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Bioinformatics Advance Access originally published online on February 5, 2007
Bioinformatics 2007 23(7):895-897; doi:10.1093/bioinformatics/btm020
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

NetPhosYeast: prediction of protein phosphorylation sites in yeast

Christian R. Ingrell 1, Martin L. Miller 2, Ole N. Jensen 1 and Nikolaj Blom 2,*

1University of Southern Denmark, Campusvej 55, DK-5230, Odense M, Denmark and 2Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Anker Engelunds Vej 1, DK-2800 Kgs. Lyngby, Denmark

*To whom correspondence should be addressed.


  Abstract

Summary: We here present a neural network-based method for the prediction of protein phosphorylation sites in yeast—an important model organism for basic research. Existing protein phosphorylation site predictors are primarily based on mammalian data and show reduced sensitivity on yeast phosphorylation sites compared to those in humans, suggesting the need for an yeast-specific phosphorylation site predictor. NetPhosYeast achieves a correlation coefficient close to 0.75 with a sensitivity of 0.84 and specificity of 0.90 and outperforms existing predictors in the identification of phosphorylation sites in yeast.

Availability: The NetPhosYeast prediction service is available as a public web server at http://www.cbs.dtu.dk/services/NetPhosYeast/

Contact: nikob{at}cbs.dtu.dk

Associate Editor: Thomas Lengauer

Received on November 3, 2006; revised on January 8, 2007; accepted on January 18, 2007

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