RT Journal Article SR Electronic T1 The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.09.01.278879 DO 10.1101/2020.09.01.278879 A1 Thomas H. Gillespie A1 Shreejoy Tripathy A1 Mohameth François Sy A1 Maryann E. Martone A1 Sean L. Hill YR 2020 UL http://biorxiv.org/content/early/2020/09/02/2020.09.01.278879.abstract AB The challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to the many proposed evidence-based types that are based on the results of new techniques. Further, comparing different models across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO) that provides a standardized and machine computable approach for naming and normalizing phenotypes in cell types by using community ontology identifiers as a common language. The NPO provides a framework for systematically organizing knowledge about cellular properties and enables interoperability with existing neuron naming schemes. We evaluate the NPO by populating a knowledge base with three independent cortical neuron classifications derived from published data sets that describe neurons according to molecular, morphological, electrophysiological and synaptic properties. Competency queries to this knowledge base demonstrate that this knowledge model enables interoperability between the three test cases and common usage neuron names from the literature.Competing Interest StatementMEM is Chief Scientific Officer of SciCrunch, Inc., a tech start up out of UCSD developing tools and services around Research Resource Identifiers (RRIDs).