PMID- 26589500
OWN - NLM
STAT- MEDLINE
DCOM- 20161005
LR  - 20220330
IS  - 1756-994X (Electronic)
IS  - 1756-994X (Linking)
VI  - 7
DP  - 2015 Nov 20
TI  - Immunoinformatics and epitope prediction in the age of genomic medicine.
PG  - 119
LID - 10.1186/s13073-015-0245-0 [doi]
LID - 119
AB  - 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.
FAU - Backert, Linus
AU  - Backert L
AD  - Applied Bioinformatics, Center of Bioinformatics and Department of Computer 
      Science, University of Tubingen, Sand 14, 72076, Tubingen, Germany. 
      backert@informatik.uni-tuebingen.de.
FAU - Kohlbacher, Oliver
AU  - Kohlbacher O
AD  - Applied Bioinformatics, Center of Bioinformatics and Department of Computer 
      Science, University of Tubingen, Sand 14, 72076, Tubingen, Germany.
AD  - Quantitative Biology Center, University of Tubingen, Auf der Morgenstelle 10, 
      72076, Tubingen, Germany.
AD  - Biomolecular Interactions, Max Planck Institute for Developmental Biology, 
      Spemannstrasse 35, 72076, Tubingen, Germany.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20151120
PL  - England
TA  - Genome Med
JT  - Genome medicine
JID - 101475844
RN  - 0 (HLA Antigens)
SB  - IM
MH  - Computational Biology/*methods
MH  - Epitope Mapping/*methods
MH  - Genomics/*methods
MH  - HLA Antigens/genetics
MH  - High-Throughput Nucleotide Sequencing/methods
MH  - Humans
MH  - Neoplasms/immunology/therapy
MH  - Precision Medicine/*methods
PMC - PMC4654883
EDAT- 2015/11/22 06:00
MHDA- 2016/10/07 06:00
PMCR- 2015/11/20
CRDT- 2015/11/22 06:00
PHST- 2015/11/22 06:00 [entrez]
PHST- 2015/11/22 06:00 [pubmed]
PHST- 2016/10/07 06:00 [medline]
PHST- 2015/11/20 00:00 [pmc-release]
AID - 10.1186/s13073-015-0245-0 [pii]
AID - 245 [pii]
AID - 10.1186/s13073-015-0245-0 [doi]
PST - epublish
SO  - Genome Med. 2015 Nov 20;7:119. doi: 10.1186/s13073-015-0245-0.