Anatomical ontologies: names and places in biology

An ontology-based search engine for digital reconstructions of neuronal morphology

Neuronal morphology is extremely diverse across and within animal species, developmental stages, brain regions, and cell types. This diversity is functionally important because neuronal structure strongly affects synaptic integration, spiking dynamics, and network connectivity. Digital reconstructions of axonal and dendritic arbors are thus essential to quantify and model information processing in the nervous system. NeuroMorpho.Org is an established repository containing tens of thousands of digitally reconstructed neurons shared by several hundred laboratories worldwide. Each neuron is annotated with specific metadata based on the published references and additional details provided by data owners. The number of represented metadata concepts has grown over the years in parallel with the increase of available data. Until now, however, the lack of standardized terminologies and of an adequately structured metadata schema limited the effectiveness of user searches. Here we present a new organization of NeuroMorpho.Org metadata grounded on a set of interconnected hierarchies focusing on the main dimensions of animal species, anatomical regions, and cell types. We have comprehensively mapped each metadata term in NeuroMorpho.Org to this formal ontology, explicitly resolving all ambiguities caused by synonymy and homonymy. Leveraging this consistent framework, we introduce OntoSearch, a powerful functionality that seamlessly enables retrieval of morphological data based on expert knowledge and logical inferences through an intuitive string-based user interface with auto-complete capability. In addition to returning the data directly matching the search criteria, OntoSearch also identifies a pool of possible hits by taking into consideration incomplete metadata annotation.

An estimation of the number of cells in the human body

BACKGROUND

All living organisms are made of individual and identifiable cells, whose number, together with their size and type, ultimately defines the structure and functions of an organism. While the total cell number of lower organisms is often known, it has not yet been defined in higher organisms. In particular, the reported total cell number of a human being ranges between 10(12) and 10(16) and it is widely mentioned without a proper reference.

AIM

To study and discuss the theoretical issue of the total number of cells that compose the standard human adult organism.

SUBJECTS AND METHODS

A systematic calculation of the total cell number of the whole human body and of the single organs was carried out using bibliographical and/or mathematical approaches.

RESULTS

A current estimation of human total cell number calculated for a variety of organs and cell types is presented. These partial data correspond to a total number of 3.72 × 10(13).

CONCLUSIONS

Knowing the total cell number of the human body as well as of individual organs is important from a cultural, biological, medical and comparative modelling point of view. The presented cell count could be a starting point for a common effort to complete the total calculation.

Physical properties of biological entities: an introduction to the ontology of physics for biology

As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities-molecules, cells, organs-are well-established, there are no principled ontologies of physical properties-energies, volumes, flow rates-of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration.

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.

aGEM: an integrative system for analyzing spatial-temporal gene-expression information

Motivation: The work presented here describes the ‘anatomical Gene-Expression Mapping (aGEM)’ Platform, a development conceived to integrate phenotypic information with the spatial and temporal distributions of genes expressed in the mouse. The aGEM Platform has been built by extending the Distributed Annotation System (DAS) protocol, which was originally designed to share genome annotations over the WWW. DAS is a client-server system in which a single client integrates information from multiple distributed servers.

Results: The aGEM Platform provides information to answer three main questions. (i) Which genes are expressed in a given mouse anatomical component? (ii) In which mouse anatomical structures are a given gene or set of genes expressed? And (iii) is there any correlation among these findings? Currently, this Platform includes several well-known mouse resources (EMAGE, GXD and GENSAT), hosting gene-expression data mostly obtained from in situ techniques together with a broad set of image-derived annotations.

Availability: The Platform is optimized for Firefox 3.0 and it is accessed through a friendly and intuitive display: http://agem.cnb.csic.es

Contact:natalia@cnb.csic.es

Supplementary information:Supplementary data are available at http://bioweb.cnb.csic.es/VisualOmics/aGEM/home.html and http://bioweb.cnb.csic.es/VisualOmics/index_VO.html and Bioinformatics online.

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.

Imaging in systems biology

Most systems biology approaches involve determining the structure of biological circuits using genomewide "-omic" analyses. Yet imaging offers the unique advantage of watching biological circuits function over time at single-cell resolution in the intact animal. Here, we discuss the power of integrating imaging tools with more conventional -omic approaches to analyze the biological circuits of microorganisms, plants, and animals.

Anatomics: the intersection of anatomy and bioinformatics

Computational resources are now using the tissue names of the major model organisms so that tissue-associated data can be archived in and retrieved from databases on the basis of developing and adult anatomy. For this to be done, the set of tissues in that organism (its anatome) has to be organized in a way that is computer-comprehensible. Indeed, such formalization is a necessary part of what is becoming known as systems biology, in which explanations of high-level biological phenomena are not only sought in terms of lower-level events, but are articulated within a computational framework. Lists of tissue names alone, however, turn out to be inadequate for this formalization because tissue organization is essentially hierarchical and thus cannot easily be put into tables, the natural format of relational databases. The solution now adopted is to organize the anatomy of each organism as a hierarchy of tissue names and linking relationships (e.g. the tibia is PART OF the leg, the tibia IS-A bone) within what are known as ontologies. In these, a unique ID is assigned to each tissue and this can be used within, for example, gene-expression databases to link data to tissue organization, and also used to query other data sources (interoperability), while inferences about the anatomy can be made within the ontology on the basis of the relationships. There are now about 15 such anatomical ontologies, many of which are linked to organism databases; these ontologies are now publicly available at the Open Biological Ontologies website (http://obo.sourceforge.net) from where they can be freely downloaded and viewed using standard tools. This review considers how anatomy is formalized within ontologies, together with the problems that have had to be solved for this to be done. It is suggested that the appropriate term for the analysis, computer formulation and use of the anatome is anatomics.