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Release Notes
Dig Bio
Bond LSC
  • Bug fix release

  • 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.
  • 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.
  • 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.
  • Musite provides a user-friendly graphic user interface, which makes it easy for biologists to perform predictions in an automated fashion.
  • 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.
  • 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.
  • 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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