@conference {ICBO_2018_15, title = {ICBO_2018_15: Quality Assurance of Ontology Content Reuse}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

Building ontologies is difficult and time-consuming. As such, content reuse has been promoted as an important guiding principle in ontology development. Reusing content from other ontologies can reduce the overall effort involved in new ontology construction and provide better alignment with existing knowledge modeling. However, reuse is not a panacea, and it comes with its own attendant difficulties. In this paper, we investigate some common quality assurance issues associated with reuse, such as duplicated content and versioning problems. Some heuristic-based approaches are proposed for analyzing ontologies for these kinds of quality assurance issues. An analysis is carried out on a sample of the large collection of BioPortal-hosted ontologies, many of which employ reuse. The findings indicate that curators and authors, particularly those new to the reuse process, should be on the alert when developing an ontology with reused content to avoid introducing problems into their own ontologies.

}, keywords = {BioPortal, modeling, Ontology, ontology quality assurance, ontology reuse}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_15.pdf }, author = {Michael Halper and Christopher Ochs and Yehoshua Perl and Sivaram Arabandi and Mark Musen} } @conference {ICBO_2018_27, title = {ICBO_2018_27: Semantic Interoperability: Challenges and Opportunities in Cell Type Knowledge Representation}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

The Human Cell Atlas (HCA) and the California Institute for Regenerative Medicine{\textquoteright}s Center of Excellence in Stem Cell Genomics (CESCG) are identifying novel cell types at a rapid pace using single cell RNA sequencing (scRNAseq). To maximize the scientific return on these discoveries, it will be critical that the data derived from these studies is translated into biological knowledge that is findable, accessible, interoperable and reproducible (FAIR). To achieve this objective, we are developing a provisional cell type ontology (pCL) where the data being gathered from experimental work is represented in a standard semantic format that can be exchanged, retrieved, and inferred over using standard approaches and tools.

}, keywords = {cell type, CL Ontology, GABAergic, glutamatergic, HCA, Interoperability, pCL, scRNAseq}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Richard Scheuermann and Brian Aevermann and Mohamed Keshk} } @conference {ICBO_2018_28, title = {ICBO_2018_28: KTAO: A kidney tissue atlas ontology to support community-based kidney knowledge base development and data integration}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

Human kidney has its complex structure and diverse interactions among its cells during homeostasis and in its diseased states. To systematically classify, represent, and integrate kidney gene activity, cell types, cell states, and interstitial components, we developed a Kidney Tissue Atlas Ontology (KTAO). KTAO reuses and aligns with existing ontologies such as the Cell Ontology, UBERON, and Human Phenotype Ontology. KTAO also generates new semantic axioms to logically link terms of entities in different domains. As a first study, KTAO represents over 200 known kidney gene markers and their profiles in different cell types in kidney patients. Such a representation supports kidney knowledge base generation, query, and data integration.

}, keywords = {AKI, atlas, CKD, disease, gene marker, Kidney, KTAO, Ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_28.pdf }, author = {Yongqun He and Becky Steck and Edison Ong and Laura Mariani and Chrysta Lienczewski and Ulysses Balis and Matthias Kretzler and Jonathan Himmelfarb and John F. Bertram and Evren Azeloglu and Ravi Iyengar and Deborah Hoshizaki and Sean D. Mooney} } @conference {ICBO_2018_63, title = {ICBO_2018_63: GO-MAP Implements CAFA Tools: Improved Automated Gene Function Annotation for Plants}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {Maize is both a crop species and a model for genetics and genomics research. As such, maize GO annotations produced by the community data projects Gramene and Phytozome are widely used to derive hypotheses for both crop improvement and basic science. Our maize-GAMER project assessed existing maize GO annotations and to implement and test the performance of some of the most commonly used GO prediction tools (i.e., Reciprocal Best Hits and domain presence) alongside three of the top performing tools submitted for evaluation in the CAFA1 (Critical Assessment of protein Function Annotation) competition. All datasets were compared based on F-score using an independent gold-standard dataset (2002 GO annotations for 1,619 genes) provided by MaizeGDB. In addition to producing and comparing these individual GO annotation sets, we also combined the datasets we generated to produce a maize-GAMER aggregate annotation set. Compared to Gramene and Phytozome, the maize-GAMER aggregate set annotates more genes in the maize genome and assigns more GO terms per gene. In addition, the maize-GAMER dataset{\textquoteright}s functional assignments are comparable to Gramene and Phytozome overall (based on F-score). These findings have been published, and the maize-GAMER GO annotations are available via CyVerse and MaizeGDB. Here we review the methods and describe GO-MAP, the pipeline used to generate these datasets. GO-MAP has been containerized to facilitate gene function annotation for other plant proteomes and will be released via CyVerse in the very near future.}, keywords = {assessment, CAFA, function, gene ontology}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Kokulapalan Wimalanathan and Carson Andorf and Iddo Friedberg and Carolyn Lawrence-Dill} } @conference {ICBO_2018_72, title = {ICBO_2018_72: Ontology-based Comparative Transcriptomics: Novel Drought Stress-Induced Genes and Pathways in Rice}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {Rice is an important crop that feeds almost half the world population. As climate change models predict floods, drought and extreme temperatures in rice production areas, the need to better understand the genetic basis of adaptation and tolerance mechanisms to abiotic stresses is vital. To better understand tolerance mechanisms and responses under drought, we designed a time-series transcriptomic experiment with two different genotypes of Oryza sativa subspecies indica. These indica genotypes are grown in their center of diversity and are phenotyped as tolerant or susceptible to common abiotic stressors: submergence, saline, and/or drought. Using systems approach, our goal was to identify the stress tolerant candidate genes and genetic polymorphism to help accelerate the genetic gains in plant breeding efforts. We generated RNA-Seq transcriptome data for treated and untreated samples of the two indica genotypes, with three biological replicates, per time point in a drought-stress induced experiment. The sequence data generated was analyzed by calling polymorphisms, transcript isoforms, expression levels, assembling transcriptomes and identifying stress-induced pathways specific to each genetic background and tolerance level. The statistically significant results of these analyses were then annotated using various ontologies and by aligning against the quantitative trait loci and phenotypes annotated with trait ontology, SNP consequences annotated with sequence ontology, and gene ontologies, to identify function, process role and cellular localization of genes of interest. Collating these ontologies proved useful in identifying stress-induced genes overlapping the QTLs overlapping with drought phenotype and characterizing thousands of interesting genetic changes that may help us understand mechanism of drought response in rice.}, keywords = {Abiotic Stress, Bioinformatics, Biological Ontologies, Comparative Transcriptomics, Oryza sativa, Stress-Biology}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Noor Al-Bader and Pankaj Jaiswal} }