Functional annotation from predicted protein interaction networks

Authors:
Jason Mcdermott;Roger Bumgarner;Ram Samudrala
Affiliations:
Department of Microbiology, Box 357242 University of Washington School of Medicine Seattle, WA 98195, USA;Department of Microbiology, Box 357242 University of Washington School of Medicine Seattle, WA 98195, USA;Department of Microbiology, Box 357242 University of Washington School of Medicine Seattle, WA 98195, USA
Venue:
Bioinformatics
Year:
2005

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Abstract

Motivation: Progress in large-scale experimental determination of protein--protein interaction networks for several organisms has resulted in innovative methods of functional inference based on network connectivity. However, the amount of effort and resources required for the elucidation of experimental protein interaction networks is prohibitive. Previously we, and others, have developed techniques to predict protein interactions for novel genomes using computational methods and data generated from other genomes. Results: We evaluated the performance of a network-based functional annotation method that makes use of our predicted protein interaction networks. We show that this approach performs equally well on experimentally derived and predicted interaction networks, for both manually and computationally assigned annotations. We applied the method to predicted protein interaction networks for over 50 organisms from all domains of life, providing annotations for many previously unannotated proteins and verifying existing low-confidence annotations. Availability: Functional predictions for over 50 organisms are available at http://bioverse.compbio.washington.edu and datasets used for analysis at http://data.compbio.washington.edu/misc/downloads/nannotation_data/ Contact: admin@bioverse.compbio.washington.edu Supplementary information: A supplemental appendix gives additional details not in the main text. (http://data.compbio.washington.edu/misc/downloads/nannotation_data/supplement.pdf).