A fully computational and reasonable representation for karyotypes.

SUMMARY: The human karyotype has been used as a mechanism for describing and detecting gross abnormalities in the genome for many decades. It is used both for routine diagnostic purposes and for research to further our understanding of the causes of disease. Despite these important applications there has been no rigorous computational representation of the karyotype; rather an informal, string-based representation is used, making it hard to check, organize and search data of this form. In this article, we describe our use of OWL, the Ontology Web Language, to generate a fully computational representation of the karyotype; the development of this ontology represents a significant advance from the traditional bioinformatics use for tagging and navigation and has necessitated the development of a new ontology development environment called Tawny-OWL. AVAILABILITY AND IMPLEMENTATION: The Karyotype Ontology and associated Tawny-OWL source code is available on GitHub at https://github.com/jaydchan/tawny-karyotype, under a LGPL License, Version 3.0.

A document-centric approach for developing the tolAPC ontology.

BACKGROUND: There are many challenges associated with ontology building, as the process often touches on many different subject areas; it needs knowledge of the problem domain, an understanding of the ontology formalism, software in use and, sometimes, an understanding of the philosophical background. In practice, it is very rare that an ontology can be completed by a single person, as they are unlikely to combine all of these skills. So people with these skills must collaborate. One solution to this is to use face-to-face meetings, but these can be expensive and time-consuming for teams that are not co-located. Remote collaboration is possible, of course, but one difficulty here is that domain specialists use a wide-variety of different "formalisms" to represent and share their data - by the far most common, however, is the "office file" either in the form of a word-processor document or a spreadsheet. Here we describe the development of an ontology of immunological cell types; this was initially developed by domain specialists using an Excel spreadsheet for collaboration. We have transformed this spreadsheet into an ontology using highly-programmatic and pattern-driven ontology development. Critically, the spreadsheet remains part of the source for the ontology; the domain specialists are free to update it, and changes will percolate to the end ontology. RESULTS: We have developed a new ontology describing immunological cell lines built by instantiating ontology design patterns written programmatically, using values from a spreadsheet catalogue. CONCLUSIONS: This method employs a spreadsheet that was developed by domain experts. The spreadsheet is unconstrained in its usage and can be freely updated resulting in a new ontology. This provides a general methodology for ontology development using data generated by domain specialists.