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Protein ontology on the semantic web for knowledge discovery. Sci Data 2020; 7:337. [PMID: 33046717 PMCID: PMC7550340 DOI: 10.1038/s41597-020-00679-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 09/17/2020] [Indexed: 11/26/2022] Open
Abstract
The Protein Ontology (PRO) provides an ontological representation of protein-related entities, ranging from protein families to proteoforms to complexes. Protein Ontology Linked Open Data (LOD) exposes, shares, and connects knowledge about protein-related entities on the Semantic Web using Resource Description Framework (RDF), thus enabling integration with other Linked Open Data for biological knowledge discovery. For example, proteins (or variants thereof) can be retrieved on the basis of specific disease associations. As a community resource, we strive to follow the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, disseminate regular updates of our data, support multiple methods for accessing, querying and downloading data in various formats, and provide documentation both for scientists and programmers. PRO Linked Open Data can be browsed via faceted browser interface and queried using SPARQL via YASGUI. RDF data dumps are also available for download. Additionally, we developed RESTful APIs to support programmatic data access. We also provide W3C HCLS specification compliant metadata description for our data. The PRO Linked Open Data is available at https://lod.proconsortium.org/.
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Marín de Evsikova C, Raplee ID, Lockhart J, Jaimes G, Evsikov AV. The Transcriptomic Toolbox: Resources for Interpreting Large Gene Expression Data within a Precision Medicine Context for Metabolic Disease Atherosclerosis. J Pers Med 2019; 9:E21. [PMID: 31032818 PMCID: PMC6617151 DOI: 10.3390/jpm9020021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 04/20/2019] [Accepted: 04/25/2019] [Indexed: 11/16/2022] Open
Abstract
As one of the most widespread metabolic diseases, atherosclerosis affects nearly everyone as they age; arteries gradually narrow from plaque accumulation over time reducing oxygenated blood flow to central and periphery causing heart disease, stroke, kidney problems, and even pulmonary disease. Personalized medicine promises to bring treatments based on individual genome sequencing that precisely target the molecular pathways underlying atherosclerosis and its symptoms, but to date only a few genotypes have been identified. A promising alternative to this genetic approach is the identification of pathways altered in atherosclerosis by transcriptome analysis of atherosclerotic tissues to target specific aspects of disease. Transcriptomics is a potentially useful tool for both diagnostics and discovery science, exposing novel cellular and molecular mechanisms in clinical and translational models, and depending on experimental design to identify and test novel therapeutics. The cost and time required for transcriptome analysis has been greatly reduced by the development of next generation sequencing. The goal of this resource article is to provide background and a guide to appropriate technologies and downstream analyses in transcriptomics experiments generating ever-increasing amounts of gene expression data.
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Affiliation(s)
- Caralina Marín de Evsikova
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
- Epigenetics & Functional Genomics Laboratories, Department of Research and Development, Bay Pines Veteran Administration Healthcare System, Bay Pines, FL 33744, USA.
| | - Isaac D Raplee
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
| | - John Lockhart
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
| | - Gilberto Jaimes
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
| | - Alexei V Evsikov
- Epigenetics & Functional Genomics Laboratories, Department of Research and Development, Bay Pines Veteran Administration Healthcare System, Bay Pines, FL 33744, USA.
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Abstract
The Protein Ontology (PRO) is the reference ontology for proteins in the Open Biomedical Ontologies (OBO) foundry and consists of three sub-ontologies representing protein classes of homologous genes, proteoforms (e.g., splice isoforms, sequence variants, and post-translationally modified forms), and protein complexes. PRO defines classes of proteins and protein complexes, both species-specific and species nonspecific, and indicates their relationships in a hierarchical framework, supporting accurate protein annotation at the appropriate level of granularity, analyses of protein conservation across species, and semantic reasoning. In the first section of this chapter, we describe the PRO framework including categories of PRO terms and the relationship of PRO to other ontologies and protein resources. Next, we provide a tutorial about the PRO website ( proconsortium.org ) where users can browse and search the PRO hierarchy, view reports on individual PRO terms, and visualize relationships among PRO terms in a hierarchical table view, a multiple sequence alignment view, and a Cytoscape network view. Finally, we describe several examples illustrating the unique and rich information available in PRO.
