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Bioinformatics Advance Access first published online on July 4, 2009
This version published online on July 6, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp409
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

FRED - A Framework for T-cell Epitope Detection

Magdalena Feldhahn 1,*, Pierre Dönnes 2, Philipp Thiel 1 and Oliver Kohlbacher 1,*

1Division for Simulation of Biological Systems, WSI/ZBIT, University of Tübingen, Sand 14, D-72076 Tübingen, Germany

*To whom correspondence should be addressed. Magdalena Feldhahn, E-mail: feldhahn{at}informatik.uni-tuebingen.de


  Abstract

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.

Associate Editor: Prof. John Quackenbush

2 Present address: Molecular Toxicology, Safety Assessment, AstraZeneca R&D, S-15185 Södertälje, Sweden

Received on March 8, 2009; revised on June 22, 2009; accepted on June 28, 2009

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