A formal ontology of subcellular neuroanatomy

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

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 many evidence-based types that are proposed based on the results of new techniques. Further, comparing different types across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO), that provides a standardized and automatable approach for naming cell types and normalizing their constituent phenotypes using identifiers from community ontologies 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 the NPO knowledge model enables interoperability between the three test cases and neuron names commonly used in the literature.

Toward knowledge support for analysis and interpretation of complex traits

The systematic description of complex traits, from the organism to the cellular level, is important for hypothesis generation about underlying disease mechanisms. We discuss how intelligent algorithms might provide support, leading to faster throughput.

The Gene Ontology (GO) Cellular Component Ontology: integration with SAO (Subcellular Anatomy Ontology) and other recent developments

Background

The Gene Ontology (GO) (http://www.geneontology.org/) contains a set of terms for describing the activity and actions of gene products across all kingdoms of life. Each of these activities is executed in a location within a cell or in the vicinity of a cell. In order to capture this context, the GO includes a sub-ontology called the Cellular Component (CC) ontology (GO-CCO). The primary use of this ontology is for GO annotation, but it has also been used for phenotype annotation, and for the annotation of images. Another ontology with similar scope to the GO-CCO is the Subcellular Anatomy Ontology (SAO), part of the Neuroscience Information Framework Standard (NIFSTD) suite of ontologies. The SAO also covers cell components, but in the domain of neuroscience.

Description

Recently, the GO-CCO was enriched in content and links to the Biological Process and Molecular Function branches of GO as well as to other ontologies. This was achieved in several ways. We carried out an amalgamation of SAO terms with GO-CCO ones; as a result, nearly 100 new neuroscience-related terms were added to the GO. The GO-CCO also contains relationships to GO Biological Process and Molecular Function terms, as well as connecting to external ontologies such as the Cell Ontology (CL). Terms representing protein complexes in the Protein Ontology (PRO) reference GO-CCO terms for their species-generic counterparts. GO-CCO terms can also be used to search a variety of databases.

Conclusions

In this publication we provide an overview of the GO-CCO, its overall design, and some recent extensions that make use of additional spatial information. One of the most recent developments of the GO-CCO was the merging in of the SAO, resulting in a single unified ontology designed to serve the needs of GO annotators as well as the specific needs of the neuroscience community.

Ontological phenotype standards for neurogenetics

Neurological disorders comprise one of the largest groups of human diseases. Due to the myriad symptoms and the extreme degree of clinical variability characteristic of many neurological diseases, the differential diagnosis process is extremely challenging. Even though most neurogenetic diseases are individually rare, collectively, the subgroup of neurogenetic disorders is large, comprising more than 2,400 different disorders. Recently, increasing efforts have been undertaken to unravel the molecular basis of neurogenetic diseases and to correlate pathogenetic mechanisms with clinical signs and symptoms. In order to enable computer-based analyses, the systematic representation of the neurological phenotype is of major importance. We demonstrate how the Human Phenotype Ontology (HPO) can be incorporated into these efforts by providing a systematic semantic representation of phenotypic abnormalities encountered in human genetic diseases. The combination of the HPO together with the Orphanet disease classification represents a promising resource for automated disease classification, performing computational clustering and analysis of the neurogenetic phenome. Furthermore, standardized representations of neurologic phenotypic abnormalities employing the HPO link neurological phenotypic abnormalities to anatomical and functional entities represented in other biomedical ontologies through the semantic references provided by the HPO.

An ontological approach to describing neurons and their relationships

The advancement of neuroscience, perhaps one of the most information rich disciplines of all the life sciences, requires basic frameworks for organizing the vast amounts of data generated by the research community to promote novel insights and integrated understanding. Since Cajal, the neuron remains a fundamental unit of the nervous system, yet even with the explosion of information technology, we still have few comprehensive or systematic strategies for aggregating cell-level knowledge. Progress toward this goal is hampered by the multiplicity of names for cells and by lack of a consensus on the criteria for defining neuron types. However, through umbrella projects like the Neuroscience Information Framework (NIF) and the International Neuroinformatics Coordinating Facility (INCF), we have the opportunity to propose and implement an informatics infrastructure for establishing common tools and approaches to describe neurons through a standard terminology for nerve cells and a database (a Neuron Registry) where these descriptions can be deposited and compared. This article provides an overview of the problem and outlines a solution approach utilizing ontological characterizations. Based on illustrative implementation examples, we also discuss the need for consensus criteria to be adopted by the research community, and considerations on future developments. A scalable repository of neuron types will provide researchers with a resource that materially contributes to the advancement of neuroscience.