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Rudashevskaya EL, Sickmann A, Markoutsa S. Global profiling of protein complexes: current approaches and their perspective in biomedical research. Expert Rev Proteomics 2016; 13:951-964. [PMID: 27602509 DOI: 10.1080/14789450.2016.1233064] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Despite the rapid evolution of proteomic methods, protein interactions and their participation in protein complexes - an important aspect of their function - has rarely been investigated on the proteome-wide level. Disease states, such as muscular dystrophy or viral infection, are induced by interference in protein-protein interactions within complexes. The purpose of this review is to describe the current methods for global complexome analysis and to critically discuss the challenges and opportunities for the application of these methods in biomedical research. Areas covered: We discuss advancements in experimental techniques and computational tools that facilitate profiling of the complexome. The main focus is on the separation of native protein complexes via size exclusion chromatography and gel electrophoresis, which has recently been combined with quantitative mass spectrometry, for a global protein-complex profiling. The development of this approach has been supported by advanced bioinformatics strategies and fast and sensitive mass spectrometers that have allowed the analysis of whole cell lysates. The application of this technique to biomedical research is assessed, and future directions are anticipated. Expert commentary: The methodology is quite new, and has already shown great potential when combined with complementary methods for detection of protein complexes.
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Affiliation(s)
- Elena L Rudashevskaya
- a Department of Bioanalytics , Leibniz-Institut für Analytische Wissenschaften - ISAS eV , Dortmund , Germany
| | - Albert Sickmann
- a Department of Bioanalytics , Leibniz-Institut für Analytische Wissenschaften - ISAS eV , Dortmund , Germany.,b Medizinisches Proteom-Center , Ruhr-Universität Bochum , Bochum , Germany.,c School of Natural & Computing Sciences, Department of Chemistry , University of Aberdeen , Aberdeen , UK
| | - Stavroula Markoutsa
- a Department of Bioanalytics , Leibniz-Institut für Analytische Wissenschaften - ISAS eV , Dortmund , Germany
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Arighi C, Shamovsky V, Masci AM, Ruttenberg A, Smith B, Natale DA, Wu C, D’Eustachio P. Toll-like receptor signaling in vertebrates: testing the integration of protein, complex, and pathway data in the protein ontology framework. PLoS One 2015; 10:e0122978. [PMID: 25894391 PMCID: PMC4404318 DOI: 10.1371/journal.pone.0122978] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2014] [Accepted: 02/26/2015] [Indexed: 11/20/2022] Open
Abstract
The Protein Ontology (PRO) provides terms for and supports annotation of species-specific protein complexes in an ontology framework that relates them both to their components and to species-independent families of complexes. Comprehensive curation of experimentally known forms and annotations thereof is expected to expose discrepancies, differences, and gaps in our knowledge. We have annotated the early events of innate immune signaling mediated by Toll-Like Receptor 3 and 4 complexes in human, mouse, and chicken. The resulting ontology and annotation data set has allowed us to identify species-specific gaps in experimental data and possible functional differences between species, and to employ inferred structural and functional relationships to suggest plausible resolutions of these discrepancies and gaps.
