilitates the description and specification of complex nutritional studies as demonstrated with two application scenarios. Conclusions The ONS is the first systematic effort to provide a solid and extensible formal ontology framework for nutritional studies. Integration of new information can be easily achieved by the addition of extra modules (i.e., nutrigenomics, metabolomics, nutrikinetics, and quality appraisal). The ONS provides a unified and standardized terminology for nutritional studies as a resource for nutrition researchers who might not necessarily be familiar with ontologies and standardization concepts. Electronic supplementary material The online version of this article (10.1186/s12263-018-0601-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Francesco Vitali
- 1Institute of Biometeorology (IBIMET), National Research Council (CNR), Via Giovanni Caproni, 8, 50145 Florence, FI Italy.,4Department of Biology, University of Florence, Via Madonna del Piano, 6, 50019 Sesto F, FI Italy
| | - Rosario Lombardo
- 2The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, I-38068 Rovereto, TN Italy
| | - Damariz Rivero
- 4Department of Biology, University of Florence, Via Madonna del Piano, 6, 50019 Sesto F, FI Italy
| | - Fulvio Mattivi
- 5Food Quality and Nutrition Department, Research and Innovation Centre, Edmund Mach Foundation, Via Edmund Mach, 1, 38010 San Michele all'Adige, TN Italy.,12Center Agriculture Food Environment, University of Trento, San Michele all'Adige, Italy
| | - Pietro Franceschi
- 5Food Quality and Nutrition Department, Research and Innovation Centre, Edmund Mach Foundation, Via Edmund Mach, 1, 38010 San Michele all'Adige, TN Italy
| | - Alessandra Bordoni
- 6Department of Agri-Food Sciences and Technologies, University of Bologna, Piazza Goidanich 60, Cesena, FC Italy
| | - Alessia Trimigno
- 6Department of Agri-Food Sciences and Technologies, University of Bologna, Piazza Goidanich 60, Cesena, FC Italy
| | - Francesco Capozzi
- 6Department of Agri-Food Sciences and Technologies, University of Bologna, Piazza Goidanich 60, Cesena, FC Italy
| | - Giovanni Felici
- 7Institute for Systems Analysis and Computer Science (IASI), National Research Council (CNR), Via dei Taurini, 19, 00185 Rome, RM Italy
| | - Francesco Taglino
- 7Institute for Systems Analysis and Computer Science (IASI), National Research Council (CNR), Via dei Taurini, 19, 00185 Rome, RM Italy
| | - Franco Miglietta
- 1Institute of Biometeorology (IBIMET), National Research Council (CNR), Via Giovanni Caproni, 8, 50145 Florence, FI Italy
| | - Nathalie De Cock
- 3Department of Food Technology, Safety and Health, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Carl Lachat
- 3Department of Food Technology, Safety and Health, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Bernard De Baets
- 8KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Guy De Tré
- 9Department of Telecommunications and Information Processing, Ghent University, Coupure links 653, 9000 Ghent, Belgium
| | - Mariona Pinart
- 10Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Katharina Nimptsch
- 10Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Tobias Pischon
- 10Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Jildau Bouwman
- 11Microbiology and Systems Biology, TNO, Utrechtseweg 48, 3704HE Zeist, The Netherlands
| | - Duccio Cavalieri
- 1Institute of Biometeorology (IBIMET), National Research Council (CNR), Via Giovanni Caproni, 8, 50145 Florence, FI Italy.,4Department of Biology, University of Florence, Via Madonna del Piano, 6, 50019 Sesto F, FI Italy
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16
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Garcelon N, Neuraz A, Salomon R, Faour H, Benoit V, Delapalme A, Munnich A, Burgun A, Rance B. A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse. J Biomed Inform 2018; 80:52-63. [DOI: 10.1016/j.jbi.2018.02.019] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 02/22/2018] [Accepted: 02/28/2018] [Indexed: 01/26/2023]
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17
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Harper L, Campbell J, Cannon EKS, Jung S, Poelchau M, Walls R, Andorf C, Arnaud E, Berardini TZ, Birkett C, Cannon S, Carson J, Condon B, Cooper L, Dunn N, Elsik CG, Farmer A, Ficklin SP, Grant D, Grau E, Herndon N, Hu ZL, Humann J, Jaiswal P, Jonquet C, Laporte MA, Larmande P, Lazo G, McCarthy F, Menda N, Mungall CJ, Munoz-Torres MC, Naithani S, Nelson R, Nesdill D, Park C, Reecy J, Reiser L, Sanderson LA, Sen TZ, Staton M, Subramaniam S, Tello-Ruiz MK, Unda V, Unni D, Wang L, Ware D, Wegrzyn J, Williams J, Woodhouse M, Yu J, Main D. AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture. Database (Oxford) 2018; 2018:5096675. [PMID: 30239679 PMCID: PMC6146126 DOI: 10.1093/database/bay088] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 07/19/2018] [Accepted: 07/30/2018] [Indexed: 01/07/2023]
Abstract
The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.
