Immunoinformatics and epitope prediction in the age of genomic medicine

被引:150
作者
Backert, Linus [1 ,2 ]
Kohlbacher, Oliver [1 ,2 ,3 ,4 ]
机构
[1] Univ Tubingen, Ctr Bioinformat, Appl Bioinformat, D-72076 Tubingen, Germany
[2] Univ Tubingen, Dept Comp Sci, D-72076 Tubingen, Germany
[3] Univ Tubingen, Quantitat Biol Ctr, D-72076 Tubingen, Germany
[4] Max Planck Inst Dev Biol, Biomol Interact, D-72076 Tubingen, Germany
关键词
Immunoinformatics; Bioinformatics; Next-generation sequencing; Machine learning; HLA; Vaccine design; Personalized medicine; I BINDING PEPTIDES; T-CELL EPITOPES; INDEPENDENT BINDING; VACCINE DESIGN; MHC MOLECULES; HLA-DR; BENCHMARKING; DATABASE; PROTEIN; SERVER;
D O I
10.1186/s13073-015-0245-0
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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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页数:12
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