Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data
(2022)
- Author
- Arne Claeys (UGent) , Jasper Staut (UGent) , Peter Merseburger (UGent) , Kathleen Marchal (UGent) and Jimmy Van den Eynden (UGent)
- Organization
- Abstract
- The HLA genes are a group of highly polymorphic genes that are located in the MHC region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. Therefore, methods to accurately type HLA alleles are critical to study differences in immune response between cancer patients. PCR-based methods are the current gold standard, but large-scale datasets with PCR-based HLA genotypes are rarely available. 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. Here, we compiled a list of 13 HLA callers and evaluated their accuracy on three different datasets. Based on these results, best-practice guidelines were constructed, and consensus HLA allele predictions were made for DNA and RNA samples from The Cancer Genome Atlas (TCGA).
Downloads
-
2022.04.28.489842v1.full.pdf
- full text (Published version)
- |
- open access
- |
- |
- 693.49 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8758449
- MLA
- Claeys, Arne, et al. Benchmark of Tools for in Silico Prediction of MHC Class I and Class II Genotypes from NGS Data. 2022, doi:10.1101/2022.04.28.489842.
- APA
- Claeys, A., Staut, J., Merseburger, P., Marchal, K., & Van den Eynden, J. (2022). Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data. https://doi.org/10.1101/2022.04.28.489842
- Chicago author-date
- Claeys, Arne, Jasper Staut, Peter Merseburger, Kathleen Marchal, and Jimmy Van den Eynden. 2022. “Benchmark of Tools for in Silico Prediction of MHC Class I and Class II Genotypes from NGS Data.” https://doi.org/10.1101/2022.04.28.489842.
- Chicago author-date (all authors)
- Claeys, Arne, Jasper Staut, Peter Merseburger, Kathleen Marchal, and Jimmy Van den Eynden. 2022. “Benchmark of Tools for in Silico Prediction of MHC Class I and Class II Genotypes from NGS Data.” doi:10.1101/2022.04.28.489842.
- Vancouver
- 1.Claeys A, Staut J, Merseburger P, Marchal K, Van den Eynden J. Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data. 2022.
- IEEE
- [1]A. Claeys, J. Staut, P. Merseburger, K. Marchal, and J. Van den Eynden, “Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data.” 2022.
@misc{8758449,
abstract = {{The HLA genes are a group of highly polymorphic genes that are located in the MHC region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. Therefore, methods to accurately type HLA alleles are critical to study differences in immune response between cancer patients. PCR-based methods are the current gold standard, but large-scale datasets with PCR-based HLA genotypes are rarely available. 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. Here, we compiled a list of 13 HLA callers and evaluated their accuracy on three different datasets. Based on these results, best-practice guidelines were constructed, and consensus HLA allele predictions were made for DNA and RNA samples from The Cancer Genome Atlas (TCGA).}},
author = {{Claeys, Arne and Staut, Jasper and Merseburger, Peter and Marchal, Kathleen and Van den Eynden, Jimmy}},
language = {{eng}},
pages = {{21}},
title = {{Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data}},
url = {{http://doi.org/10.1101/2022.04.28.489842}},
year = {{2022}},
}
- Altmetric
- View in Altmetric