Evidence map›Paper›PMID 36426357›Full record

ArticleFrontiers in immunology2022

Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.

Nikolas Hallberg Thuesen, Michael Schantz Klausen, Shyam Gopalakrishnan, Thomas Trolle, Gabriel Renaud

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

24 citing papers in PubMed, 28 citations in OpenAlex.

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  15. An immunogenetic basis for lung cancer risk.Science (New York, N.Y.) · 2024
    Article
  16. Full resolution HLA and KIR genes annotation for human genome assemblies.bioRxiv : the preprint server for biology · 2024
    Article
  17. Article
  18. Frontiers in pharmacology · 2024
    Article
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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

5 authors at 2 institutions in 1 country.

Nikolas Hallberg ThuesenEvaxion Biotech, Copenhagen, Denmark.
Michael Schantz KlausenEvaxion Biotech, Copenhagen, Denmark.
Shyam GopalakrishnanSection for Hologenomics, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Thomas TrolleEvaxion Biotech, Copenhagen, Denmark.
Gabriel RenaudDepartment of Health Technology, Section for Bioinformatics, Technical University of Denmark, Lyngby, Denmark.
Technical University of Denmark · DKUniversity of Copenhagen · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying the specific human leukocyte antigen (HLA) allele combination of an individual is crucial in organ donation, risk assessment of autoimmune and infectious diseases and cancer immunotherapy. However, due to the high genetic polymorphism in this region, HLA typing requires specialized methods. We investigated the performance of five next-generation sequencing (NGS) based HLA typing tools with a non-restricted license namely HLA*LA, Optitype, HISAT-genotype, Kourami and STC-Seq. This evaluation was done for the five HLA loci, HLA-A, -B, -C, -DRB1 and -DQB1 using whole-exome sequencing (WES) samples from 829 individuals. The robustness of the tools to lower depth of coverage (DOC) was evaluated by subsampling and HLA typing 230 WES samples at DOC ranging from 1X to 100X. The HLA typing accuracy was measured across four typing resolutions. Among these, we present two clinically-relevant typing resolutions (P group and pseudo-sequence), which specifically focus on the peptide binding region. On average, across the five HLA loci examined, HLA*LA was found to have the highest typing accuracy. For the individual loci, HLA-A, -B and -C, Optitype's typing accuracy was the highest and HLA*LA had the highest typing accuracy for HLA-DRB1 and -DQB1. The tools' robustness to lower DOC data varied widely and further depended on the specific HLA locus. For all Class I loci, Optitype had a typing accuracy above 95% (according to the modification of the amino acids in the functionally relevant portion of the HLA molecule) at 50X, but increasing the DOC beyond even 100X could still improve the typing accuracy of HISAT-genotype, Kourami, and STC-seq across all five HLA loci as well as HLA*LA's typing accuracy for HLA-DQB1. HLA typing is also used in studies of ancient DNA (aDNA), which is often based on sequencing data with lower quality and DOC. Interestingly, we found that Optitype's typing accuracy is not notably impaired by short read length or by DNA damage, which is typical of aDNA, as long as the DOC is sufficiently high.

Indexed as

High-Throughput Nucleotide SequencingHLA-A AntigensAlgorithmsHistocompatibility TestingHumansSequence Analysis, DNAHLA-A Antigensalgorithmbenchmarkdepth of coveragehuman leukycote antigennext-generation sequencing (NGS)typing resolutionwhole exome sequencing

Identifiers

PMID36426357
PMCPMC9679531
OpenAlexW4308461138

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.