@conference {IT407, title = {IT407: Annotating germplasm to Planteome reference ontologies}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {
An expected use case of plant phenotype ontologies will be the identification of germplasm containing particular traits of interest. If phenotype data from experiments is annotated using ontologies, it makes sense to include annotations to that germplasm source. A lack of standardized data formatting reduces the utility of these data. Standardizing germplasm data, including links to germplasm databases, or distribution locations improves collaboration, and benefits both researchers and the scientific community as a whole. All plant traits contained in the Planteome reference ontologies are searchable, and interconnected through relationships in the ontology. All data annotated to these reference ontologies will be displayed, shareable, and computable through the Planteome website (www.planteome.org) and APIs. This manuscript will discuss the advantages of standardizing germplasm trait annotation, and the semi-automated process developed to achieve such standardization.
}, url = {http://ceur-ws.org/Vol-1747/IT407_ICBO2016.pdf}, author = {Austin Meier and Laurel Cooper and Justin Elser and Pankaj Jaiswal and Marie-Ang{\'e}lique Laporte} } @conference {IT406, title = {IT406-IP35: The Planteome Project}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {The Planteome project is a centralized online plant informatics portal which provides semantic integration of widely diverse datasets with the goal of plant improvement. Traditional plant breeding methods for crop improvement may be combined with next-generation analysis methods and automated scoring of traits and phenotypes to develop improved varieties. The Planteome project (www.planteome.org) develops and hosts a suite of reference ontologies for plants associated with a growing corpus of genomics data. Data annotations linking phenotypes and germplasm to genomics resources are achieved by data transformation and mapping species-specific controlled vocabularies to the reference ontologies. Analysis and annotation tools are being developed to facilitate studies of plant traits, phenotypes, diseases, gene function and expression and genetic diversity data across a wide range of plant species. The project database and the online resources provide researchers tools to search and browse and access remotely via APIs for semantic integration in annotation tools and data repositories providing resources for plant biology, breeding, genomics and genetics.
}, url = {http://ceur-ws.org/Vol-1747/IT406-IP35_ICBO2016.pdf}, author = {Laurel Cooper and Austin Meier and Justin Elser and Justin Preece and Xu Xu and Ryan Kitchen and Botong Qu and Eugene Zhang and Sinisa Todorovic and Pankaj Jaiswal and Marie-Ang{\'e}lique Laporte and Elizabeth Arnaud and Seth Carbon and Chris Mungall and Barry Smith and Georgios Gkoutos and John Doonan} } @conference {373, title = {IP34: Plant Reactome: A Resource for Comparative Plant Pathway Analysis}, booktitle = {ICBO and BioCreative 2016}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, address = {Corvallis, OR}, abstract = {The Plant Reactome database (http://plantreactome.gramene.org/) hosts metabolic, genetic and signaling pathways for several model and crop plant species. The Reactome data model organizes gene products, small molecules and macromolecular interactions into reactions and pathways in the context of their subcellular location to build a systemslevel framework of a plant cell. The Plant Reactome features Oryza sativa (rice) as a reference species, built by importing the RiceCyc metabolic network and curating new metabolic, signaling and genetic pathways. The Plant Reactome database now contains 241 rice reference pathways and orthology-based pathway projections for 58 plant species. Plant Reactome allows users to i) compare pathways across various plant species; ii) query and visualize curated baseline and differential expression data available in the EMBL-EBI{\textquoteright}s Expression Atlas in the context of pathways in the Plant Reactome; and iii) analyze genome-scale expression data and conduct pathway enrichment analysis to enable researchers to identify pathways affected by the stresses or treatments studied in their data sets. Plant Reactome links out to numerous external reference resources, including the gene pages of Gramene, Phytozome, SoyBase, Legume Information System, PeanutBase, Uniprot, as well as ChEBI for small molecules, PubMed for literature supported evidences, and GO for molecular function and biological processes. Users can access/download our data in various formats from our website and via APIs. The presentation will discuss tools for pathway enrichment analysis and homologue pathway comparison, development of the Plant Reactome portal, curation of reference rice pathways, and phylogeny-based analyses of projected pathway annotations. The project is supported by the Gramene database award (NSF IOS-1127112)and the Human Reactome award (NIH: P41 HG003751, ENFIN LSHG-CT-2005-518254, Ontario Research Fund, and EBI Industry Programme).
