Last major update of page content: (see log): April 21, 2006 11:35
Computational modeling of the Plasmodium falciparum interactome
reveals protein function on a genome-wide scale
Shailesh V. Date* and Christian J. Stoeckert, Jr
Center for Bioinformatics, Department of Genetics,
School of Medicine, University of Pennsylvania, Philadelphia, PA 19104.
*Please address all correspondence to:
Companion website | Version 1.0
Many thousands of proteins encoded by the genome of Plasmodium falciparum, the causal organism of the deadliest form of human malaria, are of unknown function. It is of utmost importance that these proteins be characterized if we are to develop combative strategies against malaria based on the biology of the parasite. In an attempt to infer protein function on a genome-wide scale, we computationally modeled the P. falciparum interactome, elucidating local and global functional relationships between gene products. The resulting interaction network, reconstructed by integrating in silico and experimental functional genomics data within a Bayesian framework, covers ~68% of the parasite genome and provides functional inferences for more than 2000 uncharacterized proteins, based on their associations. Network reconstruction involved the use of a novel strategy, where we incorporated continuously updated, uniform reference priors in our Bayesian model. This method for generating interaction maps is thus also well suited for application to other genomes, where pre-existing interactome knowledge is sparse. Additionally, we superimposed this map on genomes of three apicomplexan pathogens Plasmodium yoelii, Toxoplasma gondii, and Cryptosporidium parvum describing relationships between these organisms based on retained functional linkages. This comparison provided a glimpse of the highly evolved nature of P. falciparum; for instance, a deficit of nearly 26% in terms of predicted interactions is observed against P. yoelii, because of missing ortholog partners in pairs of functionally linked proteins.
[Supplemental material is available online at www.genome.org and results from this study are available for download from http://cbil.upenn.edu/plasmoMAP/.]
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Note: Viewing LGL files requires LGLView, a java application that will run on any computer that has java installed. To download the viewer, go to:
Please follow the instructions described on the LGLView page regarding the required SUN Java version. The network files are extremely large, and it will be best to run LGLView on machines with 1GB or more RAM.
Please refer to: Adai AT, Date SV, Wieland S, Marcotte EM (2004) LGL: creating a map of protein function with an algorithm for visualizing very large biological networks. J Mol Biol 340: 179-90.
Last major update of page content: April 21, 2006 11:35
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