Benchmarking the HLA typing performance of Polysolver and Optitype in 50 Danish parental trios

Matey-Hernandez, María Luisa; Brunak, Søren; Izarzugaza, Jose M. G.

doi:10.1186/s12859-018-2239-6

Cited by 30 publications

(32 citation statements)

References 47 publications

(64 reference statements)

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“…In the Matey‐Hernandez et al . study, Polysolver and OptiType were benchmarked against 50 Danish parental trios . In this study, Polysolver was reported to be the most accurate typing tool at two‐field resolution, which is in discordance with the results obtained by Kiyotani et al .…”

Section: Introductioncontrasting

confidence: 53%

“…In the Matey-Hernandez et al study, Polysolver and OptiType were benchmarked against 50 Danish parental trios. 34 In this study, Polysolver was reported to be the most accurate typing tool at two-field resolution, which is in discordance with the results obtained by Kiyotani et al Another study, performed by Bauer et al, evaluated five computational tools for HLA typing: OptiType, PHLAT, HLA-VBSeq, seq2HLA, and HLAminer. 35 The performance of the five tools was benchmarked against a curated dataset of more than 1300 patient samples from five published studies where the patient samples had been HLA genotyped using PCR-based methods.…”

Section: Introductioncontrasting

confidence: 42%

“…Each method has its own advantages and limitations, and recent studies have benchmarked the different tools in order to characterize their performance. [32][33][34][35] Larjo et al benchmarked five tools: ATHLATES, HLAssign, HLAreporter, OptiType, and Omixon and compared their prediction accuracy on NGS data not especially targeted to the HLA typing: 94 in-house samples and 63 samples from the 1000 Genome collection. 32 They concluded that none of the tools consistently outperformed the others in their tested cases and that none of the five tools analyzed gave perfect assignment for all HLA genes.…”

Section: Introductionmentioning

confidence: 99%

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Improved HLA typing of Class I and Class II alleles from next‐generation sequencing data

Sverchkova

Anzar

Stratford

et al. 2019

HLA

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Precise HLA genotyping is of great clinical importance, albeit a challenging bioinformatics endeavor because of the hyper polymorphism of the HLA region. The ever‐increasing availability of next‐generation sequencing (NGS) solutions has spurred the development of several computational methods for predicting HLA genotypes from NGS data. Although some of these tools genotype HLA Class I alleles reasonably well, there is a need to incorporate integrative parameters related to ethnicity frequency information, in order to improve performance for both Class I and Class II alleles. Here, we present a bioinformatics method that addresses some of the current shortfalls in HLA genotyping from NGS. First, reads that map to the HLA region is aligned against a comprehensive library of reference HLA alleles. The allele type was then subsequently determined on the basis of the distribution of aligned reads, and the prior probabilities of the ethnic frequencies of alleles. Three public NGS datasets were used to benchmark the approach against six similar tools. The method outlined in this manuscript displayed an overall accuracy of 98.73% for Class I and 96.37% for Class II alleles. We illustrate an improved integrative approach that outperforms existing tools and is able to predict HLA alleles with improved fidelity for both Class I and Class II alleles.

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Section: Introductioncontrasting

confidence: 53%

Section: Introductioncontrasting

confidence: 42%

Section: Introductionmentioning

confidence: 99%

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Improved HLA typing of Class I and Class II alleles from next‐generation sequencing data

Sverchkova

Anzar

Stratford

et al. 2019

HLA

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“…genotyping tools has been carried out by multiple groups relying on either real-time PCR validation of a small set of HLA alleles 33 or correlations of HLA-types between family trios based on haplotype inferences. 34 We have established a novel approach for evaluating HLA genotyping methods, eliminating the uncertainty of haplotype inference and including all called HLA alleles. The details of our approach will be described in a separate manuscript (Ren Y. et al, in preparation), but as illustrated in Supplement Figure S2 using 12 TCGA cases, the performances of the current class-I and class-I HLA genotyping methods are vastly different, as represented by consistencies of the called HLA subtypes between germline (blood), tumor, and normal tissue exomes of the same patient.…”

