Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: BioSysBio: Bioinformatics and Systems Biology Conference

Open Access Poster presentation

Automated Terminological and Structural Analysis of Human-Mouse Anatomical Ontology Mappings

Sarah Luger*, Stuart Aitken and Bonnie Webber

Author Affiliations

The University of Edinburgh, School of Informatics, Edinburgh EH8 9LW, UK

For all author emails, please log on.

BMC Bioinformatics 2005, 6(Suppl 3):P19  doi:10.1186/1471-2105-6-S3-P19

The electronic version of this article is the complete one and can be found online at:


Published:21 September 2005

©

Poster presentation

Anatomical information is crucial to human biomedical research but not all research is based on human tissues. However, exploiting discoveries in model organisms such as the mouse at a systems level, involving metabolic and developmental networks in tissues, requires the identification of the links between human and model organism anatomies. The question is: can we exploit similarities between mouse and human to automatically associate data between them?

We start with the current anatomy ontologies for mouse (the Mouse Anatomy Nomenclature [1]) and for human. Consider the arterial system (EHDAA.1024) and its counterpart in the mouse ontology (EMAPA.16371):

Human/ extraembyonic component/ vascular component/ arterial system Mouse/ extraembryonic component/ cardiovascular system/ arterial system

Are the two tissues similar? These may be structurally different paths made up of lexically similar terms or structurally similar paths made up of some different terms. In fact, either tissue path in one species may have no corresponding tissue path in the other species.

We use language processing to normalize the ontologies' paths. This includes regularizing spelling variants, removing stop words, stemming and lemmatizing content words, and treating the descriptors in an individual node label as a set. Next, tissue pairs above a similarity threshold are assessed structurally. This involves viewing the ontologies as graphs with directed but unlabelled edges [2].

At similarity thresholds above 90%, we found the percentage of structurally compatible matches varied between 84.7% and 92.8%.

References

  1. Hayamizu TF, Mangan M, Corradi JP, Kadin JA, Ringwald M: The Adult Mouse Anatomical Dictionary.

    Genome Biology 2005, 6:R29. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  2. Luger S, Aitken JS, Webber BL: Cross-species Mapping between Anatomical Ontologies Based on Lexico-syntactic Properties.

    ISMB-2004. Poster C-40 OpenURL