An ontology of fungal subcellular traits

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PREMISE OF THE STUDY

The Fungal Subcellular Ontology used in the Assembling the Fungal Tree of Life project is a taxon-wide ontology (controlled vocabulary for attributes) designed to clarify and integrate the broad range of subcellular characters and character states used in higher-level fungal systematics. As in the algae, cellular characters are important phylogenetic markers in kingdom Fungi. The Fungal Subcellular Ontology has been developed primarily to help researchers, especially systematists, in their search for information on subcellular characters across the Fungi, and it complements existing biological ontologies, including the Gene Ontology. •

METHODS

The character and character state data set used in the Assembling the Fungal Tree of Life Structural and Biochemical Database (http://aftol.umn.edu) is the source of terms for generating the ontology. After the terms were accessioned and defined, they were combined in OBO-Edit file format, and the ontology was edited using OBO-Edit, an open source Java tool supported by the Gene Ontology project. •

KEY RESULTS

The Fungal Subcellular Ontology covers both model and nonmodel fungi in great detail and is downloadable in OBO-Edit format at website http://aftol.umn.edu/ontology/fungal_subcellular.obo. •

CONCLUSIONS

The ontology provides a controlled vocabulary of fungal subcellular terms and functions as an operating framework for the Assembling the Fungal Tree of Life Structural and Biochemical Database. An ontology-based design enhances reuse of data deposited in the Structural and Biochemical Database from other independent biological and genetic databases. Data integration approaches that advance access to data from the diversity of biological databases are imperative as interdisciplinary research gains importance. In this sense, the Fungal Subcellular Ontology becomes highly relevant to mycologists as well as nonmycologists because fungi interact actively as symbionts and parasites or passively with many other life forms.

Invertebrate neurophylogeny: suggested terms and definitions for a neuroanatomical glossary

BACKGROUND

Invertebrate nervous systems are highly disparate between different taxa. This is reflected in the terminology used to describe them, which is very rich and often confusing. Even very general terms such as 'brain', 'nerve', and 'eye' have been used in various ways in the different animal groups, but no consensus on the exact meaning exists. This impedes our understanding of the architecture of the invertebrate nervous system in general and of evolutionary transformations of nervous system characters between different taxa.

RESULTS

We provide a glossary of invertebrate neuroanatomical terms with a precise and consistent terminology, taxon-independent and free of homology assumptions. This terminology is intended to form a basis for new morphological descriptions. A total of 47 terms are defined. Each entry consists of a definition, discouraged terms, and a background/comment section.

CONCLUSIONS

The use of our revised neuroanatomical terminology in any new descriptions of the anatomy of invertebrate nervous systems will improve the comparability of this organ system and its substructures between the various taxa, and finally even lead to better and more robust homology hypotheses.

Spatio-structural granularity of biological material entities

BACKGROUND

With the continuously increasing demands on knowledge- and data-management that databases have to meet, ontologies and the theories of granularity they use become more and more important. Unfortunately, currently used theories and schemes of granularity unnecessarily limit the performance of ontologies due to two shortcomings: (i) they do not allow the integration of multiple granularity perspectives into one granularity framework; (ii) they are not applicable to cumulative-constitutively organized material entities, which cover most of the biomedical material entities.

RESULTS

The above mentioned shortcomings are responsible for the major inconsistencies in currently used spatio-structural granularity schemes. By using the Basic Formal Ontology (BFO) as a top-level ontology and Keet's general theory of granularity, a granularity framework is presented that is applicable to cumulative-constitutively organized material entities. It provides a scheme for granulating complex material entities into their constitutive and regional parts by integrating various compositional and spatial granularity perspectives. Within a scale dependent resolution perspective, it even allows distinguishing different types of representations of the same material entity. Within other scale dependent perspectives, which are based on specific types of measurements (e.g. weight, volume, etc.), the possibility of organizing instances of material entities independent of their parthood relations and only according to increasing measures is provided as well. All granularity perspectives are connected to one another through overcrossing granularity levels, together forming an integrated whole that uses the compositional object perspective as an integrating backbone. This granularity framework allows to consistently assign structural granularity values to all different types of material entities.

CONCLUSIONS

The here presented framework provides a spatio-structural granularity framework for all domain reference ontologies that model cumulative-constitutively organized material entities. With its multi-perspectives approach it allows querying an ontology stored in a database at one's own desired different levels of detail: The contents of a database can be organized according to diverse granularity perspectives, which in their turn provide different views on its content (i.e. data, knowledge), each organized into different levels of detail.

Ontologies for Neuroscience: What are they and What are they Good for?

Current information technology practices in neuroscience make it difficult to understand the organization of the brain across spatial scales. Subcellular junctional connectivity, cytoarchitectural local connectivity, and long-range topographical connectivity are just a few of the relevant data domains that must be synthesized in order to make sense of the brain. However, due to the heterogeneity of the data produced within these domains, the landscape of multiscale neuroscience data is fragmented. A standard framework for neuroscience data is needed to bridge existing digital data resources and to help in the conceptual unification of the multiple disciplines of neuroscience. Using our efforts in building ontologies for neuroscience as an example, we examine the benefits and limits of ontologies as a solution for this data integration problem. We provide several examples of their application to problems of image annotation, content-based retrieval of structural data, and integration of data across scales and researchers.