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Affiliation(s)
- Cecilia Arighi
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
| | - Veronica Shamovsky
- Department of Biochemistry & Molecular Pharmacology, NYU School of Medicine, New York, New York, United States of America
| | - Anna Maria Masci
- Department of Immunology, Duke University, Durham, North Carolina, United States of America
| | - Alan Ruttenberg
- School of Dental Medicine, State University of New York at Buffalo, Buffalo, New York, United States of America
| | - Barry Smith
- Department of Philosophy and Center of Excellence in Bioinformatics and Life Sciences, State University of New York at Buffalo, Buffalo, New York, United States of America
| | - Darren A. Natale
- Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, D. C., United States of America
| | - Cathy Wu
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
- Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, D. C., United States of America
| | - Peter D’Eustachio
- Department of Biochemistry & Molecular Pharmacology, NYU School of Medicine, New York, New York, United States of America
- * E-mail:
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Laukens K, Naulaerts S, Berghe WV. Bioinformatics approaches for the functional interpretation of protein lists: from ontology term enrichment to network analysis. Proteomics 2015; 15:981-96. [PMID: 25430566 DOI: 10.1002/pmic.201400296] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Revised: 10/16/2014] [Accepted: 11/24/2014] [Indexed: 12/24/2022]
Abstract
The main result of a great deal of the published proteomics studies is a list of identified proteins, which then needs to be interpreted in relation to the research question and existing knowledge. In the early days of proteomics this interpretation was only based on expert insights, acquired by digesting a large amount of relevant literature. With the growing size and complexity of the experimental datasets, many computational techniques, databases, and tools have claimed a central role in this task. In this review we discuss commonly and less commonly used methods to functionally interpret experimental proteome lists and compare them with available knowledge. We first address several functional analysis and enrichment techniques based on ontologies and literature. Then we outline how various types of network and pathway information can be used. While the problem of functional interpretation of proteome data is to an extent equivalent to the interpretation of transcriptome or other ''omics'' data, this paper addresses some of the specific challenges and solutions of the proteomics field.
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Affiliation(s)
- Kris Laukens
- Department of Mathematics and Computer Science, University of Antwerp, Middelheimlaan, Antwerp, Belgium; Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp / Antwerp University Hospital, Antwerp, Belgium
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Goh WWB, Wong L. Computational proteomics: designing a comprehensive analytical strategy. Drug Discov Today 2014; 19:266-74. [DOI: 10.1016/j.drudis.2013.07.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2013] [Revised: 06/28/2013] [Accepted: 07/11/2013] [Indexed: 02/02/2023]
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Natale DA, Arighi CN, Blake JA, Bult CJ, Christie KR, Cowart J, D'Eustachio P, Diehl AD, Drabkin HJ, Helfer O, Huang H, Masci AM, Ren J, Roberts NV, Ross K, Ruttenberg A, Shamovsky V, Smith B, Yerramalla MS, Zhang J, AlJanahi A, Çelen I, Gan C, Lv M, Schuster-Lezell E, Wu CH. Protein Ontology: a controlled structured network of protein entities. Nucleic Acids Res 2013; 42:D415-21. [PMID: 24270789 PMCID: PMC3964965 DOI: 10.1093/nar/gkt1173] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
The Protein Ontology (PRO; http://proconsortium.org) formally defines protein entities and explicitly represents their major forms and interrelations. Protein entities represented in PRO corresponding to single amino acid chains are categorized by level of specificity into family, gene, sequence and modification metaclasses, and there is a separate metaclass for protein complexes. All metaclasses also have organism-specific derivatives. PRO complements established sequence databases such as UniProtKB, and interoperates with other biomedical and biological ontologies such as the Gene Ontology (GO). PRO relates to UniProtKB in that PRO’s organism-specific classes of proteins encoded by a specific gene correspond to entities documented in UniProtKB entries. PRO relates to the GO in that PRO’s representations of organism-specific protein complexes are subclasses of the organism-agnostic protein complex terms in the GO Cellular Component Ontology. The past few years have seen growth and changes to the PRO, as well as new points of access to the data and new applications of PRO in immunology and proteomics. Here we describe some of these developments.