}, author = {Sushma Naithani and Justin Preece and Parul Gupta and Peter D{\textquoteright}Eustachio and Justin Elser and Antonio Mundao and Joel Weiser and Sheldon McKay and Lincoln Stein and Doreen Ware and Pankaj Jaiswal} } @conference {IP31, title = {IP31: Planteome Gene Annotation Enrichment Analysis}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {Annotation enrichment analysis of a gene list helps biologists to identify the potential biological functions associated with it. With the extensions of plant ontology categories, the discovery of significant ontology terms associated with a gene list becomes more and more informative. We introduce a tool to help biologists to find out these terms based on the expanding ontology database of the Planteome project. In addition, we propose some new visualization schemes to help users construct a meaningful interpretation of the results guided by the ontology tree.
}, url = {http://ceur-ws.org/Vol-1747/IP31_ICBO2016.pdf}, author = {Botong Qu and Jaden Diefenbaugh and Eugene Zhang and Justin Elser and Pankaj Jaiswal and Seth Carbon and Christopher Mungall} } @conference {IP17, title = {IP17: Comparison of ontology mapping techniques to map plant trait ontologies}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {Crop specific ontologies for phenotype annotations in breeding have proliferated over the last 10 years. Across-crop data interoperability involves linking those ontologies together. For this purpose, the Planteome project is mapping the Crop Ontology traits (www.cropontology.org) to the reference ontology for plant traits, Trait Ontology (TO). Manual mapping is time-consuming and not sustainable in the long-run as ontologies keep on evolving and multiplicating. We are thus working on developing reliable automated mapping techniques to assist curators in performing semantic integration. Our study shows the benefit of the ontology matching technique based on formal definitions and shared ontology design patterns, compared to standard automatic ontology matching algorithm, such as AML (AgreementMakerLight).
}, url = {http://ceur-ws.org/Vol-1747/IP17_ICBO2016.pdf}, author = {Marie-Ang{\'e}lique Laporte and L{\'e}o Valette and Laurel Cooper and Chris Mungall and Austin Meier and Pankaj Jaiswal and Elizabeth Arnaud} } @conference {D101-BP04, title = {D106-BP04: Enhancing Information Accessibility of Publications with Text Mining and Ontology}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {We present an ongoing effort on utilizing text mining methods and existing biological ontologies to help readers to access the information contained in the scientific articles. Our approach includes using multiple strategies for biological entity detection and using association analysis on extracted analysis. The entity extraction processes utilizes regular expression rules, ontologies, and keyword dictionary to get a comprehensive list of biological entities. In addition to extract list of entities, we also apply natural language processing and association analysis techniques to generate inferences among entities and comparing to known relations documented in the existing ontologies.
}, url = {http://ceur-ws.org/Vol-1747/D106-BP04_ICBO2016.pdf}, author = {Weijia Xu and Amit Gupta and Pankaj Jaiswal and Crispin Taylor and Patti Lockhart} } @conference {D201, title = {D101: Plant Image Segmentation and Annotation with Ontologies in BisQue}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {n/a
}, url = {http://ceur-ws.org/Vol-1747/D101_ICBO2016.pdf}, author = {Justin Preece and Justin Elser and Pankaj Jaiswal and Kris Kvilekval and Dmitry Fedorov and B.S. Manjunath and Ryan Kitchen and Xu Xu and Dmitrios Trigkakis and Sinisa Todorovic and Seth Carbon} } @conference {BIT105, title = {BIT105: A Web Application for Extracting Key Domain Information for Scientific Publications using Ontology}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {We present demos of an ongoing project, domain informational vocabulary extraction (DIVE), which aims to enrich digital publications through entity and key informational words detection and by adding additional annotations. The system implements multiple strategies for biological entity detection, including using regular expression rules, ontologies, and a keyword dictionary. These extracted entities are then stored in a database and made accessible through an interactive web application for curation and evaluation by authors. Through the web interface, the user can make additional annotations and corrections to the current results. The updates can then be used to improve the entity detection in subsequent processed articles. Although the system is being developed in the context of annotating journal articles, it can be also be beneficial to domain curators and researchers at large.
}, url = {http://ceur-ws.org/Vol-1747/BIT105_ICBO2016.pdf}, author = {Weijia Xu and Amit Gupta and Pankaj Jaiswal and Crispin Taylor and Patti Lockhart} }