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Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions
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 metho...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679531/ https://www.ncbi.nlm.nih.gov/pubmed/36426357 http://dx.doi.org/10.3389/fimmu.2022.987655 |
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author | Thuesen, Nikolas Hallberg Klausen, Michael Schantz Gopalakrishnan, Shyam Trolle, Thomas Renaud, Gabriel |
author_facet | Thuesen, Nikolas Hallberg Klausen, Michael Schantz Gopalakrishnan, Shyam Trolle, Thomas Renaud, Gabriel |
author_sort | Thuesen, Nikolas Hallberg |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9679531 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96795312022-11-23 Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions Thuesen, Nikolas Hallberg Klausen, Michael Schantz Gopalakrishnan, Shyam Trolle, Thomas Renaud, Gabriel Front Immunol Immunology 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. Frontiers Media S.A. 2022-11-08 /pmc/articles/PMC9679531/ /pubmed/36426357 http://dx.doi.org/10.3389/fimmu.2022.987655 Text en Copyright © 2022 Thuesen, Klausen, Gopalakrishnan, Trolle and Renaud https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Thuesen, Nikolas Hallberg Klausen, Michael Schantz Gopalakrishnan, Shyam Trolle, Thomas Renaud, Gabriel Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions |
title | Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions |
title_full | Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions |
title_fullStr | Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions |
title_full_unstemmed | Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions |
title_short | Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions |
title_sort | benchmarking freely available hla typing algorithms across varying genes, coverages and typing resolutions |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679531/ https://www.ncbi.nlm.nih.gov/pubmed/36426357 http://dx.doi.org/10.3389/fimmu.2022.987655 |
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