Evidence map›Paper›PMID 26589500›Full record

ReviewGenome medicine2015

Immunoinformatics and epitope prediction in the age of genomic medicine.

Linus Backert, Oliver Kohlbacher

Open access · goldAbstract readReview
In one paragraph

Review in Genome medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
105citing papers in PubMed, 2 pooled it
9.0field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

105 citing papers in PubMed, 2 syntheses or guidelines pooled it, 211 citations in OpenAlex.

  1. Pooled it
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  17. A review on trends in development and translation of omics signatures in cancer.Computational and structural biotechnology journal · 2024
    Review
  18. Deep learning of antibody epitopes using positional permutation vectors.Computational and structural biotechnology journal · 2024
    Article
  19. Review
  20. Review

45 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Linus BackertApplied Bioinformatics, Center of Bioinformatics and Department of Computer Science, University of Tübingen, Sand 14, 72076, Tübingen, Germany. backert@informatik.uni-tuebingen.de.
Oliver KohlbacherApplied Bioinformatics, Center of Bioinformatics and Department of Computer Science, University of Tübingen, Sand 14, 72076, Tübingen, Germany.
Max Planck Institute for Developmental Biology · DEUniversity of Tübingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Computational BiologyEpitope MappingGenomicsHigh-Throughput Nucleotide SequencingHLA AntigensHumansNeoplasmsPrecision MedicineHLA Antigens

Identifiers

PMID26589500
PMCPMC4654883
OpenAlexW2177083469

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.