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FRED—a framework for T-cell epitope detection

Summary: Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different meth...

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Detalles Bibliográficos
Autores principales: Feldhahn, Magdalena, Dönnes, Pierre, Thiel, Philipp, Kohlbacher, Oliver
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759545/
https://www.ncbi.nlm.nih.gov/pubmed/19578173
http://dx.doi.org/10.1093/bioinformatics/btp409
Descripción
Sumario:Summary: Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods. Availability: FRED is freely available for download at http://www-bs.informatik.uni-tuebingen.de/Software/FRED. Contact: feldhahn@informatik.uni-tuebingen.de