Proceedings of the Joint Ontology Workshops co-located with the Bolzano Summer of Knowledge (BOSK 2020)

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Dimensions Affecting Representation Styles in Ontologies

There are different ways to formalise roughly the same knowledge, which negatively affects ontology reuse and alignment and other tasks such as formalising competency questions automatically. We aim to shed light on, and make more precise, the intuitive notion of such `representation styles' through characterising their inherent features and the dimensions by which a style may differ. This has led to a total of 28 different traits that are partitioned over 10 dimensions. The operationalisability was assessed through an evaluation of 30 ontologies on those dimensions and applicable values. It showed that it is feasible to use the dimensions and values and resulting in three easily recognisable types of ontologies. Most ontologies had clearly one or the other trait, whereas some were inherently mixed due to inclusion of different and conflicting design decisions

Unintended consequences of existential quantifications in biomedical ontologies

<p>Abstract</p> <p>Background</p> <p>The Open Biomedical Ontologies (OBO) Foundry is a collection of freely available ontologically structured controlled vocabularies in the biomedical domain. Most of them are disseminated via both the OBO Flatfile Format and the semantic web format Web Ontology Language (OWL), which draws upon formal logic. Based on the interpretations underlying OWL description logics (OWL-DL) semantics, we scrutinize the OWL-DL releases of OBO ontologies to assess whether their logical axioms correspond to the meaning intended by their authors.</p> <p>Results</p> <p>We analyzed ontologies and ontology cross products available via the OBO Foundry site <url>http://www.obofoundry.org</url> for existential restrictions (<it>someValuesFrom</it>), from which we examined a random sample of 2,836 clauses.</p> <p>According to a rating done by four experts, 23% of all existential restrictions in OBO Foundry candidate ontologies are suspicious (Cohens' <it>κ </it>= 0.78). We found a smaller proportion of existential restrictions in OBO Foundry cross products are suspicious, but in this case an accurate quantitative judgment is not possible due to a low inter-rater agreement (<it>κ </it>= 0.07). We identified several typical modeling problems, for which satisfactory ontology design patterns based on OWL-DL were proposed. We further describe several usability issues with OBO ontologies, including the lack of ontological commitment for several common terms, and the proliferation of domain-specific relations.</p> <p>Conclusions</p> <p>The current OWL releases of OBO Foundry (and Foundry candidate) ontologies contain numerous assertions which do not properly describe the underlying biological reality, or are ambiguous and difficult to interpret. The solution is a better anchoring in upper ontologies and a restriction to relatively few, well defined relation types with given domain and range constraints.</p

Comparative genomics study reveals Red Sea Bacillus with characteristics associated with potential microbial cell factories (MCFs)

© 2019, The Author(s). Recent advancements in the use of microbial cells for scalable production of industrial enzymes encourage exploring new environments for efficient microbial cell factories (MCFs). Here, through a comparison study, ten newly sequenced Bacillus species, isolated from the Rabigh Harbor Lagoon on the Red Sea shoreline, were evaluated for their potential use as MCFs. Phylogenetic analysis of 40 representative genomes with phylogenetic relevance, including the ten Red Sea species, showed that the Red Sea species come from several colonization events and are not the result of a single colonization followed by speciation. Moreover, clustering reactions in reconstruct metabolic networks of these Bacillus species revealed that three metabolic clades do not fit the phylogenetic tree, a sign of convergent evolution of the metabolism of these species in response to special environmental adaptation. We further showed Red Sea strains Bacillus paralicheniformis (Bac48) and B. halosaccharovorans (Bac94) had twice as much secreted proteins than the model strain B. subtilis 168. Also, Bac94 was enriched with genes associated with the Tat and Sec protein secretion system and Bac48 has a hybrid PKS/NRPS cluster that is part of a horizontally transferred genomic region. These properties collectively hint towards the potential use of Red Sea Bacillus as efficient protein secreting microbial hosts, and that this characteristic of these strains may be a consequence of the unique ecological features of the isolation environment

GOPHER, an HPC framework for large scale graph exploration and inference

Biological ontologies, such as the Human Phenotype Ontology (HPO) and the Gene Ontology (GO), are extensively used in biomedical research to investigate the complex relationship that exists between the phenome and the genome. The interpretation of the encoded information requires methods that efficiently interoperate between multiple ontologies providing molecular details of disease-related features. To this aim, we present GenOtype PHenotype ExplOrer (GOPHER), a framework to infer associations between HPO and GO terms harnessing machine learning and large-scale parallelism and scalability in High-Performance Computing. The method enables to map genotypic features to phenotypic features thus providing a valid tool for bridging functional and pathological annotations. GOPHER can improve the interpretation of molecular processes involved in pathological conditions, displaying a vast range of applications in biomedicine.This work has been developed with the support of the Severo Ochoa Program (SEV-2015-0493); the Spanish Ministry of Science and Innovation (TIN2015- 65316-P); and the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement.Peer ReviewedPostprint (author's final draft

Semantic prioritization of novel causative genomic variants

Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.NS was funded by Wellcome Trust (Grant 100585/Z/12/Z) and the National Institute for Health Research Cambridge Biomedical Research Centre. IB, RBMR, MK, YH, VBB, RH were funded by the King Abdullah University of Science and Technology. GVG acknowledges funding from the National Science Foundation (NSF grant number: IOS-1340112) and the European Commision H2020 (Grant Agreement No. 731075)

OREMPdb: a semantic dictionary of computational pathway models

<p>Abstract</p> <p>Background</p> <p>The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems.</p> <p>Methods</p> <p>Employing an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets.</p> <p>Results</p> <p>OREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared. </p