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The Musite open-source framework for phosphorylation-site prediction

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 spe...

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Detalles Bibliográficos
Autores principales: Gao, Jianjiong, Xu, Dong
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3040535/
https://www.ncbi.nlm.nih.gov/pubmed/21210988
http://dx.doi.org/10.1186/1471-2105-11-S12-S9
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author Gao, Jianjiong
Xu, Dong
author_facet Gao, Jianjiong
Xu, Dong
author_sort Gao, Jianjiong
collection PubMed
description 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.
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spelling pubmed-30405352011-02-18 The Musite open-source framework for phosphorylation-site prediction Gao, Jianjiong Xu, Dong BMC Bioinformatics Proceedings 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. BioMed Central 2010-12-21 /pmc/articles/PMC3040535/ /pubmed/21210988 http://dx.doi.org/10.1186/1471-2105-11-S12-S9 Text en Copyright ©2010 Gao and Xu; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Gao, Jianjiong
Xu, Dong
The Musite open-source framework for phosphorylation-site prediction
title The Musite open-source framework for phosphorylation-site prediction
title_full The Musite open-source framework for phosphorylation-site prediction
title_fullStr The Musite open-source framework for phosphorylation-site prediction
title_full_unstemmed The Musite open-source framework for phosphorylation-site prediction
title_short The Musite open-source framework for phosphorylation-site prediction
title_sort musite open-source framework for phosphorylation-site prediction
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3040535/
https://www.ncbi.nlm.nih.gov/pubmed/21210988
http://dx.doi.org/10.1186/1471-2105-11-S12-S9
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