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The Choice between MapMan and Gene Ontology for Automated Gene Function Prediction in Plant Science

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons97232

Klie,  S.
Small Molecules, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;
BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97320

Nikoloski,  Z.
Mathematical Modelling and Systems Biology, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Klie-2012-The Choice between M.pdf
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Zitation

Klie, S., & Nikoloski, Z. (2012). The Choice between MapMan and Gene Ontology for Automated Gene Function Prediction in Plant Science. Frontiers in Genetics, 3, 115. doi:10.3389/fgene.2012.00115.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0014-1F5C-6
Zusammenfassung
Since the introduction of the Gene Ontology (GO), the analysis of high-throughput data has become tightly coupled with the use of ontologies to establish associations between knowledge and data in an automated fashion. Ontologies provide a systematic description of knowledge by a controlled vocabulary of defined structure in which ontological concepts are connected by pre-defined relationships. In plant science, MapMan and GO offer two alternatives for ontology-driven analyses. Unlike GO, initially developed to characterize microbial systems, MapMan was specifically designed to cover plant-specific pathways and processes. While the dependencies between concepts in MapMan are modeled as a tree, in GO these are captured in a directed acyclic graph. Therefore, the difference in ontologies may cause discrepancies in data reduction, visualization, and hypothesis generation. Here provide the first systematic comparative analysis of GO and MapMan for the case of the model plant species Arabidopsis thaliana (Arabidopsis) with respect to their structural properties and difference in distributions of information content. In addition, we investigate the effect of the two ontologies on the specificity and sensitivity of automated gene function prediction via the coupling of co-expression networks and the guilt-by-association principle. Automated gene function prediction is particularly needed for the model plant Arabidopsis in which only half of genes have been functionally annotated based on sequence similarity to known genes. The results highlight the need for structured representation of species-specific biological knowledge, and warrants caution in the design principles employed in future ontologies.