The Musite open-source framework for phosphorylation-site prediction.
The Musite open-source framework for phosphorylation-site prediction.
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DOI：
10.1186/1471-2105-11-s12-s9
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发表时间：
2010-12-21
影响因子：
3
通讯作者：
Xu D
中科院分区：
文献类型：
--
作者：
Gao J;Xu D
随着磷酸化蛋白质组学数据的快速积累，磷酸化位点预测成为一个日益活跃的研究领域。在过去的十年中，已经发布了十几种磷酸化位点预测工具。然而，除了Musite之外，目前还没有专门为磷酸化位点预测设计的开源框架。在这里，我们介绍了Musite开源框架，用于构建应用程序来执行基于机器学习的磷酸化位点预测。Musite由六个彼此松散耦合的模块实现。凭借其精心设计的Java应用程序编程接口（API），Musite可以很容易地扩展到整合磷酸化位点预测的各种生物学证据来源。Musite在GNU GPL开源许可下发布，为磷酸化位点预测提供了一个开放和可扩展的框架。该软件及其源代码可在http://musite.sourceforge.net上获得。
With the rapid accumulation of phosphoproteomics data, phosphorylation-site prediction is becoming an increasingly active research area. More than a dozen phosphorylation-site prediction tools have been released in the past decade. However, there is currently no open-source framework specifically designed for phosphorylation-site prediction except Musite. Here we present the Musite open-source framework for building applications to perform machine learning based phosphorylation-site prediction. Musite was implemented with six modules loosely coupled with each other. With its well-designed Java application programming interface (API), Musite can be easily extended to integrate various sources of biological evidence for phosphorylation-site prediction. Released under the GNU GPL open source license, Musite provides an open and extensible framework for phosphorylation-site prediction. The software with its source code is available at http://musite.sourceforge.net.