Anatomics: the intersection of anatomy and bioinformatics

Abstract

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.

Fig. 1

Pictorial representations of the various sorts of graphs that represent ontologies. (Reprinted from Bard, J.B.L. ‘Ontologies formalizing biological knowledge for bioinformatics’, BioEssays, 25, 501–506, with permission.)

Fig. 2

Screenshot of the Dagedit program showing the full Drosophila ontology (from Flybase*). In the left panel, the anatomy hierarchy is shown with the embryonic Malpighian tubules highlighted. The upper part of the central panel shows the ID associated with this tissue. The right panel shows some of the subhierarchies within the ontology that include the embryonic Malpighian tubules.

Fig. 3

Screenshot of the COBrA ontology program. The left panel shows the ontology of human developmental anatomy, which does not include the Carnegie stage in the name for each tissue. The right panel shows the zebrafish anatomy ontology, which does include the stagename for each tissue. Not shown are the alternative panels for these ontologies that give the relationships and IDs for the human (hidden on right) and zebrafish tissues (hidden on left).

Fig. 4

Screenshot of the Protégé program displaying the Foundation Model of (Adult Human) Anatomy (or FMA*). The ileum has been highlighted in the left panel, and some of the properties associated with this tissue are displayed in the left panel. Further properties are also accessible from the model.

Fig. 5

Composite picture of two screenshots. The upper screenshot shows the EMAP* site displaying the TS14 (E9) mouse embryo. The red line indicates the plane of the section shown in the central section; here, the cursor overlies the bulbus cordis (blue). The name of the tissue is given in the upper panel and its place in the ontology shown in the right panel. The lower screenshot shows the gene-expression data stored at GXD* for the TS14 bulbus cordis. This example of interoperability was initiated by double clicking on the bulbus cordis image in EMAP*.