Meeting report: advancing practical applications of biodiversity ontologies

Providing Adverse Outcome Pathways from the AOP-Wiki in a Semantic Web Format to Increase Usability and Accessibility of the Content

Introduction:

The AOP-Wiki is the main platform for the development and storage of adverse outcome pathways (AOPs). These AOPs describe mechanistic information about toxicodynamic processes and can be used to develop effective risk assessment strategies. However, it is challenging to automatically and systematically parse, filter, and use its contents. We explored solutions to better structure the AOP-Wiki content, and to link it with chemical and biological resources. Together, this allows more detailed exploration, which can be automated.

Materials and Methods:

We converted the complete AOP-Wiki content into resource description framework (RDF) triples. We used >20 ontologies for the semantic annotation of property–object relations, including the Chemical Information Ontology, Dublin Core, and the AOP Ontology.

Results:

The resulting RDF contains >122,000 triples describing 158 unique properties of >15,000 unique subjects. Furthermore, >3500 link-outs were added to 12 chemical databases, and >7500 link-outs to 4 gene and protein databases. The AOP-Wiki RDF has been made available at https://aopwiki.rdf.bigcat-bioinformatics.org

Discussion:

SPARQL queries can be used to answer biological and toxicological questions, such as listing measurement methods for all Key Events leading to an Adverse Outcome of interest. The full power that the use of this new resource provides becomes apparent when combining the content with external databases using federated queries.

Conclusion:

Overall, the AOP-Wiki RDF allows new ways to explore the rapidly growing AOP knowledge and makes the integration of this database in automated workflows possible, making the AOP-Wiki more FAIR.

Organizing phenotypic data-a semantic data model for anatomy

BACKGROUND

Currently, almost all morphological data are published as unstructured free text descriptions. This not only brings about terminological problems regarding semantic transparency, which hampers their re-use by non-experts, but the data cannot be parsed by computers either, which in turn hampers their integration across many fields in the life sciences, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. With an ever-increasing amount of available ontologies and the development of adequate semantic technology, however, a solution to this problem becomes available. Instead of free text descriptions, morphological data can be recorded, stored, and communicated through the Web in the form of highly formalized and structured directed graphs (semantic graphs) that use ontology terms and URIs as terminology.

RESULTS

After introducing an instance-based approach of recording morphological descriptions as semantic graphs (i.e., Semantic Instance Anatomy Knowledge Graphs) and discussing accompanying metadata graphs, I propose a general scheme of how to efficiently organize the resulting graphs in a tuple store framework based on instances of defined named graph ontology classes. The use of such named graph resources allows meaningful fragmentation of the data, which in turn enables subsequent specification of all kinds of data views for managing and accessing morphological data.

CONCLUSIONS

Morphological data that comply with the here proposed semantic data model will not only be computer-parsable but also re-usable by non-experts and could be better integrated with other sources of data in the life sciences. This would allow morphology as a discipline to further participate in eScience and Big Data.

The Plant Phenology Ontology: A New Informatics Resource for Large-Scale Integration of Plant Phenology Data

Plant phenology - the timing of plant life-cycle events, such as flowering or leafing out - plays a fundamental role in the functioning of terrestrial ecosystems, including human agricultural systems. Because plant phenology is often linked with climatic variables, there is widespread interest in developing a deeper understanding of global plant phenology patterns and trends. Although phenology data from around the world are currently available, truly global analyses of plant phenology have so far been difficult because the organizations producing large-scale phenology data are using non-standardized terminologies and metrics during data collection and data processing. To address this problem, we have developed the Plant Phenology Ontology (PPO). The PPO provides the standardized vocabulary and semantic framework that is needed for large-scale integration of heterogeneous plant phenology data. Here, we describe the PPO, and we also report preliminary results of using the PPO and a new data processing pipeline to build a large dataset of phenology information from North America and Europe.

The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperation

Background

The Environment Ontology (ENVO; http://www.environmentontology.org/), first described in 2013, is a resource and research target for the semantically controlled description of environmental entities. The ontology's initial aim was the representation of the biomes, environmental features, and environmental materials pertinent to genomic and microbiome-related investigations. However, the need for environmental semantics is common to a multitude of fields, and ENVO's use has steadily grown since its initial description. We have thus expanded, enhanced, and generalised the ontology to support its increasingly diverse applications.

Methods

We have updated our development suite to promote expressivity, consistency, and speed: we now develop ENVO in the Web Ontology Language (OWL) and employ templating methods to accelerate class creation. We have also taken steps to better align ENVO with the Open Biological and Biomedical Ontologies (OBO) Foundry principles and interoperate with existing OBO ontologies. Further, we applied text-mining approaches to extract habitat information from the Encyclopedia of Life and automatically create experimental habitat classes within ENVO.

Results

Relative to its state in 2013, ENVO's content, scope, and implementation have been enhanced and much of its existing content revised for improved semantic representation. ENVO now offers representations of habitats, environmental processes, anthropogenic environments, and entities relevant to environmental health initiatives and the global Sustainable Development Agenda for 2030. Several branches of ENVO have been used to incubate and seed new ontologies in previously unrepresented domains such as food and agronomy. The current release version of the ontology, in OWL format, is available at http://purl.obolibrary.org/obo/envo.owl.

Conclusions

ENVO has been shaped into an ontology which bridges multiple domains including biomedicine, natural and anthropogenic ecology, ‘omics, and socioeconomic development. Through continued interactions with our users and partners, particularly those performing data archiving and sythesis, we anticipate that ENVO’s growth will accelerate in 2017. As always, we invite further contributions and collaboration to advance the semantic representation of the environment, ranging from geographic features and environmental materials, across habitats and ecosystems, to everyday objects in household settings.

Location and foraging as basis for classification of biotic interactions

Ecologists face an overwhelming diversity of ecological relationships in natural communities. In this paper, I propose to differentiate various types of the interspecific relations on the basis of two factors: relative localization and foraging activity of interacting partners. I advocate recognition of four types of environments: internal, surface, proximate external and distant external. Then I distinguish four types of synoikia-one partner lives in different degree of proximity to another; and four types of synmensalism: one partner forages in different degree of proximity to another. Intersection of localization-based (four subtypes of synoikia) and foraging-based (four subtypes of synmensalism) rows results in 16 types of interactions. This scheme can serve as a framework that manages diverse biotic interactions in a standardized way. I have made the first step to set up nomenclature standards for terms describing interspecific interactions and hope that this will facilitate research and communication.

Emerging semantics to link phenotype and environment

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.

Meeting report: Identifying practical applications of ontologies for biodiversity informatics

This report describes the outcomes of a recent workshop, building on a series of workshops from the last three years with the goal if integrating genomics and biodiversity research, with a more specific goal here to express terms in Darwin Core and Audubon Core, where class constructs have been historically underspecified, into a Biological Collections Ontology (BCO) framework. For the purposes of this workshop, the BCO provided the context for fully defining classes as well as object and data properties, including domain and range information, for both the Darwin Core and Audubon Core. In addition, the workshop participants reviewed technical specifications and approaches for annotating instance data with BCO terms. Finally, we laid out proposed activities for the next 3 to 18 months to continue this work.