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Affiliation(s)
- Darren A Natale
- Protein Information Resource, Georgetown University Medical Center, WA 20007, USA, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA, Department of Bioinformatics and Computational Biology, The Jackson Laboratory, Bar Harbor, ME 04609, USA, Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, NY 10016, USA, Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY 14203, USA, Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, Buffalo, NY 14203, USA, Department of Immunology, Duke University Medical Center, Durham, NC 27705, USA and Department of Oral Diagnostic Sciences, University at Buffalo School of Dental Medicine, Buffalo, NY 14214, USA
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Roncaglia P, Martone ME, Hill DP, Berardini TZ, Foulger RE, Imam FT, Drabkin H, Mungall CJ, Lomax J. The Gene Ontology (GO) Cellular Component Ontology: integration with SAO (Subcellular Anatomy Ontology) and other recent developments. J Biomed Semantics 2013; 4:20. [PMID: 24093723 PMCID: PMC3852282 DOI: 10.1186/2041-1480-4-20] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2013] [Accepted: 09/24/2013] [Indexed: 12/31/2022] Open
Abstract
Background The Gene Ontology (GO) (http://www.geneontology.org/) contains a set of terms for describing the activity and actions of gene products across all kingdoms of life. Each of these activities is executed in a location within a cell or in the vicinity of a cell. In order to capture this context, the GO includes a sub-ontology called the Cellular Component (CC) ontology (GO-CCO). The primary use of this ontology is for GO annotation, but it has also been used for phenotype annotation, and for the annotation of images. Another ontology with similar scope to the GO-CCO is the Subcellular Anatomy Ontology (SAO), part of the Neuroscience Information Framework Standard (NIFSTD) suite of ontologies. The SAO also covers cell components, but in the domain of neuroscience. Description Recently, the GO-CCO was enriched in content and links to the Biological Process and Molecular Function branches of GO as well as to other ontologies. This was achieved in several ways. We carried out an amalgamation of SAO terms with GO-CCO ones; as a result, nearly 100 new neuroscience-related terms were added to the GO. The GO-CCO also contains relationships to GO Biological Process and Molecular Function terms, as well as connecting to external ontologies such as the Cell Ontology (CL). Terms representing protein complexes in the Protein Ontology (PRO) reference GO-CCO terms for their species-generic counterparts. GO-CCO terms can also be used to search a variety of databases. Conclusions In this publication we provide an overview of the GO-CCO, its overall design, and some recent extensions that make use of additional spatial information. One of the most recent developments of the GO-CCO was the merging in of the SAO, resulting in a single unified ontology designed to serve the needs of GO annotators as well as the specific needs of the neuroscience community.
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Affiliation(s)
- Paola Roncaglia
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK.
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Ross KE, Arighi CN, Ren J, Huang H, Wu CH. Construction of protein phosphorylation networks by data mining, text mining and ontology integration: analysis of the spindle checkpoint. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat038. [PMID: 23749465 PMCID: PMC3675891 DOI: 10.1093/database/bat038] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Knowledge representation of the role of phosphorylation is essential for the meaningful understanding of many biological processes. However, such a representation is challenging because proteins can exist in numerous phosphorylated forms with each one having its own characteristic protein–protein interactions (PPIs), functions and subcellular localization. In this article, we evaluate the current state of phosphorylation event curation and then present a bioinformatics framework for the annotation and representation of phosphorylated proteins and construction of phosphorylation networks that addresses some of the gaps in current curation efforts. The integrated approach involves (i) text mining guided by RLIMS-P, a tool that identifies phosphorylation-related information in scientific literature; (ii) data mining from curated PPI databases; (iii) protein form and complex representation using the Protein Ontology (PRO); (iv) functional annotation using the Gene Ontology (GO); and (v) network visualization and analysis with Cytoscape. We use this framework to study the spindle checkpoint, the process that monitors the assembly of the mitotic spindle and blocks cell cycle progression at metaphase until all chromosomes have made bipolar spindle attachments. The phosphorylation networks we construct, centered on the human checkpoint kinase BUB1B (BubR1) and its yeast counterpart MAD3, offer a unique view of the spindle checkpoint that emphasizes biologically relevant phosphorylated forms, phosphorylation-state–specific PPIs and kinase–substrate relationships. Our approach for constructing protein phosphorylation networks can be applied to any biological process that is affected by phosphorylation. Database URL:http://www.yeastgenome.org/
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Affiliation(s)
- Karen E Ross
- Center for Bioinformatics and Computational Biology, 15 Innovation Way, Suite 205, University of Delaware, Newark, DE 19711, USA.