The NIFSTD and BIRNLex vocabularies: building comprehensive ontologies for neuroscience

A critical component of the Neuroscience Information Framework (NIF) project is a consistent, flexible terminology for describing and retrieving neuroscience-relevant resources. Although the original NIF specification called for a loosely structured controlled vocabulary for describing neuroscience resources, as the NIF system evolved, the requirement for a formally structured ontology for neuroscience with sufficient granularity to describe and access a diverse collection of information became obvious. This requirement led to the NIF standardized (NIFSTD) ontology, a comprehensive collection of common neuroscience domain terminologies woven into an ontologically consistent, unified representation of the biomedical domains typically used to describe neuroscience data (e.g., anatomy, cell types, techniques), as well as digital resources (tools, databases) being created throughout the neuroscience community. NIFSTD builds upon a structure established by the BIRNLex, a lexicon of concepts covering clinical neuroimaging research developed by the Biomedical Informatics Research Network (BIRN) project. Each distinct domain module is represented using the Web Ontology Language (OWL). As much as has been practical, NIFSTD reuses existing community ontologies that cover the required biomedical domains, building the more specific concepts required to annotate NIF resources. By following this principle, an extensive vocabulary was assembled in a relatively short period of time for NIF information annotation, organization, and retrieval, in a form that promotes easy extension and modification. We report here on the structure of the NIFSTD, and its predecessor BIRNLex, the principles followed in its construction and provide examples of its use within NIF.

NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF

Neuronal morphology affects network connectivity, plasticity, and information processing. Uncovering the design principles and functional consequences of dendritic and axonal shape necessitates quantitative analysis and computational modeling of detailed experimental data. Digital reconstructions provide the required neuromorphological descriptions in a parsimonious, comprehensive, and reliable numerical format. NeuroMorpho.Org is the largest web-accessible repository service for digitally reconstructed neurons and one of the integrated resources in the Neuroscience Information Framework (NIF). Here we describe the NeuroMorpho.Org approach as an exemplary experience in designing, creating, populating, and curating a neuroscience digital resource. The simple three-tier architecture of NeuroMorpho.Org (web client, web server, and relational database) encompasses all necessary elements to support a large-scale, integrate-able repository. The data content, while heterogeneous in scientific scope and experimental origin, is unified in format and presentation by an in house standardization protocol. The server application (MRALD) is secure, customizable, and developer-friendly. Centralized processing and expert annotation yields a comprehensive set of metadata that enriches and complements the raw data. The thoroughly tested interface design allows for optimal and effective data search and retrieval. Availability of data in both original and standardized formats ensures compatibility with existing resources and fosters further tool development. Other key functions enable extensive exploration and discovery, including 3D and interactive visualization of branching, frequently measured morphometrics, and reciprocal links to the original PubMed publications. The integration of NeuroMorpho.Org with version-1 of the NIF (NIFv1) provides the opportunity to access morphological data in the context of other relevant resources and diverse subdomains of neuroscience, opening exciting new possibilities in data mining and knowledge discovery. The outcome of such coordination is the rapid and powerful advancement of neuroscience research at both the conceptual and technological level.

Bioimage informatics: a new area of engineering biology

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ‘bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.

Contact:pengh@janelia.hhmi.org

Supplementary information:Supplementary data are available at Bioinformatics online.

BAMS Neuroanatomical Ontology: Design and Implementation

We describe in this paper the structure and main features of a domain specific ontology for neuroscience, the BAMS Neuroanatomical Ontology. The ontology includes a complete set of concepts that describe the parts of the rat nervous system, a growing set of concepts that describe neuron populations identified in different brain regions, and relationships between concepts. The ontology is linked with a complex representation of structural and physiological variables used to classify neurons, which is encoded in BAMS. BAMS Neuroanatomical Ontology is accessible on the web and includes an interface that allows browsing terms, viewing criteria for classification, and accessing associated information.

The cell centered database project: an update on building community resources for managing and sharing 3D imaging data

Databases have become integral parts of data management, dissemination, and mining in biology. At the Second Annual Conference on Electron Tomography, held in Amsterdam in 2001, we proposed that electron tomography data should be shared in a manner analogous to structural data at the protein and sequence scales. At that time, we outlined our progress in creating a database to bring together cell level imaging data across scales, The Cell Centered Database (CCDB). The CCDB was formally launched in 2002 as an on-line repository of high-resolution 3D light and electron microscopic reconstructions of cells and subcellular structures. It contains 2D, 3D, and 4D structural and protein distribution information from confocal, multiphoton, and electron microscopy, including correlated light and electron microscopy. Many of the data sets are derived from electron tomography of cells and tissues. In the 5 years since its debut, we have moved the CCDB from a prototype to a stable resource and expanded the scope of the project to include data management and knowledge engineering. Here, we provide an update on the CCDB and how it is used by the scientific community. We also describe our work in developing additional knowledge tools, e.g., ontologies, for annotation and query of electron microscopic data.