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V1.0
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To address the various limitations of current tools when applying
to proteomes and to better utilize the large magnitude of experimentally
verified phosphorylation sites, we developed a unique standalone application
system Musite, specifically designed for large-scale prediction of both general
and kinase-specific phosphorylation sites.
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Musite utilized local sequence similarity patterns (KNN scores) and generic features
(disorder scores and amino acid frequencies) of phosphorylation sites, and employed
a comprehensive machine learning approach to make predictions.
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Musite is the first tool that provides utility for training a phosphorylation-site
prediction model from users' own data and supports continuous adjustment of
stringency levels.
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Musite provides a user-friendly graphic user interface, which makes it easy
for biologists to perform predictions in an automated fashion.
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Applications of Musite on six proteomes yielded tens of thousands of putative
phosphorylation sites with high stringency. These predictions provide useful
hypotheses for experimental validations.
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Cross-validation tests show that Musite significantly outperforms existing
tools for predicting general phosphorylation sites and is at least comparable
to those for predicting kinase-specific phosphorylation sites.
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Moreover, as an open-source software, Musite can be also served as an open
platform for building machine learning application for phosphorylation-site
prediction.
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