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Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data

BACKGROUND: The Human Leukocyte Antigen (HLA) genes are a group of highly polymorphic genes that are located in the Major Histocompatibility Complex (MHC) region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. While knowledge of these genotypes i...

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Autores principales: Claeys, Arne, Merseburger, Peter, Staut, Jasper, Marchal, Kathleen, Van den Eynden, Jimmy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10170851/
https://www.ncbi.nlm.nih.gov/pubmed/37161318
http://dx.doi.org/10.1186/s12864-023-09351-z
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author Claeys, Arne
Merseburger, Peter
Staut, Jasper
Marchal, Kathleen
Van den Eynden, Jimmy
author_facet Claeys, Arne
Merseburger, Peter
Staut, Jasper
Marchal, Kathleen
Van den Eynden, Jimmy
author_sort Claeys, Arne
collection PubMed
description BACKGROUND: The Human Leukocyte Antigen (HLA) genes are a group of highly polymorphic genes that are located in the Major Histocompatibility Complex (MHC) region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. While knowledge of these genotypes is of utmost importance to study differences in immune responses between cancer patients, gold standard, PCR-derived genotypes are rarely available in large Next Generation Sequencing (NGS) datasets. Therefore, a variety of methods for in silico NGS-based HLA genotyping have been developed, bypassing the need to determine these genotypes with separate experiments. However, there is currently no consensus on the best performing tool. RESULTS: We evaluated 13 MHC class I and/or class II HLA callers that are currently available for free academic use and run on either Whole Exome Sequencing (WES) or RNA sequencing data. Computational resource requirements were highly variable between these tools. Three orthogonal approaches were used to evaluate the accuracy on several large publicly available datasets: a direct benchmark using PCR-derived gold standard HLA calls, a correlation analysis with population-based allele frequencies and an analysis of the concordance between the different tools. The highest MHC-I calling accuracies were found for Optitype (98.0%) and arcasHLA (99.4%) on WES and RNA sequencing data respectively, while for MHC-II HLA-HD was the most accurate tool for both data types (96.2% and 99.4% on WES and RNA data respectively). CONCLUSION: The optimal strategy for HLA genotyping from NGS data depends on the availability of either WES or RNA data, the size of the dataset and the available computational resources. If sufficient resources are available, we recommend Optitype and HLA-HD for MHC-I and MHC-II genotype calling respectively. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09351-z.
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spelling pubmed-101708512023-05-11 Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data Claeys, Arne Merseburger, Peter Staut, Jasper Marchal, Kathleen Van den Eynden, Jimmy BMC Genomics Research BACKGROUND: The Human Leukocyte Antigen (HLA) genes are a group of highly polymorphic genes that are located in the Major Histocompatibility Complex (MHC) region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. While knowledge of these genotypes is of utmost importance to study differences in immune responses between cancer patients, gold standard, PCR-derived genotypes are rarely available in large Next Generation Sequencing (NGS) datasets. Therefore, a variety of methods for in silico NGS-based HLA genotyping have been developed, bypassing the need to determine these genotypes with separate experiments. However, there is currently no consensus on the best performing tool. RESULTS: We evaluated 13 MHC class I and/or class II HLA callers that are currently available for free academic use and run on either Whole Exome Sequencing (WES) or RNA sequencing data. Computational resource requirements were highly variable between these tools. Three orthogonal approaches were used to evaluate the accuracy on several large publicly available datasets: a direct benchmark using PCR-derived gold standard HLA calls, a correlation analysis with population-based allele frequencies and an analysis of the concordance between the different tools. The highest MHC-I calling accuracies were found for Optitype (98.0%) and arcasHLA (99.4%) on WES and RNA sequencing data respectively, while for MHC-II HLA-HD was the most accurate tool for both data types (96.2% and 99.4% on WES and RNA data respectively). CONCLUSION: The optimal strategy for HLA genotyping from NGS data depends on the availability of either WES or RNA data, the size of the dataset and the available computational resources. If sufficient resources are available, we recommend Optitype and HLA-HD for MHC-I and MHC-II genotype calling respectively. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09351-z. BioMed Central 2023-05-09 /pmc/articles/PMC10170851/ /pubmed/37161318 http://dx.doi.org/10.1186/s12864-023-09351-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Claeys, Arne
Merseburger, Peter
Staut, Jasper
Marchal, Kathleen
Van den Eynden, Jimmy
Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data
title Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data
title_full Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data
title_fullStr Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data
title_full_unstemmed Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data
title_short Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data
title_sort benchmark of tools for in silico prediction of mhc class i and class ii genotypes from ngs data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10170851/
https://www.ncbi.nlm.nih.gov/pubmed/37161318
http://dx.doi.org/10.1186/s12864-023-09351-z
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