Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights

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Abstract

Motivation:

Phosphorylation is the most important post-translational modification in eukaryotes. Although many computational phosphorylation site prediction tools exist for mammals, and a few were created specifically for Arabidopsis thaliana, none are currently available for other plants.

Results:

In this article, we propose a novel random forest-based method called PHOSFER (PHOsphorylation Site FindER) for applying phosphorylation data from other organisms to enhance the accuracy of predictions in a target organism. As a test case, PHOSFER is applied to phosphorylation sites in soybean, and we show that it more accurately predicts soybean sites than both the existing Arabidopsis-specific predictors, and a simpler machine-learning scheme that uses only known phosphorylation sites and non-phosphorylation sites from soybean. In addition to soybean, PHOSFER will be extended to other organisms in the near future.

Availability:

PHOSFER is available via a web interface at http://saphire.usask.ca.

Contact:

brett.trost@usask.ca

Supplementary information:

Supplementary data are available at Bioinformatics online.

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