Section: Discussionmentioning

confidence: 99%

HLA class-I and class-II restricted neoantigen loads predict overall survival in breast cancer

et al. 2020

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Tumors acquire numerous mutations during development and progression. When translated into proteins, these mutations give rise to neoantigens that can be recognized by T cells and generate antibodies, representing an exciting direction of cancer immunotherapy. While neoantigens have been reported in many cancer types, the profiling of neoantigens often focused on the class-I subtype that are presented to CD8 + T cells, and the relationship between neoantigen load and clinical outcomes was often inconsistent among cancer types. In this study, we described an informatics workflow, REAL-neo, for identification, quality control (QC), and prioritization of both class-I and class-II human leukocyte antigen (HLA) bound neoantigens that arise from somatic single nucleotide mutations (SNM), small insertions and deletions (INDEL), and gene fusions. We applied REAL-neo to 835 primary breast tumors in the Cancer Genome Atlas (TCGA) and performed comprehensive profiling and characterization of the detected neoantigens. We found recurrent HLA class-I and class-II restricted neoantigens across breast cancer cases, and uncovered associations between neoantigen load and clinical traits. Both class-I and class-II neoantigen loads from SNM and INDEL were found to predict overall survival independent of tumor mutational burden (TMB), breast cancer subtypes, tumor-infiltrating lymphocyte (TIL) levels, tumor stage, and age at diagnosis. Our study highlighted the importance of accurate and comprehensive neoantigen profiling and QC, and is the first to report the predictive value of neoantigen load for overall survival in breast cancer. ARTICLE HISTORY

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“…Several studies have been conducted to compare the accuracy of different software [21][22][23][24]. Bauer et al…”

Section: Introductionmentioning

confidence: 99%

Benchmarking the HLA typing performance of three HLA assays and seven NGS-based HLA algorithms

Liu

Zheng

Gong

et al. 2020

Preprint

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Abstract Background With the great progress made recently in NGS (Next Generation Sequencing) technology, sequencing accuracy and throughput have increased, while the cost for data has decreased. Various HLA (Human Leukocyte Antigen) typing algorithms and assays have been developed and have begun to be used in clinical practice. However, there is no systematic benchmarking to evaluate the HLA typing performance of different HLA assays and algorithms. In this study, we compared the HLA typing performance of three HLA assays and seven NGS-based HLA algorithms and assessed the impact of sequencing depth and length on HLA typing accuracy. Results Seven HLA typing algorithms at 4- and 6-digit allele levels were compared on three different assays in terms of accuracy, read depth and read length. The algorithms HLA-HD and HISAT-genotype showed the highest accuracy at both 2- and 4-digit resolution, followed by HLAscan. We designed a capture-based HLA assay, which showed comparable or even better performance compared with WES (Whole Exome Sequencing). In the depth evaluation, the sequencing data were down-sampled from 500X to 10X based on the depth of HLA genes. We found that the minimal depth was 100X for HLA-HD and HISAT-genotype to obtain more than 90% HLA typing accuracy at the 6-digit allele level. The accuracy of all three algorithms did not change when the read length decreased from 150 bp to 76 bp. Conclusion Although HISAT-genotype and HLA-HD may need more computing resources, we recommend using them for NGS-based HLA genotyping because of their higher accuracy and robustness to sequencing depth and read length. We propose that the minimal sequence depth for obtaining more than 90% HLA typing accuracy at the 6-digit allele level is 100X. Besides, targeting capture-based NGS HLA typing may be more suitable than WES in clinical practice due to its lower sequencing cost and higher HLA sequencing depth.

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Benchmarking the HLA typing performance of Polysolver and Optitype in 50 Danish parental trios

Cited by 30 publications

References 47 publications

Improved HLA typing of Class I and Class II alleles from next‐generation sequencing data

Improved HLA typing of Class I and Class II alleles from next‐generation sequencing data

HLA class-I and class-II restricted neoantigen loads predict overall survival in breast cancer

Benchmarking the HLA typing performance of three HLA assays and seven NGS-based HLA algorithms

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