Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada.
Bioinformatics. 2013 Mar 15;29(6):686-94. doi: 10.1093/bioinformatics/btt031. Epub 2013 Jan 22.
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.
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.
磷酸化是真核生物中最重要的翻译后修饰。尽管有许多用于哺乳动物的计算磷酸化位点预测工具，并且有几个是专门为拟南芥创建的，但目前尚无其他植物的工具。
在本文中，我们提出了一种新的基于随机森林的方法，称为 PHOSFER（磷酸化位点查找器），用于将来自其他生物体的磷酸化数据应用于提高目标生物体中预测的准确性。作为一个测试案例，PHOSFER 应用于大豆中的磷酸化位点，我们表明它比现有的拟南芥特异性预测器以及仅使用大豆中已知的磷酸化和非磷酸化位点的更简单的机器学习方案更准确地预测了大豆的位点。除了大豆，PHOSFER 将在不久的将来扩展到其他生物体。
