Abstract
Background: 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.Results: 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.Conclusions: 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. © 2010 Gao and Xu; licensee BioMed Central Ltd.
Cite
CITATION STYLE
Gao, J., & Xu, D. (2010). The Musite open-source framework for phosphorylation-site prediction. BMC Bioinformatics, 11(SUPPL. 12). https://doi.org/10.1186/1471-2105-11-S12-S9
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.