ArticleBriefings in bioinformatics2018
Evaluation of computational programs to predict HLA genotypes from genomic sequencing data.
Article in Briefings in bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- From fragments to full pictures: how long-read sequencing platforms are revolutionizing oncology research.Molecular biology reports · 2026Review
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- Quantifying HLA transcripts by genotype in chimeric mixtures at single-cell resolution.bioRxiv : the preprint server for biology · 2025Article
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review).International journal of oncology · 2025Review
- Article
- Ancient DNA HLA typing reveals significant shifts in frequency in Europe since the Neolithic.Scientific reports · 2025Article
- A novel framework for human leukocyte antigen (HLA) genotyping using probe capture-based targeted next-generation sequencing and computational analysis.Computational and structural biotechnology journal · 2024Article
- Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Article
- A rigorous benchmarking of alignment-based HLA typing algorithms for RNA-seq data.bioRxiv : the preprint server for biology · 2024Article
- Historical perspective and future directions: computational science in immuno-oncology.Journal for immunotherapy of cancer · 2024Review
- Evaluation of HLA typing content of next-generation sequencing datasets from family trios and individuals of arab ethnicity.Frontiers in genetics · 2024Article
- Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.Frontiers in immunology · 2024Review
- Leading the pack: Best practices in comparative canine cancer genomics to inform human oncology.Veterinary and comparative oncology · 2023Review
- Proteogenomics Reveal the Overexpression of HLA-I in Cancer.Journal of proteome research · 2023Article
- Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data.BMC genomics · 2023Article
- HLA Variation and SARS-CoV-2 Specific Antibody Response.Viruses · 2023Review
- A Kmer-based paired-end readiScience · 2023Article
- Genome screening, reporting, and genetic counseling for healthy populations.Human genetics · 2023Article
- Phenotypic pliancy and the breakdown of epigenetic polycomb mechanisms.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Despite being essential for numerous clinical and research applications, high-resolution human leukocyte antigen (HLA) typing remains challenging and laboratory tests are also time-consuming and labour intensive. With next-generation sequencing data becoming widely accessible, on-demand in silico HLA typing offers an economical and efficient alternative. Results: In this study we evaluate the HLA typing accuracy and efficiency of five computational HLA typing methods by comparing their predictions against a curated set of > 1000 published polymerase chain reaction-derived HLA genotypes on three different data sets (whole genome sequencing, whole exome sequencing and transcriptomic sequencing data). The highest accuracy at clinically relevant resolution (four digits) we observe is 81% on RNAseq data by PHLAT and 99% accuracy by OptiType when limited to Class I genes only. We also observed variability between the tools for resource consumption, with runtime ranging from an average of 5 h (HLAminer) to 7 min (seq2HLA) and memory from 12.8 GB (HLA-VBSeq) to 0.46 GB (HLAminer) per sample. While a minimal coverage is required, other factors also determine prediction accuracy and the results between tools do not correlate well. Therefore, by combining tools, there is the potential to develop a highly accurate ensemble method that is able to deliver fast, economical HLA typing from existing sequencing data.
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