FRED—a framework for T-cell epitope detection

Feldhahn, Magdalena; Dönnes, Pierre; Thiel, Philipp; Kohlbacher, Oliver

doi:10.1093/bioinformatics/btp409

Cited by 19 publications

(13 citation statements)

References 14 publications

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“…However, web services with an abstract description of the interface (e.g., RESTful interfaces, representational state transfer used by IEDB [ 15 ]) enable the integration of these tools into complex workflows driven by tailor-made code. Other options are toolboxes for rapid software prototyping integrating a larger number of algorithms into convenient scripting languages such as Python [ 87 ]. Furthermore, graphical workflow engines such as Galaxy [ 88 ] do not require programming skills.…”

Section: Integration and Application Of Immunoinformatics Toolsmentioning

confidence: 99%

Immunoinformatics and epitope prediction in the age of genomic medicine

Backert¹,

Kohlbacher²

2015

Genome Med

184

146

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Immunoinformatics involves the application of computational methods to immunological problems. Prediction of B- and T-cell epitopes has long been the focus of immunoinformatics, given the potential translational implications, and many tools have been developed. With the advent of next-generation sequencing (NGS) methods, an unprecedented wealth of information has become available that requires more-advanced immunoinformatics tools. Based on information from whole-genome sequencing, exome sequencing and RNA sequencing, it is possible to characterize with high accuracy an individual’s human leukocyte antigen (HLA) allotype (i.e., the individual set of HLA alleles of the patient), as well as changes arising in the HLA ligandome (the collection of peptides presented by the HLA) owing to genomic variation. This has allowed new opportunities for translational applications of epitope prediction, such as epitope-based design of prophylactic and therapeutic vaccines, and personalized cancer immunotherapies. Here, we review a wide range of immunoinformatics tools, with a focus on B- and T-cell epitope prediction. We also highlight fundamental differences in the underlying algorithms and discuss the various metrics employed to assess prediction quality, comparing their strengths and weaknesses. Finally, we discuss the new challenges and opportunities presented by high-throughput data-sets for the field of epitope prediction.

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Section: Integration and Application Of Immunoinformatics Toolsmentioning

confidence: 99%

Immunoinformatics and epitope prediction in the age of genomic medicine

Backert¹,

Kohlbacher²

2015

Genome Med

184

146

View full text Add to dashboard Cite

show abstract

“…We choose netMHCpan since it provides predictions for a wide range of HLA alleles. Our pipeline is based on FRED, a framework for epitope prediction (Feldhahn et al, 2009), and is therefore very flexible with respect to the prediction method to use.…”

Section: Step 3: Predicting Candidate Mihasmentioning

confidence: 99%

miHA-Match: Computational detection of tissue-specific minor histocompatibility antigens

Feldhahn¹,

Dönnes²,

Schubert³

et al. 2012

Journal of Immunological Methods

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“…A prerequisite for successful peptide vaccination therapy is the presentation on the tumor cell surface by MHC molecules. In silico digestion and HLA binding prediction (Feldhahn et al 2009) of the respective parts can shed light on which of these are putative tumor antigens suitable for further evaluation of immunogenicity in T-cell arrays.…”

Section: Bottom-upmentioning

confidence: 99%