The environment ontology: contextualising biological and biomedical entities

Abstract

As biological and biomedical research increasingly reference the environmental context of the biological entities under study, the need for formalisation and standardisation of environment descriptors is growing. The Environment Ontology (ENVO; http://www.environmentontology.org) is a community-led, open project which seeks to provide an ontology for specifying a wide range of environments relevant to multiple life science disciplines and, through an open participation model, to accommodate the terminological requirements of all those needing to annotate data using ontology classes. This paper summarises ENVO’s motivation, content, structure, adoption, and governance approach. The ontology is available from http://purl.obolibrary.org/obo/envo.owl - an OBO format version is also available by switching the file suffix to “obo”.

Proceedings of the 4th BioNLP Shared Task Workshop

“…The need for information processing is not only the extraction of habitats and microorganisms relationships from text, but also their normalization with respect to a common referential so that they can be integrated and compared. This need has been acknowledged by previous work on habitat classifications for metagenomic samples (Ivanova et al, 2010), microorganisms (Floyd et al, 2005) and other living organisms (Buttigieg et al, 2013) and text-mining tools for mapping textual descriptions to habitat classification (Pignatelli et al, 2009).…”

“…Systems used dictionary-based (TagIt) and machinelearning based (LIMSI, WhuNlpRE, UTS) methods to detect entity mentions in text in the BBcat+ner and BB-event+ner subtasks. All relied on existing terminology and ontology resources, including the NCBI Taxonomy, the List of Prokaryotic Names with Standing in Nomenclature (Parte, 2013), the Brenda Tissue Ontology (Gremse et al, 2011), the Environment Ontology (Buttigieg et al, 2013), the OntoBiotope ontology, and WordNet (Fellbaum, 1998). The TagIt system performed dictionary matching coupled with acronym detection and heuristic rules to adjust entity boundaries.…”

DNA Sequencing as a Tool to Monitor Marine Ecological Status

“…Progress is being made to create standardized protocols (Field et al, 2008;Chain et al, 2009;Gilbert, 2015;Droege et al, 2016). Efforts that promote community collaboration and participation to produce standard practices include the MIxS standard from the Genomic Standards Consortium (GSC) (Yilmaz et al, 2011;ten Hoopen et al, 2015), the Environmental Ontology (Buttigieg et al, 2013, and data standards of the Global Genome Biodiversity Network (GGBN) (Droege et al, 2016). Despite widespread recognition of its importance and the realization that subsequent data curation is costly and time-consuming (ten Hoopen et al, 2016), compliance with metadata collection and curation standards is lacking.…”

TraitBank: Practical semantics for organism attribute data

“…MeasurementValue holds either a number or a categorical value represented by a URI from an ontology, if possible (e.g., PATO or Environments Ontology (ENVO) [7]). Associated measurement metadata may include MeasurementUnit (mapped to the Units of Measurement Ontology, UO 16 ), MeasurementAccuracy and MeasurementMethod (not yet standardized), and StatisticalMethod, (e.g.…”

“…For example, marine environmental modelers need high-quality inputs about large numbers of species in order to understand current and historical distributions of species; how these distributions are impacted by environmental changes such as climate change, overharvesting, or invasive species; how biological communities function to provide ecosystem services; and what could happen to these services under future scenarios that change the composition of these communities. Such large-scale data have also been identified by DI-VERSITAS 6 and the Group on Earth Observations Biodiversity Observation Network (GEO BON) 7 as likely to be required by the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) [35]. Aggregating and standardizing these data, making them freely re-usable, and providing discovery mechanisms for them could facilitate rapid analyses for investigators interested in these urgent problems.…”