Anatomy ontologies and potential users: bridging the gap

Towards a semantic approach to numerical tree inference in phylogenetics

Conventional approaches to phylogeny reconstruction require a character analysis step prior to and methodologically separated from a numerical tree inference step. The former results in a character matrix that contains the empirical data analysed in the latter. This separation of steps involves various methodological and conceptual problems (e.g. homology assessment independent of tree inference and character optimization, character dependencies, discounting of alternative homology hypotheses). In morphology, the character analysis step covers the stages of morphological comparative studies, homology assessment and the identification and coding of morphological characters. Unfortunately, only the last stage requires some formalism, whereas the preceding stages are commonly regarded to be pre-rational and intuitive, which is why their reproducibility and analytical accessibility is limited. Here, I introduce a rational for a semantic approach to numerical tree inference that uses sets of semantic instance anatomies as data source instead of character matrices, thereby avoiding the above-mentioned problems. A semantic instance anatomy is an ontology-based description of the anatomical organization of a specimen in the form of a semantic graph. The semantic approach to numerical tree inference combines and integrates the steps of character analysis and numerical tree inference and makes both analytically accessible and communicable. Before outlining first steps for a research programme dedicated to the semantic approach to numerical tree inference, I discuss in detail the methodological, conceptual, and computational challenges and requirements that first have to be dealt with before adequate algorithms can be developed.

My Corporis Fabrica: an ontology-based tool for reasoning and querying on complex anatomical models

Background

Multiple models of anatomy have been developed independently and for different purposes. In particular, 3D graphical models are specially useful for visualizing the different organs composing the human body, while ontologies such as FMA (Foundational Model of Anatomy) are symbolic models that provide a unified formal description of anatomy. Despite its comprehensive content concerning the anatomical structures, the lack of formal descriptions of anatomical functions in FMA limits its usage in many applications. In addition, the absence of connection between 3D models and anatomical ontologies makes it difficult and time-consuming to set up and access to the anatomical content of complex 3D objects.

Results

First, we provide a new ontology of anatomy called My Corporis Fabrica (MyCF), which conforms to FMA but extends it by making explicit how anatomical structures are composed, how they contribute to functions, and also how they can be related to 3D complex objects. Second, we have equipped MyCF with automatic reasoning capabilities that enable model checking and complex queries answering. We illustrate the added-value of such a declarative approach for interactive simulation and visualization as well as for teaching applications.

Conclusions

The novel vision of ontologies that we have developed in this paper enables a declarative assembly of different models to obtain composed models guaranteed to be anatomically valid while capturing the complexity of human anatomy. The main interest of this approach is its declarativity that makes possible for domain experts to enrich the knowledge base at any moment through simple editors without having to change the algorithmic machinery. This provides MyCF software environment a flexibility to process and add semantics on purpose for various applications that incorporate not only symbolic information but also 3D geometric models representing anatomical entities as well as other symbolic information like the anatomical functions.

MouseFinder: Candidate disease genes from mouse phenotype data

Mouse phenotype data represents a valuable resource for the identification of disease-associated genes, especially where the molecular basis is unknown and there is no clue to the candidate gene's function, pathway involvement or expression pattern. However, until recently these data have not been systematically used due to difficulties in mapping between clinical features observed in humans and mouse phenotype annotations. Here, we describe a semantic approach to solve this problem and demonstrate highly significant recall of known disease-gene associations and orthology relationships. A Web application (MouseFinder; www.mousemodels.org) has been developed to allow users to search the results of our whole-phenome comparison of human and mouse. We demonstrate its use in identifying ARTN as a strong candidate gene within the 1p34.1-p32 mapped locus for a hereditary form of ptosis.