title: FRED--a framework for T-cell epitope detection.
abstract: 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.
  
  
  BACKGROUND
  FRED is freely available for download at http://www-bs.informatik.uni-tuebingen.de/Software/FRED.
authors: Division for Simulation of Biological Systems, WSI/ZBIT, University of Tübingen, Sand 14, D-72076 Tübingen, Germany. feldhahn@informatik.uni-tuebingen.de
authors: Magdalena
authors: M
authors: Feldhahn
authors: Pierre
authors: P
authors: Dönnes
authors: Philipp
authors: P
authors: Thiel
authors: Oliver
authors: O
authors: Kohlbacher
mesh_terms: D001665:Binding Sites / Q000276:immunology; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry*; D012984:Software
chemical_list: D018984:Epitopes, T-Lymphocyte