Conceptual data modelling for bioinformatics
Senior Lecturer in Bioinformatics in the School of Biological Sciences, University of Manchester, UK. His research interests past and present range from the theory of biopolymer evolution to modelling biochemical pathways, from the analysis of modular composition of genomes and proteins to the integration of biological data.
Professor of Computer Science at the University of Manchester where he co-leads the Information Management Group. His research interests have principally been in databases, in particular active databases, spatial databases, deductive object-oriented databases and user interfaces to databases. He is currently working on parallel object databases, spatio-temporal databases, distributed information systems and information management for bioinformatics.
Erich Bornberg-Bauer, School of Biological Sciences, University of Manchester, 2.205 Stopford Building, Oxford Road, Manchester M13 9PT, UK Tel: +44 (0) 161 275 7396 E-mail: ebb{at}bioinf.man.ac.uk
Current research in the biosciences depends heavily on the effective exploitation of huge amounts of data. These are in disparate formats, remotely dispersed, and based on the different vocabularies of various disciplines. Furthermore, data are often stored or distributed using formats that leave implicit many important features relating to the structure and semantics of the data. Conceptual data modelling involves the development of implementation independent models that capture and make explicit the principal structural properties of data. Entities such as a biopolymer or a reaction, and their relations, eg catalyses, can be formalised using a conceptual data model. Conceptual models are implementation-independent and can be transformed in systematic ways for implementation using different platforms, eg traditional database management systems. This paper describes the basics of the most widely used conceptual modelling notations, the ER (entity–relationship) model and the class diagrams of the UML (unified modelling language), and illustrates their use through several examples from bioinformatics. In particular, models are presented for protein structures and motifs, and for genomic sequences.
Keywords: conceptual data model (CDM), database, entity—relationship (ER), unified modelling language (UML), biological data
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