Format

Send to

Choose Destination
Genome Med. 2015 Nov 20;7:119. doi: 10.1186/s13073-015-0245-0.

Immunoinformatics and epitope prediction in the age of genomic medicine.

Author information

1
Applied Bioinformatics, Center of Bioinformatics and Department of Computer Science, University of Tübingen, Sand 14, 72076, Tübingen, Germany. backert@informatik.uni-tuebingen.de.
2
Applied Bioinformatics, Center of Bioinformatics and Department of Computer Science, University of Tübingen, Sand 14, 72076, Tübingen, Germany.
3
Quantitative Biology Center, University of Tübingen, Auf der Morgenstelle 10, 72076, Tübingen, Germany.
4
Biomolecular Interactions, Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany.

Abstract

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.

PMID:
26589500
PMCID:
PMC4654883
DOI:
10.1186/s13073-015-0245-0
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center