An analysis of extensible modelling for functional genomics data.
BACKGROUND
Several data formats have been developed for large scale biological experiments, using a variety of methodologies. Most data formats contain a mechanism for allowing extensions to encode unanticipated data types. Extensions to data formats are important because the experimental methodologies tend to be fairly diverse and rapidly evolving, which hinders the creation of formats that will be stable over time.


RESULTS
In this paper we review the data formats that exist in functional genomics, some of which have become de facto or de jure standards, with a particular focus on how each domain has been modelled, and how each format allows extensions. We describe the tasks that are frequently performed over data formats and analyse how well each task is supported by a particular modelling structure.


CONCLUSIONS
From our analysis, we make recommendations as to the types of modelling structure that are most suitable for particular types of experimental annotation. There are several standards currently under development that we believe could benefit from systematically following a set of guidelines.
Jones
Andrew R
AR
School of Computer Science, University of Manchester, Manchester, UK. ajones@cs.man.ac.uk
Paton
Norman W
NW
D002623:Chemistry Techniques, Analytical; D003198:Computer Simulation; D003625:Data Collection / Q000592:standards*; D023281:Genomics / Q000379:methods* / Q000592:standards*; D017408:Guidelines as Topic; D013058:Mass Spectrometry; D046228:Microarray Analysis / Q000592:standards*; D008954:Models, Biological; D008958:Models, Molecular; D012984:Software; D018875:Vocabulary, Controlled