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Ross KE, Arighi CN, Ren J, Natale DA, Huang H, Wu CH. Use of the protein ontology for multi-faceted analysis of biological processes: a case study of the spindle checkpoint. Front Genet 2013; 4:62. [PMID: 23637705 PMCID: PMC3636526 DOI: 10.3389/fgene.2013.00062] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2013] [Accepted: 04/05/2013] [Indexed: 11/13/2022] Open
Abstract
As a member of the Open Biomedical Ontologies (OBO) foundry, the Protein Ontology (PRO) provides an ontological representation of protein forms and complexes and their relationships. Annotations in PRO can be assigned to individual protein forms and complexes, each distinguishable down to the level of post-translational modification, thereby allowing for a more precise depiction of protein function than is possible with annotations to the gene as a whole. Moreover, PRO is fully interoperable with other OBO ontologies and integrates knowledge from other protein-centric resources such as UniProt and Reactome. Here we demonstrate the value of the PRO framework in the investigation of the spindle checkpoint, a highly conserved biological process that relies extensively on protein modification and protein complex formation. The spindle checkpoint maintains genomic integrity by monitoring the attachment of chromosomes to spindle microtubules and delaying cell cycle progression until the spindle is fully assembled. Using PRO in conjunction with other bioinformatics tools, we explored the cross-species conservation of spindle checkpoint proteins, including phosphorylated forms and complexes; studied the impact of phosphorylation on spindle checkpoint function; and examined the interactions of spindle checkpoint proteins with the kinetochore, the site of checkpoint activation. Our approach can be generalized to any biological process of interest.
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Affiliation(s)
- Karen E Ross
- Center for Bioinformatics and Computational Biology, University of Delaware Newark, DE, USA
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Walls RL, Athreya B, Cooper L, Elser J, Gandolfo MA, Jaiswal P, Mungall CJ, Preece J, Rensing S, Smith B, Stevenson DW. Ontologies as integrative tools for plant science. AMERICAN JOURNAL OF BOTANY 2012; 99:1263-75. [PMID: 22847540 PMCID: PMC3492881 DOI: 10.3732/ajb.1200222] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
PREMISE OF THE STUDY Bio-ontologies are essential tools for accessing and analyzing the rapidly growing pool of plant genomic and phenomic data. Ontologies provide structured vocabularies to support consistent aggregation of data and a semantic framework for automated analyses and reasoning. They are a key component of the semantic web. METHODS This paper provides background on what bio-ontologies are, why they are relevant to botany, and the principles of ontology development. It includes an overview of ontologies and related resources that are relevant to plant science, with a detailed description of the Plant Ontology (PO). We discuss the challenges of building an ontology that covers all green plants (Viridiplantae). KEY RESULTS Ontologies can advance plant science in four keys areas: (1) comparative genetics, genomics, phenomics, and development; (2) taxonomy and systematics; (3) semantic applications; and (4) education. CONCLUSIONS Bio-ontologies offer a flexible framework for comparative plant biology, based on common botanical understanding. As genomic and phenomic data become available for more species, we anticipate that the annotation of data with ontology terms will become less centralized, while at the same time, the need for cross-species queries will become more common, causing more researchers in plant science to turn to ontologies.
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Affiliation(s)
- Ramona L. Walls
- New York Botanical Garden, 2900 Southern Blvd., Bronx, New York 10458-5126 USA
| | - Balaji Athreya
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, Oregon 97331-2902 USA
| | - Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, Oregon 97331-2902 USA
| | - Justin Elser
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, Oregon 97331-2902 USA
| | - Maria A. Gandolfo
- L.H. Bailey Hortorium, Department of Plant Biology, Cornell University, 412 Mann Library Building, Ithaca, New York 14853 USA
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, Oregon 97331-2902 USA
| | - Christopher J. Mungall
- Berkeley Bioinformatics Open-Source Projects, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mailstop 64-121, Berkeley, California 94720 USA
| | - Justin Preece
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, Oregon 97331-2902 USA
| | - Stefan Rensing
- Faculty of Biology, University of Freiburg, Schänzlestr. 1, D-79104 Freiburg, Germany
| | - Barry Smith
- Department of Philosophy, University at Buffalo, 126 Park Hall, Buffalo, New York 14260 USA
| | - Dennis W. Stevenson
- New York Botanical Garden, 2900 Southern Blvd., Bronx, New York 10458-5126 USA
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