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Affiliation(s)
- Lisa Harper
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
| | | | - Ethalinda K S Cannon
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
- Computer Science, Iowa State University, Ames, IA, USA
| | - Sook Jung
- Horticulture, Washington State University, Pullman, WA, USA
| | - Monica Poelchau
- National Agricultural Library, USDA Agricultural Research Service, Beltsville, MD, USA
| | | | - Carson Andorf
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
- Computer Science, Iowa State University, Ames, IA, USA
| | - Elizabeth Arnaud
- Bioversity International, Informatics Unit, Conservation and Availability Programme, Parc Scientifique Agropolis II, Montpellier, France
| | - Tanya Z Berardini
- The Arabidopsis Information Resource, Phoenix Bioinformatics, Fremont, CA, USA
| | | | - Steve Cannon
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
| | - James Carson
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX, USA
| | - Bradford Condon
- Entomology and Plant Pathology, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Nathan Dunn
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Christine G Elsik
- Division of Animal Sciences and Division of Plant Sciences, University of Missouri, Columbia, MO, USA
| | - Andrew Farmer
- National Center for Genome Resources, Santa Fe, NM, USA
| | | | - David Grant
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
| | - Emily Grau
- National Center for Genome Resources, Santa Fe, NM, USA
| | - Nic Herndon
- Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
| | - Zhi-Liang Hu
- Animal Science, Iowa State University, Ames, USA
| | - Jodi Humann
- Horticulture, Washington State University, Pullman, WA, USA
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Clement Jonquet
- Laboratory of Informatics, Robotics, Microelectronics of Montpellier, University of Montpellier & CNRS, Montpellier, France
| | - Marie-Angélique Laporte
- Bioversity International, Informatics Unit, Conservation and Availability Programme, Parc Scientifique Agropolis II, Montpellier, France
| | | | - Gerard Lazo
- Crop Improvement and Genetics Research Unit, USDA-ARS, Albany, CA, USA
| | - Fiona McCarthy
- School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, AZ, USA
| | | | | | | | - Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Rex Nelson
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
| | - Daureen Nesdill
- Marriott Library, University of Utah, Salt Lake City, UT, USA
| | - Carissa Park
- Animal Science, Iowa State University, Ames, USA
| | - James Reecy
- Animal Science, Iowa State University, Ames, USA
| | - Leonore Reiser
- The Arabidopsis Information Resource, Phoenix Bioinformatics, Fremont, CA, USA
| | | | - Taner Z Sen
- Crop Improvement and Genetics Research Unit, USDA-ARS, Albany, CA, USA
| | - Margaret Staton
- Entomology and Plant Pathology, University of Tennessee Knoxville, Knoxville, TN, USA
| | | | | | - Victor Unda
- Horticulture, Washington State University, Pullman, WA, USA
| | - Deepak Unni
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Liya Wang
- Plant Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Doreen Ware
- USDA, Plant, Soil and Nutrition Research, Ithaca, NY, USA
- Plant Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jill Wegrzyn
- Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
| | - Jason Williams
- Cold Spring Harbor Laboratory, DNA Learning Center, Cold Spring Harbor, NY, USA
| | - Margaret Woodhouse
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
| | - Jing Yu
- Horticulture, Washington State University, Pullman, WA, USA
| | - Doreen Main
- Horticulture, Washington State University, Pullman, WA, USA
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