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The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types

View ORCID ProfileThomas H. Gillespie, View ORCID ProfileShreejoy Tripathy, View ORCID ProfileMohameth François Sy, View ORCID ProfileMaryann E. Martone, View ORCID ProfileSean L. Hill
doi: https://doi.org/10.1101/2020.09.01.278879
Thomas H. Gillespie
1Department of Neuroscience, University of California, San Diego
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Shreejoy Tripathy
2Department of Psychiatry, University of Toronto
3Krembil Center for Neuroinformatics, Centre for Addiction and Mental Health: Toronto
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Mohameth François Sy
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Maryann E. Martone
1Department of Neuroscience, University of California, San Diego
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Sean L. Hill
2Department of Psychiatry, University of Toronto
3Krembil Center for Neuroinformatics, Centre for Addiction and Mental Health: Toronto
4Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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  • For correspondence: sean.hill@epfl.ch
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Abstract

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 Statement

MEM is Chief Scientific Officer of SciCrunch, Inc., a tech start up out of UCSD developing tools and services around Research Resource Identifiers (RRIDs).

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 02, 2020.
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The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types
Thomas H. Gillespie, Shreejoy Tripathy, Mohameth François Sy, Maryann E. Martone, Sean L. Hill
bioRxiv 2020.09.01.278879; doi: https://doi.org/10.1101/2020.09.01.278879
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The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types
Thomas H. Gillespie, Shreejoy Tripathy, Mohameth François Sy, Maryann E. Martone, Sean L. Hill
bioRxiv 2020.09.01.278879; doi: https://doi.org/10.1101/2020.09.01.278879

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