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A predicted functional gene network for the plant pathogen Phytophthora infestans as a framework for genomic biology

Michael F Seidl123*, Adrian Schneider1, Francine Govers23 and Berend Snel12

Author Affiliations

1 Theoretical Biology and Bioinformatics, Department of Biology, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands

2 Centre for BioSystems Genomics, P.O. Box 98, Wageningen, 6700 AB, The Netherlands

3 Laboratory of Phytopathology, Wageningen University, P.O. Box 8025, Wageningen, 6700 EE, The Netherlands

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BMC Genomics 2013, 14:483  doi:10.1186/1471-2164-14-483

Published: 17 July 2013



Associations between proteins are essential to understand cell biology. While this complex interplay between proteins has been studied in model organisms, it has not yet been described for the oomycete late blight pathogen Phytophthora infestans.


We present an integrative probabilistic functional gene network that provides associations for 37 percent of the predicted P. infestans proteome. Our method unifies available genomic, transcriptomic and comparative genomic data into a single comprehensive network using a Bayesian approach. Enrichment of proteins residing in the same or related subcellular localization validates the biological coherence of our predictions. The network serves as a framework to query existing genomic data using network-based methods, which thus far was not possible in Phytophthora. We used the network to study the set of interacting proteins that are encoded by genes co-expressed during sporulation. This identified potential novel roles for proteins in spore formation through their links to proteins known to be involved in this process such as the phosphatase Cdc14.


The functional association network represents a novel genome-wide data source for P. infestans that also acts as a framework to interrogate other system-wide data. In both capacities it will improve our understanding of the complex biology of P. infestans and related oomycete pathogens.

Oomycetes; Phytophthora infestans; Protein-protein interaction; Protein-protein associations; Network; Bayesian integration; Protein complex; Functional module