期刊
BIOINFORMATICS
卷 29, 期 6, 页码 686-694出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btt031
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资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
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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